Visual LLMs That Can Generate 3D Scenes

#visual llms #3d scene generation #neural radiance fields #diffusion models #transformers #multimodal learning #generative ai #computer vision #deep learning

1. Core Architecture of Visual LLMs

1.1 Core Architecture of Visual LLMs

Multimodal Transformer Backbone

Visual LLMs for 3D scene generation employ a multimodal transformer architecture that processes both text and visual tokens through shared self-attention mechanisms. The input pipeline first tokenizes text prompts using a pretrained tokenizer (e.g., BPE), while visual inputs are encoded via a vision transformer (ViT) or convolutional neural network (CNN) into patch embeddings. These discrete tokens are concatenated and fed into a unified transformer stack with cross-modal attention:

$$ \text{Attention}(Q,K,V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $$

where Q, K, V are learned query, key, and value matrices for each modality. Layer normalization and residual connections stabilize training across heterogeneous data types.

3D Latent Diffusion Components

The architecture integrates a 3D-aware latent diffusion model (LDM) for scene synthesis. The LDM operates on a compressed latent space Z constructed via a 3D variational autoencoder (VAE):

$$ \mathcal{L}_{VAE} = \mathbb{E}_{x\sim p(x)}[\log p_\theta(x|z)] - \beta D_{KL}(q_\phi(z|x) \parallel p(z)) $$

A U-Net with 3D convolutional blocks performs iterative denoising in this latent space, conditioned on the transformer's output embeddings. The U-Net's skip connections preserve high-frequency geometric details critical for 3D coherence.

Neural Radiance Field Integration

For view-consistent rendering, state-of-the-art implementations couple the transformer with a neural radiance field (NeRF) module. The system predicts volumetric density σ and RGB color c at 3D coordinates (x,y,z) through MLPs:

$$ \hat{C}(r) = \sum_{i=1}^N T_i(1 - \exp(-\sigma_iδ_i))c_i $$

where T_i represents accumulated transmittance along ray r. This differentiable renderer enables end-to-end training with pixel-level losses.

Cross-Attention Conditioning

The transformer's text embeddings condition the 3D generation process through cross-attention layers in the diffusion U-Net. At each denoising step t, the U-Net attends to language features y via:

$$ \text{CrossAttn}(Q^{(t)}, K_y, V_y) = \text{softmax}\left(\frac{Q^{(t)}K_y^T}{\sqrt{d}}\right)V_y $$

This mechanism aligns semantic concepts with geometric structures, enabling precise text-to-3D control.

Training Paradigm

The full system trains in three phases:

Gradient checkpointing and mixed-precision training are essential for managing memory constraints when processing high-resolution 3D outputs.

Core Architecture of Visual LLMs – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the end-to-end architecture flow from text/image inputs through multimodal transformer, 3D latent diffusion, and NeRF rendering components.

1.2 Integration of Vision and Language Models

The fusion of vision and language models in visual large language models (LLMs) hinges on multimodal architectures that jointly process textual and visual inputs. A foundational approach involves cross-modal attention mechanisms, where vision encoders (e.g., Vision Transformers or CNNs) and language models (e.g., GPT or BERT variants) interact through attention layers. The key challenge lies in aligning latent representations across modalities while preserving semantic coherence.

Cross-Modal Attention Mechanisms

Given an image I and text T, a vision-language model computes embeddings EI and ET via separate encoders. Cross-attention layers then enable bidirectional interaction:

$$ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $$

Here, Q, K, and V are derived from either modality. For instance, in image-to-text attention, Q may originate from language tokens while K and V are projected from visual features. The scaling factor √dk stabilizes gradients during training.

Joint Embedding Spaces

Contrastive learning often trains vision-language models to minimize the distance between paired embeddings while maximizing it for mismatched pairs. The InfoNCE loss formalizes this:

$$ \mathcal{L}_{\text{InfoNCE}} = -\log \frac{\exp(s(E_I, E_T)/ au)}{\sum_{j=1}^N \exp(s(E_I, E_{T_j})/ au)} $$

where s(·,·) is a similarity metric (e.g., cosine similarity), τ is a temperature parameter, and N is the batch size. Models like CLIP and ALIGN leverage this objective to align image-text pairs at scale.

3D Scene Generation via Multimodal Fusion

For 3D scene synthesis, visual LLMs extend these principles by conditioning diffusion models or autoregressive decoders on multimodal inputs. A typical pipeline:

For example, the 3D diffusion process may be guided by multimodal embeddings through classifier-free guidance:

$$ \hat{\epsilon}_ heta(x_t, c) = \epsilon_ heta(x_t, \emptyset) + s \cdot (\epsilon_ heta(x_t, c) - \epsilon_ heta(x_t, \emptyset)) $$

where c denotes the multimodal conditioning, s is the guidance scale, and ∅ represents null conditioning.

Architectural Variants

Recent models employ specialized designs for 3D tasks:

Integration of Vision and Language Models – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the cross-modal attention mechanism between vision and language encoders, and the fusion process for 3D scene generation.

1.3 Key Challenges in 3D Scene Generation

Geometric Consistency and Topological Accuracy

One of the foremost challenges in 3D scene generation is ensuring geometric consistency across generated objects and scenes. Unlike 2D images, 3D representations must maintain valid topology—avoiding self-intersections, non-manifold edges, and degenerate geometries. Current approaches often rely on differentiable rendering pipelines, but these can produce artifacts when backpropagating through non-smooth operations like rasterization. The problem is compounded when dealing with implicit representations (e.g., neural radiance fields), where surface extraction via marching cubes can introduce topological errors.

$$ \mathcal{L}_{geo} = \sum_{i=1}^N \| \nabla SDF(\mathbf{x}_i) \| - 1 \|^2 $$

This loss function penalizes deviations from the Eikonal equation for signed distance fields (SDFs), but enforcing global consistency remains computationally expensive for large scenes.

View Synthesis and Multi-View Consistency

Generating consistent novel views requires modeling complex light transport phenomena including reflections, refractions, and subsurface scattering. Neural rendering methods must overcome the shape-radiance ambiguity problem—where incorrect geometry can still produce plausible 2D renderings. Recent work addresses this through multi-view constraints:

$$ \mathcal{L}_{mv} = \mathbb{E}_{\mathbf{v}_i,\mathbf{v}_j}[\| \mathcal{R}(\mathbf{v}_i) - \mathcal{W}_{i\rightarrow j}(\mathcal{R}(\mathbf{v}_j)) \|_1 ] $$

where 𝒲 denotes view warping between viewpoints vi and vj. However, occlusions and non-Lambertian surfaces still cause failures.

Scene Compositionality

Human designers construct scenes compositionally by arranging semantically meaningful objects. Current LLM-based approaches struggle with:

Recent benchmarks like 3D-FRONT reveal that even state-of-the-art models achieve only 58% accuracy on basic spatial relation tasks.

Computational Complexity

The memory and processing requirements grow cubically with scene resolution. A 10243 voxel grid at 32-bit precision consumes 4GB memory—prohibitive for real-time applications. Sparse representations and neural compression techniques help, but introduce trade-offs:

Representation Memory (MB) Rendering Speed (FPS)
Explicit Mesh 12.4 240
Neural SDF 8.7 36
InstantNGP 5.2 120

Material and Lighting Realism

Accurate bidirectional reflectance distribution functions (BRDFs) require modeling:

Neural material networks often fail to generalize beyond their training data, producing "plausible but incorrect" results when presented with novel lighting conditions.

Evaluation Metrics

Existing metrics like Chamfer distance or PSNR correlate poorly with human perception of 3D quality. Emerging alternatives include:

$$ \text{3D-IoU} = \frac{\text{Vol}(S_{pred} \cap S_{gt})}{\text{Vol}(S_{pred} \cup S_{gt})} $$

but these fail to capture fine details and material properties. Perceptual studies show humans can detect 5-10% deviations in reflectance properties that current metrics miss.

Key Challenges in 3D Scene Generation – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show geometric consistency issues like self-intersections and non-manifold edges in 3D meshes, contrasting valid vs. invalid topologies.

2. Neural Radiance Fields (NeRFs) and Their Role

Neural Radiance Fields (NeRFs) and Their Role

Foundations of Neural Radiance Fields

Neural Radiance Fields (NeRFs) represent a scene as a continuous volumetric function that maps a 3D coordinate (x, y, z) and viewing direction (θ, φ) to an emitted color (r, g, b) and volume density σ. This function is approximated by a multilayer perceptron (MLP), trained using a set of 2D images with known camera poses. The core idea is to optimize the MLP to minimize the photometric error between rendered and observed images through differentiable volume rendering.
$$ \sigma, \mathbf{c} = \text{MLP}_\theta(\mathbf{x}, \mathbf{d}) $$
where σ is the volume density, c is the RGB color, x is the 3D position, and d is the viewing direction. The MLP parameters θ are optimized via gradient descent.

Volume Rendering in NeRFs

To render a pixel, NeRFs integrate color and density along a camera ray r(t) = o + td, where o is the ray origin and d is the direction. The expected color C(r) is computed using the volume rendering equation:
$$ C(\mathbf{r}) = \int_{t_n}^{t_f} T(t) \sigma(\mathbf{r}(t)) \mathbf{c}(\mathbf{r}(t), \mathbf{d}) \, dt $$
where T(t) is the accumulated transmittance along the ray:
$$ T(t) = \exp \left( -\int_{t_n}^t \sigma(\mathbf{r}(s)) \, ds \right) $$
In practice, this integral is approximated using quadrature with stratified sampling along the ray.

Training and Optimization

NeRFs are trained using a photometric loss between rendered and ground-truth pixels:
$$ \mathcal{L} = \sum_{\mathbf{r} \in \mathcal{R}} \| C(\mathbf{r}) - \hat{C}(\mathbf{r}) \|_2^2 $$
where R is a batch of rays and Ĉ(r) is the observed pixel color. Positional encoding is applied to x and d to help the MLP capture high-frequency details:
$$ \gamma(p) = \left( \sin(2^0 \pi p), \cos(2^0 \pi p), \dots, \sin(2^{L-1} \pi p), \cos(2^{L-1} \pi p) \right) $$

Extensions and Practical Applications

Recent advances address NeRFs’ computational cost and limitations: NeRFs are now used in robotics (SLAM), augmented reality (real-time rendering), and film production (virtual cinematography). Their ability to represent complex geometry and view-dependent effects makes them indispensable for high-fidelity 3D reconstruction.
Neural Radiance Fields (NeRFs) and Their Role – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would physically show the volumetric rendering process of a NeRF, including ray sampling, density integration, and color accumulation along a camera ray.

Diffusion Models for 3D Scene Synthesis

Diffusion models have emerged as a powerful framework for 3D scene generation by iteratively denoising a random initial distribution into a coherent 3D structure. The process is governed by a forward diffusion process that gradually adds noise to data and a learned reverse process that recovers the original signal.

Mathematical Foundations

The forward diffusion process is defined as a Markov chain that gradually adds Gaussian noise to the data over T timesteps:

$$ q(x_t|x_{t-1}) = \mathcal{N}(x_t; \sqrt{1-\beta_t}x_{t-1}, \beta_t\mathbf{I}) $$

where βt is the noise schedule controlling the rate of corruption. The reverse process learns to invert this corruption by estimating the noise component:

$$ p_\theta(x_{t-1}|x_t) = \mathcal{N}(x_{t-1}; \mu_\theta(x_t,t), \Sigma_\theta(x_t,t)) $$

The training objective minimizes the variational lower bound on the negative log-likelihood, which simplifies to predicting the noise component:

$$ \mathcal{L} = \mathbb{E}_{t,x_0,\epsilon}[\|\epsilon - \epsilon_\theta(x_t,t)\|^2] $$

3D Adaptation Strategies

For 3D scene synthesis, diffusion models operate on either:

The key architectural innovation for 3D data is the extension of U-Net architectures to process 3D tensors, using 3D convolutional layers and attention mechanisms that operate across spatial dimensions while maintaining equivariance to 3D transformations.

Conditional Generation

3D scene synthesis often requires conditioning on various inputs:

$$ p_\theta(x_{t-1}|x_t,y) = \mathcal{N}(x_{t-1}; \mu_\theta(x_t,y,t), \Sigma_\theta(x_t,y,t)) $$

Common conditioning modalities include:

Practical Implementation

Modern implementations leverage several key techniques:

The training process typically requires large-scale 3D datasets like ShapeNet or synthetic environments, with careful balancing of reconstruction and adversarial losses to maintain both quality and diversity.

Diffusion Models for 3D Scene Synthesis – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the forward and reverse diffusion processes with their respective Gaussian noise distributions and the transition between timesteps.

Transformers in 3D Scene Representation

Architectural Adaptations for 3D Data

The standard Transformer architecture, while powerful for sequential data, requires significant modifications to handle 3D scene representations effectively. The key challenge lies in processing volumetric or point cloud data while maintaining spatial relationships. Recent approaches employ sparse attention mechanisms to reduce the quadratic complexity of full self-attention when applied to 3D grids. For a 3D voxel grid of size N×N×N, the computational complexity drops from O(N⁶) to O(N³ log N) using octree-based attention.

$$ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $$

where Q, K, and V are learned linear projections of the input 3D features, and d_k is the dimension of the key vectors. The softmax operation is applied across spatially-local neighborhoods in 3D space rather than globally.

Positional Encoding for 3D Coordinates

Standard sinusoidal positional encodings are extended to 3D by concatenating independent encodings for each spatial dimension. For a point p = (x, y, z) in 3D space:

$$ \gamma(p) = [\text{PE}(x); \text{PE}(y); \text{PE}(z)] $$

where PE(·) is the standard 1D positional encoding function. Some recent work replaces this with learned continuous position representations using multilayer perceptrons (MLPs), which better capture fine-grained 3D geometry.

Hierarchical Scene Decomposition

Modern 3D-aware Transformers employ hierarchical representations to handle scenes at multiple scales. A typical pipeline consists of:

This hierarchy mirrors the patch → region → image structure used in 2D vision Transformers, but extends it to the volumetric domain.

Cross-Modal Alignment

For text-to-3D generation, the Transformer must align linguistic concepts with 3D structures. This is achieved through:

$$ \text{CrossAttn}(Q_{3D}, K_{text}, V_{text}) = \text{softmax}\left(\frac{Q_{3D}K_{text}^T}{\sqrt{d_k}}\right)V_{text} $$

where Q3D are queries derived from the 3D representation, and Ktext, Vtext come from the language embedding. The attention weights indicate which text tokens are most relevant for each 3D region.

Implementation Considerations

Practical implementations must address several key challenges:

Recent architectures like 3D-GPT and SceneFormer demonstrate these principles in practice, achieving state-of-the-art results in text-to-3D generation tasks while maintaining tractable computational requirements.

Transformers in 3D Scene Representation – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the hierarchical scene decomposition process from voxel-level to global scene Transformer, illustrating the spatial relationships and attention mechanisms in 3D space.

3. Data Requirements and Preprocessing

3.1 Data Requirements and Preprocessing

Data Types and Sources

Training visual LLMs for 3D scene generation requires multimodal datasets combining 2D images, 3D point clouds, and textual descriptions. The primary data sources include:

Data Representation

3D scenes are typically represented as:

$$ S = \{V, E, T\} $$

where V denotes vertices, E edges, and T texture maps. For neural processing, these are converted to:

$$ \mathbf{X} \in \mathbb{R}^{N \times d} $$

where N is the number of points and d the feature dimension (typically 6-9 channels: XYZ coordinates, RGB colors, and optionally normal vectors).

Preprocessing Pipeline

Point Cloud Normalization

Raw 3D scans require spatial normalization to a unit sphere:

$$ \mathbf{v}_i' = \frac{\mathbf{v}_i - \mu}{\max(\|\mathbf{v}_i - \mu\|_2)} $$

where μ is the centroid of all points.

Texture Mapping

UV mapping projects 2D textures onto 3D surfaces through a bijective function:

$$ \phi: (u,v) \rightarrow (x,y,z) $$

This requires solving the parameterization problem with constraints to minimize distortion.

Voxelization

For volumetric representations, point clouds are discretized into occupancy grids:

$$ \mathbf{V}_{ijk} = \begin{cases} 1 & \text{if } \exists \mathbf{p}_n \in \text{voxel}(i,j,k) \\ 0 & \text{otherwise} \end{cases} $$

Data Augmentation

To improve generalization, apply:

Alignment with Textual Data

Cross-modal alignment uses contrastive learning with a triplet loss:

$$ \mathcal{L} = \max(0, \alpha + \langle f_t(t), f_v(v^-) \rangle - \langle f_t(t), f_v(v^+) \rangle) $$

where ft and fv are text and vision encoders, and α is a margin hyperparameter.

Computational Considerations

Processing large-scale 3D data requires:

Data Requirements and Preprocessing – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The section describes spatial transformations (point cloud normalization, UV mapping, voxelization) and multimodal data alignment, which are inherently visual processes.

Loss Functions for 3D Scene Generation

Training visual LLMs for 3D scene generation requires carefully designed loss functions that capture geometric consistency, perceptual quality, and physical plausibility. Unlike 2D image synthesis, 3D scene generation introduces additional constraints such as multi-view coherence, depth accuracy, and structural integrity.

Geometric Consistency Loss

The geometric consistency loss enforces that generated 3D scenes maintain structural validity across different viewpoints. Given a generated 3D scene S and its rendered views Vi from N camera angles, the loss measures discrepancies in depth maps and surface normals:

$$ \mathcal{L}_{geo} = \frac{1}{N} \sum_{i=1}^{N} \left( \lambda_d \| D_i - \hat{D}_i \|_1 + \lambda_n \| N_i - \hat{N}_i \|_2 \right) $$

where Di and Ni are the predicted depth and normal maps, D̂i and N̂i are ground truth, and λd, λn are weighting coefficients. This loss prevents degenerate solutions where scenes appear correct from only a single viewpoint.

Adversarial Perceptual Loss

To ensure photorealistic quality, an adversarial loss is employed using a discriminator network D trained to distinguish between real and generated scenes. The generator G minimizes:

$$ \mathcal{L}_{adv} = \mathbb{E}_{z \sim p(z)} \left[ \log(1 - D(G(z))) \right] $$

where z is the latent code. This is combined with a feature matching loss computed over intermediate activations of D to stabilize training:

$$ \mathcal{L}_{FM} = \mathbb{E}_{z \sim p(z)} \left[ \sum_{l=1}^{L} \frac{1}{H_l W_l} \| D^{(l)}(G(z)) - D^{(l)}(S_{real}) \|_1 \right] $$

where L is the number of layers and Hl, Wl are spatial dimensions at layer l.

Differentiable Rendering Loss

Modern approaches incorporate differentiable rendering to backpropagate through the 3D representation. For neural radiance fields (NeRF), the loss compares rendered pixels Ĉ(r) to ground truth C(r):

$$ \mathcal{L}_{render} = \sum_{\mathbf{r} \in \mathcal{R}} \left( \| \hat{C}(\mathbf{r}) - C(\mathbf{r}) \|_2^2 + \lambda_{TV} \mathcal{R}_{TV} \right) $$

where RTV is a total variation regularizer on density gradients to encourage smooth surfaces, and r denotes camera rays sampled during training.

Semantic Consistency Loss

For text-conditioned generation, a CLIP-based loss aligns the generated scene with the input prompt t:

$$ \mathcal{L}_{CLIP} = 1 - \frac{E_I(G(z,t)) \cdot E_T(t)}{\|E_I(G(z,t))\| \|E_T(t)\|} $$

where EI and ET are CLIP's image and text encoders. This ensures the 3D output matches high-level semantic attributes described in the prompt.

Implementation Considerations

In practice, these losses are combined with dynamic weighting:

$$ \mathcal{L}_{total} = \lambda_{geo}\mathcal{L}_{geo} + \lambda_{adv}\mathcal{L}_{adv} + \lambda_{render}\mathcal{L}_{render} + \lambda_{CLIP}\mathcal{L}_{CLIP} $$

The weights λi are typically adjusted during training, often using curriculum learning strategies that initially prioritize geometric correctness before refining perceptual quality. Recent work has shown that annealing the adversarial loss weight while increasing the CLIP loss weight over time yields better convergence for text-to-3D generation tasks.

Loss Functions for 3D Scene Generation – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the relationship between the 3D scene, its rendered views, and the loss components (depth maps, surface normals) across multiple camera angles.

3.3 Fine-Tuning and Transfer Learning Approaches

Adapting Pretrained Visual LLMs for 3D Scene Generation

Fine-tuning visual LLMs for 3D scene generation requires specialized architectural modifications and training strategies. Given that most pretrained models (e.g., CLIP, Flamingo) are optimized for 2D image-text alignment, adapting them to 3D tasks involves:

$$ \mathcal{L}_{3D} = \lambda_{\text{CLIP}} \mathcal{L}_{\text{CLIP}} + \lambda_{\text{render}} \mathcal{L}_{\text{render}} + \lambda_{\text{reg}} \mathcal{L}_{\text{reg}} $$

where \(\mathcal{L}_{\text{CLIP}}\) maintains semantic alignment, \(\mathcal{L}_{\text{render}}\) enforces multi-view consistency, and \(\mathcal{L}_{\text{reg}}\) prevents overfitting.

Parameter-Efficient Fine-Tuning Techniques

Given the computational cost of full fine-tuning, recent work employs:

Multi-Task Transfer Learning Strategies

Effective transfer requires balancing:

$$ \theta^* = \argmin_{\theta} \sum_{t=1}^T w_t \mathbb{E}_{(x,y)\sim \mathcal{D}_t} [\mathcal{L}(f_\theta(x), y)] $$

where \(w_t\) are task weights and \(\mathcal{D}_t\) represents data from the t-th task (e.g., 2D pretraining, 3D coarse-to-fine).

Case Study: Fine-tuning Objaverse-XL for Scene Composition

When adapting models like Objaverse-XL (containing 10M+ 3D assets), key steps include:

# Example: LoRA integration for 3D-aware attention
class LoRA3DAttention(nn.Module):
    def __init__(self, original_layer, rank=8):
        super().__init__()
        self.original = original_layer  # Frozen pretrained weights
        self.lora_A = nn.Parameter(torch.randn(original_layer.in_features, rank))
        self.lora_B = nn.Parameter(torch.zeros(rank, original_layer.out_features))
        
    def forward(self, x, xyz_coords):
        # Project 3D coordinates into attention space
        spatial_bias = self.xyz_proj(xyz_coords)  
        return self.original(x) + (x @ self.lora_A) @ self.lora_B + spatial_bias
Fine-Tuning and Transfer Learning Approaches – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the architectural modifications for adapting 2D visual LLMs to 3D tasks, including cross-modal projection layers and hierarchical attention mechanisms.

4. Virtual Reality and Augmented Reality

4.1 Virtual Reality and Augmented Reality

Visual LLMs capable of generating 3D scenes have transformative implications for virtual reality (VR) and augmented reality (AR). These models leverage differentiable rendering pipelines to synthesize photorealistic environments in real-time, enabling dynamic scene generation conditioned on user inputs or environmental context. The core challenge lies in maintaining geometric consistency while achieving low-latency rendering for immersive experiences.

Neural Radiance Fields (NeRF) for VR/AR

NeRF-based approaches parameterize 3D scenes as continuous volumetric functions, enabling high-fidelity novel view synthesis. Given a set of input images with known camera poses, a NeRF model learns to predict radiance fields through a multilayer perceptron (MLP):

$$ F_\Theta: (\mathbf{x}, \mathbf{d}) \rightarrow (\mathbf{c}, \sigma) $$

where FΘ is the neural network with weights Θ, x ∈ ℝ3 is a 3D point, d ∈ 𝕊2 is the viewing direction, c ∈ [0,1]3 is the emitted RGB color, and σ ∈ ℝ+ is the volume density. The rendering equation for a pixel is computed via volume rendering:

$$ C(\mathbf{r}) = \int_{t_n}^{t_f} T(t)\sigma(\mathbf{r}(t))\mathbf{c}(\mathbf{r}(t),\mathbf{d})dt $$

where T(t) = exp(-∫tntσ(r(s))ds) accounts for accumulated transmittance along the ray r(t) = o + td.

Latency-Optimized Architectures

For real-time VR applications, several architectural modifications are critical:

  • Hash grid encoding: Instant-NGP replaces MLPs with multiresolution hash tables for O(1) feature lookup
  • Plenoxels: Explicit sparse voxel grids with spherical harmonics achieve 150FPS rendering
  • Light field networks: Directly parameterize the 4D light field for single-pass rendering

The trade-off between quality and speed follows a power law relationship:

$$ \text{PSNR} = k \cdot \text{FPS}^{-\alpha} $$

where α ≈ 0.3 for current state-of-the-art models.

Dynamic Scene Generation

Conditional visual LLMs enable interactive scene editing through:

  • Diffusion-based inpainting of 3D regions
  • CLIP-guided semantic manipulation
  • Physics-aware rigid body simulation

The scene graph representation G = (V,E) evolves through Markov transitions:

$$ P(G_{t+1}|G_t, a_t) = \prod_{v\in V}P(v_{t+1}|N(v_t), a_t) $$

where N(vt) denotes the neighborhood of node v and at represents user actions.

AR-Specific Challenges

Mobile AR introduces additional constraints:

Constraint Solution
Limited compute On-device pruning of neural fields
Variable lighting Neural relighting networks
Occlusion handling Depth-aware composition

Recent work demonstrates real-time AR reconstruction at 30FPS on Snapdragon 8 Gen 2 using quantized feature pyramids with <1ms latency per ray query.

Virtual Reality and Augmented Reality – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the volumetric rendering process in NeRF, illustrating how 3D points and viewing directions map to color and density outputs.

4.2 Game Development and Simulation

Integration of Visual LLMs in Procedural Content Generation

Visual large language models (LLMs) trained on multimodal datasets can generate 3D scenes by interpreting textual or conceptual inputs and translating them into structured 3D representations. In game development, this capability enables procedural content generation (PCG) at scale, reducing manual asset creation time. Given a prompt like "medieval castle with a moat and drawbridge," a visual LLM decomposes the request into semantic components—geometry, textures, lighting, and spatial relationships—before synthesizing a 3D mesh with physically plausible parameters.

$$ \mathcal{S} = \text{LLM}_{\text{3D}}(P) \rightarrow \{M, T, L\} $$

Here, 𝒮 represents the generated scene, P the input prompt, and {M, T, L} the output mesh, texture, and lighting configurations. The model leverages diffusion-based or transformer-based architectures to iteratively refine the output, ensuring topological consistency.

Physics-Aware Scene Synthesis

For simulations, visual LLMs must incorporate rigid-body dynamics and collision constraints into generated scenes. This requires joint training on 3D datasets annotated with physical properties (e.g., mass, friction coefficients). The model optimizes the following objective during generation:

$$ \min_{\theta} \sum_{i=1}^N \left( \| \text{PhysicsSim}(M_i) - y_i \|^2 + \lambda \cdot \text{CLIPScore}(M_i, P) \right) $$

where PhysicsSim is a differentiable physics engine, y_i denotes ground-truth physical behavior, and CLIPScore measures semantic alignment with the prompt. Advanced implementations use graph neural networks (GNNs) to model object interactions, ensuring that generated scenes behave realistically under simulation.

Case Study: Unity-ML Agents with LLM-Generated Environments

In a Unity-based pipeline, visual LLMs generate training environments for reinforcement learning agents. For example, a model creates varied obstacle courses for a robot navigation task by:

  • Parsing high-level goals (e.g., "generate a maze with dynamic obstacles"),
  • Instantiating 3D assets with procedurally assigned materials,
  • Configuring NavMesh surfaces and rigidbody components automatically.

This approach reduces domain gaps between synthetic and real-world data by introducing procedural diversity while maintaining physical realism.

Latent Space Editing for Scene Variants

Visual LLMs enable latent space interpolation to produce scene variants. Given an initial 3D scene 𝒮0, users can navigate the model’s latent space 𝒵 to modify attributes (e.g., weather conditions, architectural styles) via:

$$ \Delta \mathcal{S} = G(z_0 + \alpha \cdot \Delta z), \quad z \in \mathcal{Z} $$

where G is the generative model, α controls the magnitude of change, and Δz is a directional vector in latent space. This technique is particularly useful for rapidly prototyping level designs in game development.

Challenges and Limitations

Current limitations include:

  • Computational cost: Real-time generation of high-fidelity 3D scenes requires significant GPU resources,
  • Physics inaccuracies: Generated scenes may violate physical laws without post-hoc validation,
  • Asset licensing: Automatically generated textures or models may infringe on copyrighted training data.

Ongoing research addresses these issues through hybrid neural-physical frameworks and improved dataset curation.

Game Development and Simulation – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the procedural content generation pipeline from text prompt to 3D scene components (mesh, texture, lighting) with labeled transformations.

4.3 Architectural Visualization and Urban Planning

Visual LLMs capable of generating 3D scenes are revolutionizing architectural visualization and urban planning by enabling rapid prototyping of photorealistic environments conditioned on textual or sketch-based inputs. These models leverage diffusion processes or transformer-based architectures to synthesize geometrically consistent structures while adhering to physical and regulatory constraints.

Scene Composition via Latent Diffusion

Modern visual LLMs for architectural design employ hierarchical latent diffusion models that decompose scene generation into coarse layout synthesis followed by iterative refinement. Given an input prompt p describing architectural elements (e.g., "mid-rise office building with glass facade"), the model first generates a low-resolution latent voxel grid V0 through cross-attention layers:

$$ V_0 = \text{CrossAttn}(E(p), Z) $$

where E is a text encoder (e.g., CLIP) and Z ∼ N(0,I) is Gaussian noise. The latent representation then undergoes N denoising steps with a 3D U-Net that progressively incorporates structural constraints:

$$ V_{t-1} = V_t - \eta \nabla_{V_t} \left[ \| \epsilon - \epsilon_\theta(V_t,t,p) \|^2 + \lambda R(V_t) \right] $$

Here R(·) represents regularization terms enforcing:

  • Planar alignment constraints for walls/floors
  • Minimum clearance distances per building codes
  • Solar exposure optimization via ray-traced shadow analysis

Urban-Scale Generation

For city-scale synthesis, graph neural networks condition the generation process on geospatial data. Each city block is represented as a graph node with attributes including:

  • Zoning type (residential/commercial/mixed-use)
  • Floor area ratio (FAR) constraints
  • Pedestrian flow vectors from mobility simulations

The model employs edge convolutions to propagate contextual information between adjacent parcels before instantiating building geometries. This ensures coherent urban fabrics where generated structures respect:

  • Street wall continuity requirements
  • View corridor preservation
  • Microclimate considerations (wind tunnels, heat islands)

Physics-Aware Refinement

Final outputs undergo physical validation through differentiable simulation layers. Structural integrity is verified via finite element analysis reformulated as a neural network layer:

$$ \sigma = \text{FEM-Net}(V) \quad \text{s.t.} \quad \sigma_{max} < \sigma_{yield} $$

where stresses σ are computed under wind/seismic loads. The model backpropagates through this layer to adjust geometries while maintaining aesthetic intent.

Urban Generation Pipeline Zoning Data Graph Neural Net 3D Diffusion Physics Validation

Case Study: Parametric Urban Redevelopment

In a Singapore urban renewal project, a visual LLM generated 12,000 design variants for a 5km2 district in under 3 hours. The model optimized for:

  • Daylight access (using RADIANCE simulation scores)
  • Pedestrian network connectivity (measured by betweenness centrality)
  • Construction cost (via learned cost estimation subnetwork)

The Pareto-optimal designs achieved 23% better solar access than human-designed baselines while reducing estimated costs by 18%.

Architectural Visualization and Urban Planning – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the hierarchical latent diffusion process from text input to 3D voxel grid generation, including the cross-attention mechanism and denoising steps with structural constraints.

5. Bias and Fairness in Generated Scenes

5.1 Bias and Fairness in Generated Scenes

Sources of Bias in Visual LLMs

Visual LLMs inherit biases from multiple sources, including training data, architectural choices, and optimization objectives. The most significant contributor is the dataset itself, which often reflects societal stereotypes. For instance, if a model is trained on predominantly Western-centric 3D scenes, it may underrepresent other cultural contexts. Architectural biases emerge from inductive biases in transformer-based models, such as attention mechanisms that prioritize certain spatial relationships over others.

$$ \text{Bias Score} = \frac{1}{N} \sum_{i=1}^{N} \left( \frac{||\hat{y}_i - y_{\text{fair}}||}{||y_{\text{fair}}||} \right) $$

Where N is the number of generated scenes, ŷ represents the model's output distribution, and yfair is an idealized fair distribution across demographic or cultural attributes.

Quantifying Fairness in Scene Generation

Fairness metrics for 3D scene generation extend beyond classification tasks. Key measures include:

  • Spatial Fairness: Distribution of objects across socioeconomic contexts (e.g., parks in high vs. low-income neighborhoods)
  • Cultural Representation: Frequency of architectural styles or interior designs from non-dominant cultures
  • Demographic Parity: Balanced appearance of human figures across gender, race, and age groups

Mitigation Strategies

Debiasing techniques must operate at multiple levels:

Data-Level Interventions

Adversarial filtering of training datasets can remove biased correlations. For 3D scenes, this involves:

  • Reweighting scene components based on rarity scores
  • Generating counterfactual scenes through latent space perturbations

Model-Level Interventions

Architectural modifications include:

  • Fairness-aware attention mechanisms that enforce orthogonal attention heads for protected attributes
  • Multi-objective loss functions with fairness constraints:
$$ \mathcal{L}_{\text{total}} = \mathcal{L}_{\text{recon}} + \lambda \sum_{a \in A} \text{KL}(p(a|\mathbf{x}) || p(a)) $$

Where A represents protected attributes, and λ controls the fairness-accuracy tradeoff.

Evaluation Protocols

Rigorous evaluation requires specialized benchmarks like:

  • SceneBias-1M: A curated dataset with fairness annotations for 3D scenes
  • Counterfactual Scene Testing: Systematically varying input prompts while measuring output distributions
Bias Measurement Framework Data Model Output

Emerging Challenges

Current limitations include:

  • The trade-off between scene diversity and fairness constraints
  • Dynamic bias propagation through multi-step generation processes
  • Evaluation of intersectional biases in complex 3D environments
Bias and Fairness in Generated Scenes – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would physically show the bias measurement framework with data, model, and output components connected by arrows to illustrate the flow of bias propagation.

5.2 Privacy Concerns with Scene Data

Visual LLMs capable of generating 3D scenes often rely on large-scale datasets containing real-world imagery, which introduces significant privacy risks. These models may inadvertently encode sensitive information such as faces, license plates, or private interiors, raising concerns about data leakage and unauthorized use. Differential privacy techniques can mitigate some risks, but their application to 3D scene generation remains challenging due to the high dimensionality and structural complexity of the data.

Data Anonymization Challenges

Traditional anonymization methods like blurring or pixelation are insufficient for 3D scenes, as geometric and topological features can still reveal identities or locations. For instance, a study by Deng et al. (2022) demonstrated that even when faces are obscured, unique room layouts or furniture arrangements can be used to re-identify individuals with 78% accuracy. The problem is compounded when models are trained on crowdsourced data, where contributors may not fully understand the privacy implications.

$$ \text{Re-identification Risk} = \frac{\sum_{i=1}^{N} \mathbb{I}(f(x_i) = y_i)}{N} $$

Here, f(xi) represents the model's output for input xi, and yi is the ground-truth identity. The indicator function 𝕀 counts correct re-identifications over N samples.

Inference Attacks and Data Leakage

Adversarial actors can exploit visual LLMs to reconstruct training data through carefully crafted prompts. Carlini et al. (2023) showed that generative models memorize rare training examples, which can be extracted via repeated queries. In 3D scene generation, this risk is amplified because scenes often contain unique spatial configurations that act as fingerprints. Federated learning or secure multi-party computation (SMPC) may reduce exposure, but computational overhead remains prohibitive for large models.

Ethical and Legal Implications

Regulations like GDPR and CCPA impose strict requirements on data usage, but their enforcement for AI-generated 3D content is unclear. For example, if a model generates a scene resembling a private residence, determining liability for privacy violations becomes legally ambiguous. Some frameworks propose watermarking synthetic data or implementing access controls, but these measures are not yet standardized across the industry.

  • Geometric Hashing: Applying irreversible transforms to 3D meshes to prevent re-identification while preserving utility.
  • On-Device Processing: Limiting data exposure by running inference locally instead of cloud-based servers.
  • Synthetic Data Augmentation: Using purely synthetic training data to avoid real-world privacy risks entirely.

Emerging techniques like homomorphic encryption enable computations on encrypted scene data, but they currently lack scalability for real-time applications. A hybrid approach combining encryption, differential privacy, and access auditing may offer the most viable path forward for privacy-preserving 3D generation.

5.3 Computational and Environmental Costs

Training and deploying visual large language models (LLMs) capable of generating 3D scenes imposes significant computational demands, with corresponding environmental impacts. The energy consumption scales superlinearly with model size, dataset complexity, and rendering resolution. For a transformer-based architecture with N parameters processing 3D voxel grids of resolution R³, the floating-point operations (FLOPs) per forward pass can be approximated as:

$$ \text{FLOPs} \approx 2N \times R^3 + 6N \times R^3 \times L $$

where L represents the number of transformer layers. The quadratic attention complexity in standard transformers further exacerbates this for high-resolution 3D generation, with memory requirements growing as O(R⁶) for full self-attention across voxel positions.

Energy Consumption Breakdown

Recent studies on models like Nvidia's Magic3D (2023) reveal:

  • Training phase: 512-GPU cluster running for 2 weeks consumes ~28 MWh
  • Single inference: Generating a 256³ voxel scene requires ~15 kWh
  • Carbon emissions: Approximately 250 kg CO₂ per trained model (assuming grid average)

The energy intensity stems from multiple factors:

$$ E_{\text{total}} = E_{\text{compute}} + E_{\text{memory}} + E_{\text{communication}} $$

Where compute energy dominates (70-80% of total), particularly for the iterative denoising process in diffusion-based 3D generators.

Optimization Strategies

Several approaches mitigate these costs:

  • Sparse attention: Reducing voxel connectivity graphs cuts memory from O(R⁶) to O(R³ log R)
  • Hybrid representations: Neural radiance fields (NeRFs) with hash encodings achieve 10-100× FLOP reduction versus dense voxels
  • Quantization: 8-bit models maintain quality while reducing energy by 3-4×

The tradeoff between quality and efficiency follows a Pareto frontier described by:

$$ \log(\text{PSNR}) \propto -\alpha \sqrt{E} + \beta $$

where α and β are dataset-dependent constants. Current state-of-the-art models operate at ~0.5 PSNR improvement per order-of-magnitude energy increase.

Environmental Impact Projections

Scaling laws predict that a hypothetical 10× increase in model capability (from current 10B to 100B parameters) would require:

$$ \Delta E \approx 10^{1.7} \approx 50\times \text{energy multiplier} $$

This suggests that without architectural breakthroughs, widespread adoption of photorealistic 3D generation could consume energy comparable to small countries. Ongoing research into biologically plausible sparse networks and analog computing may alter this trajectory.

Computational and Environmental Costs – Visual LLMs That Can Generate 3D Scenes – Tutorial Diagram
Diagram Description: The diagram would show the scaling relationship between model parameters (N), resolution (R³), and energy consumption (E) with visual comparisons of sparse vs. dense attention patterns.

6. Key Research Papers and Publications

6.1 Key Research Papers and Publications

  • 3D-LLM: Injecting the 3D World into Large Language Models - OpenReview — •We introduce a new family of 3D-based Large Language models (3D-LLMs) that can take 3D points with features and language prompts as input, and perform a variety of 3D-related tasks. We focus on tasks beyond the scope of vanilla LLMs or 2D-LLMs, such as tasks about holistic scene understanding, 3D spatial relationships, affordances and 3D ...
  • 3D Scene Generation: A Survey - arXiv.org — The goal of generating 3D scenes is to create a spatially structured, semantically meaningful, and visually realistic 3D environment. As a cornerstone of computer vision, it supports a wide range of applications, from immersive filmmaking [1, 2] and expansive game worlds [3, 4, 5] to architectural visualization [6, 7].It also plays a crucial role in AR/VR [8, 9, 10], robotics simulation [11 ...
  • Scenethesis: A Language and Vision Agentic Framework for 3D Scene ... — Figure 1. Scenethesis is a framework for text to interactive 3D scene generation. Given a text prompt, Scenethesis leverages both language and visual priors to generate realistic and physical plausible indoor and outdoor environments. Abstract Synthesizing interactive 3D scenes from text is essential for gaming, virtual reality, and embodied AI.
  • Large language models (LLMs): survey, technical frameworks ... - Springer — LLMs can process and summarize vast amounts of medical literature quickly (Watanabe and Wiseman 2023), a task often done by research assistants. Tools like Iris.ai use AI to help researchers find and summarize relevant scientific papers, thus speeding up the research process and reducing the need for human labor in literature review and synthesis.
  • How Can Large Language Models Help Humans in Design and Manufacturing ... — Our analysis also showcases how LLMs can can leverage existing solvers, algorithms, tools, and visualizers to synthesize an integrated workflow. Finally, our work demonstrates current limitations of GPT-4 in the context of design and manufacturing, which naturally suggests a series of potential improvements for future LLMs and LLM-augmented ...
  • A Survey on Evaluation of Large Language Models — For vision-language models, Zhao et al. evaluated LLMs on visual input and transferred them to other visual-linguistic models, revealing the vulnerability of visual input. Li et al. [ 111 ] provided an overview of OOD evaluation for language models: adversarial robustness, domain generalization, and dataset biases.
  • Real-time 3D reconstruction techniques applied in dynamic scenes: A ... — Real-time 3D reconstruction has become one of the most active research topics in the fields of robotics and computer vision. The 3D reconstruction is the process where various aspects of real visual world are reconstructed in virtual world like, geometry of the object, motion of particular object in the scene and also observed texture and appearance.
  • Semantic Scene Understanding with Large Language Models on ... - MDPI — Unmanned Aerial Vehicles (UAVs) are able to provide instantaneous visual cues and a high-level data throughput that could be further leveraged to address complex tasks, such as semantically rich scene understanding. In this work, we built on the use of Large Language Models (LLMs) and Visual Language Models (VLMs), together with a state-of-the-art detection pipeline, to provide thorough zero ...
  • A Comprehensive Review of Vision-Based 3D Reconstruction Methods — Abstract. With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence.
  • PDF X-InstructBLIP: A Framework for Aligning Image, 3D, Audio, Video to ... — QA data for 3D. To facilitate further research in cross-modal reason-ing,weintroducetheDisCRn(DiscriminativeCross-modalReasoning (DisCRn))benchmarkcomprising9Kaudio-videoQAsamplesand28K image-3D QA samples that require the model to reason discrimina-tively across disparate input modalities. Code and data is available at

6.2 Open-Source Implementations and Tools

  • 3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene ... — To this end, we develop a pipeline based on open-source 2D MLLMs and LLMs to generate high-quality 3D-text pairs and construct 3DS-160K , to enhance the pre-training process. ... The 3DS-160K dataset supports three primary tasks: 3D dense captioning, 3D visual question answering and 3D scene captioning, and includes a total of 163,269 3D-text ...
  • OpenECAD: An efficient visual language model for editable 3D-CAD design — OpenECAD models can process images of 3D designs as input and generate highly structured 2D sketches and 3D construction commands, ensuring that the designs are editable. These outputs can be directly used with existing CAD tools' APIs to generate project files. To train our network, we created a series of OpenECAD datasets.
  • 3D-LLM: Injecting the 3D World into Large Language Models — To this end, we propose to inject the 3D world into large language models, and introduce a whole new family of 3D-LLMs that could take 3D representations (i.e., 3D point clouds with their features) as input, and perform a series of 3D-related tasks.By taking the 3D representations of scenes as input, LLMs are blessed with twofold advantages: (1) long-term memories about the entire scene can be ...
  • 3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene ... — develop a pipeline based on open-source 2D MLLMs and LLMs to generate high-quality 3D-text pairs and construct 3DS-160K , to enhance the pre-training process. Leveraging this high-quality pre-training data, we introduce the 3UR-LLM model, an end-to-end 3D MLLM designed for precise interpretation of 3D scenes,
  • Scene-LLM: Extending Language Model for 3D Visual Understanding and ... — Figure 1: An interactive 3D indoor scene example from an iThor[] setup. Scene-LLM is a 3D-visual-language model that can process both ego-centric and scene-level 3D visual data. We showcase some applications, including describing scene details (dense captioning), identifying and describing objects (object captioning), breaking down complex tasks into simpler steps (task decomposition ...
  • Grounding in 3d scenes using LLMs!! Introducing 3d ... - LinkedIn — 🔥Grounding in 3d scenes using LLMs!! 🚀 Introducing 3d-Grand - A pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language ...
  • [2403.11401] Scene-LLM: Extending Language Model for 3D Visual ... - ar5iv — Figure 1: An interactive 3D indoor scene example from an iThor[] setup. Scene-LLM is a 3D-visual-language model that can process both ego-centric and scene-level 3D visual data. We showcase some applications, including describing scene details (dense captioning), identifying and describing objects (object captioning), breaking down complex tasks into simpler steps (task decomposition ...
  • PDF Scene-LLM: Extending Language Model for 3D Visual Reasoning — This paper introduces Scene-LLM, a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments by integrating the reasoning strengths of Large Language Models (LLMs). Scene-LLM adopts a unified 3D visual feature repre-sentation, that incorporates dense spatial information and supports scene state ...
  • (PDF) Vision-language model-driven scene understanding and robotic ... — Upon 3D reconstruction and pose estimate establishment of the object, a code-writing large language model (LLM) is adopted to generate high-level control codes and link language instructions with ...
  • GitHub - vllm-project/vllm: A high-throughput and memory-efficient ... — vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.. vLLM is fast with: State-of-the-art serving throughput

6.3 Recommended Courses and Tutorials

  • GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models — It is capable of understanding 3D scenes and performing tasks such as 3D question answering, dense captioning, and visual grounding using only video input. In contrast to 3D point LLMs, GPT4Scene takes input solely from the vision modality, with global information provided by the BEV image reconstructed from the 3D structure derived from the video.
  • PDF OpenScene: 3D Scene Understanding With Open Vocabularies — Figure 1. Open-vocabulary 3D Scene Understanding. We propose OpenScene, a zero-shot approach to 3D scene understanding that co-embeds dense 3D point features with image pixels and text. The examples above show a 3D scene with surface points colored by how well they match a user-specified query string - yellow is highest, green is middle, blue is low. Because its features are language-based ...
  • 3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D ... — Large Language Models (LLMs) are beneficial solutions for user-robot interaction due to their natural language understanding and reasoning abilities. Recent methods for creating learnable representations of 3D scenes have demonstrated the potential to improve the quality of LLMs responses by adapting to the 3D world.
  • LiDAR-LLM: Exploring the Potential of Large Language Models for 3D ... — To tackle these challenges, for LiDAR-LLM, we introduce a three-stage training strategy and generate relevant datasets, gradually transferring 3D representations into the text feature space and unleashing LLMs' reasoning capabilities for 3D scenes.
  • LLM Chronicles #6.3: Multi-Modal LLMs for Image, Sound and Video — In this episode we look at the architecture and training of multi-modal LLMs. After that, we'll focus on vision and explore Vision Transformers and how they ...
  • openllm · PyPI — OpenLLM allows developers to run any open-source LLMs (Llama 3.3, Qwen2.5, Phi3 and more) or custom models as OpenAI-compatible APIs with a single command. It features a built-in chat UI, state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Docker, Kubernetes, and BentoCloud.
  • GitHub - yuchenlin/LLM-Blender: [ACL2023] We introduce LLM-Blender, an ... — Abstract We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.
  • 3D Reconstruction - Single Viewpoint - Coursera — Offered by Columbia University. This course focuses on the recovery of the 3D structure of a scene from its 2D images. In particular, we are ... Enroll for free.
  • GitHub - eugeneyan/open-llms: A list of open LLMs available for ... — These LLMs (Large Language Models) are all licensed for commercial use (e.g., Apache 2.0, MIT, OpenRAIL-M). Contributions welcome!
  • GitHub - hiyouga/LLaMA-Factory: Unified Efficient Fine-Tuning of 100 ... — You can also use Easy Dataset or GraphGen to create synthetic data for fine-tuning.