DreamFusion

DreamFusion [MM-MODELS-DF1] uses a pretrained text-to-image diffusion model to perform text-to-3D synthesis. The model uses a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator.

Using this loss in a DeepDream-like procedure, the model optimizes a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss. The resulting 3D model of the given text can be viewed from any angle, relit by arbitrary illumination, or composited into any 3D environment. This approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors.

Dreamfusion models can be instantiated using the DreamFusion class.

dreamfusion_model_overview1.png

Image guidance

This section of DreamFusion pertains to the initial phase where the model interprets and translates text inputs into visual concepts. Utilizing a diffusion based text-to-image model, DreamFusion processes the text input, extracts key visual elements, and translates these into initial 2D images. The process ensures that the generated 3D models are not only accurate in terms of the text description but also visually coherent and detailed by conditioning the 2D image based on the view angle.

NeRF (foreground) network

The Neural Radiance Fields (NeRF) network is at the heart of DreamFusion’s 3D rendering capabilities. In DreamFusion, the NeRF network takes the 2D images generated from the textual description and constructs a 3D model. This model is represented as a continuous volumetric scene function, which encodes the color and density of points in space, allowing for highly detailed and photorealistic renderings.

Background layer

DreamFusion can leverage a background layer dedicated to background modeling.

In scenarios where a dynamic background is needed, DreamFusion can be configured to use a secondary NeRF network to generate a background. This network functions in parallel to the primary NeRF network, focusing on creating a coherent and contextually appropriate backdrop for the main scene. It dynamically adjusts to lighting and perspective changes, maintaining consistency with the foreground model.

Alternatively, DreamFusion allows for the integration of a static background color, which is particularly useful in scenarios where the focus is predominantly on the object being generated, and a non-distracting backdrop is desirable. Implementing a static color background involves setting a uniform chromatic value that encompasses the periphery of the 3D model. This approach simplifies the rendering process and can be beneficial in reducing computational load while maintaining focus on the primary object.

Materials network

The material network in DreamFusion is responsible for adding realism to the 3D models by accurately simulating the physical properties of different materials. This network takes into account various aspects like texture, reflectivity, and transparency. By doing so, it adds another layer of detail, making the objects generated by DreamFusion not just structurally accurate but also visually and tactilely realistic.

Renderer layer

The renderer layer functions as the culminating stage in DreamFusion’s processing pipeline. It translates the synthesized volumetric data from the NeRF and material networks into perceptible imagery. Employing ray-tracing algorithms, this layer computes the interaction of light with the 3D scene, producing images that exhibit sophisticated attributes like accurate shadow casting, dynamic lighting, and perspective-correct renderings.

DreamFusion models can be instantiated using the DreamFusion class. The model configuration file is organized into the following sections:

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_target_: nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion defaults: - nerf: torchngp - background: static - material: basic_shading - renderer: torchngp_raymarching - guidance: sd_huggingface - optim: adan - loss: dreamfusion - data: data - _self_ ### model options resume_from_checkpoint: prompt: 'ahamburger' negative_prompt: '' front_prompt: ',frontview' side_prompt: ',sideview' back_prompt: ',backview' update_extra_interval: 16 guidance_scale: 100 export_video: False iters: ${trainer.max_steps} latent_iter_ratio: 0.2 albedo_iter_ratio: 0.0 min_ambient_ratio: 0.1 textureless_ratio: 0.2 data: train_dataset: width: 64 height: 64 val_dataset: width: 800 height: 800 test_dataset: width: 800 height: 800

  • defaults: Defines default modules for different components like nerf, background, material, etc.

  • resume_from_checkpoint: Path to a checkpoint file to initialize the model with.

  • prompt: Main textual input for the model describing the object to generate.

  • negative_prompt: Textual input describing what to avoid in the generated object.

  • front_prompt, side_prompt, back_prompt: Textual inputs that are appended to the prompts for more detailed orientation guidance.

  • update_extra_interval: Interval for updating internal module parameters.

  • guidance_scale: The guidance scaled used with the diffusion model.

  • export_video: Boolean to determine whether to export a 360 video of the generated object.

  • iters, latent_iter_ratio, albedo_iter_ratio, min_ambient_ratio, textureless_ratio: Various ratios and parameters defining iteration behavior and visual characteristics of the output.

  • data: Defines dataset dimensions for training, validation, and testing.

The behavior of the pipeline can be precisely adjusted by fine-tuning the parameters of various components in the default section. Some components support different backends and implementations, the full components catalog can be viewed in the config directory {NEMO_ROOT/examples/multimodal/generative/nerf/conf/model}.

Image guidance

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_target_: nemo.collections.multimodal.modules.nerf.guidance.stablediffusion_huggingface_pipeline.StableDiffusion precision: ${trainer.precision} model_key: stabilityai/stable-diffusion-2-1-base t_range: [0.02, 0.98]

  • precision: Sets the precision of computations (e.g., FP32 or FP16).

  • model_key: Specifies the pre-trained model to use for image guidance.

  • t_range: Range of threshold values for guidance stability.

NeRF (foreground) network

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_target_: nemo.collections.multimodal.modules.nerf.geometry.torchngp_nerf.TorchNGPNerf num_input_dims: 3 bound: 1 density_activation: exp blob_radius: 0.2 blob_density: 5 normal_type: central_finite_difference encoder_cfg: encoder_type: 'hashgrid' encoder_max_level: log2_hashmap_size: 19 desired_resolution: 2048 interpolation: smoothstep sigma_net_num_output_dims: 1 sigma_net_cfg: num_hidden_dims: 64 num_layers: 3 bias: True features_net_num_output_dims: 3 features_net_cfg: num_hidden_dims: 64 num_layers: 3 bias: True

Describes the NeRF network’s architecture, including the density activation function, network configuration, and the specification of the sigma and features networks.

Background layer

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_target_: nemo.collections.multimodal.modules.nerf.background.static_background.StaticBackground background: [0, 0, 1]

Static background, where the background key is the RGB color.

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_target_: nemo.collections.multimodal.modules.nerf.background.torchngp_background.TorchNGPBackground encoder_type: "frequency" encoder_input_dims: 3 encoder_multi_res: 6 num_output_dims: 3 net_cfg: num_hidden_dims: 32 num_layers: 2 bias: True

Dynamic background, where the background is generated by a NeRF network.

Materials network

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_target_: nemo.collections.multimodal.modules.nerf.materials.basic_shading.BasicShading

Defines the basic shading model for the material network. The basic shading model supports textureless, lambertian and phong shading.

Renderer layer

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_target_: nemo.collections.multimodal.modules.nerf.renderers.torchngp_volume_renderer.TorchNGPVolumeRenderer bound: ${model.nerf.bound} update_interval: 16 grid_resolution: 128 density_thresh: 10 max_steps: 1024 dt_gamma: 0

Configures the renderer, specifying parameters like update interval, grid resolution, and rendering thresholds.

NeRF models integrate geometry and appearance through volume rendering. As a result, using NeRF for 3D modeling can be less effective when it comes to capturing both the intricate details of a surface as well as its material and texture.

DMTet finetunning disentangles the learning of geometry and appearance models, such that both a fine surface and a rich material/texture can be generated. To enable such a disentangled learning, a hybrid scene representation of [DMTet](https://nv-tlabs.github.io/DMTet/) is used.

The DMTet model maintains a deformable tetrahedral grid that encodes a discretized signed distance function and a differentiable marching tetrahedra layer that converts the implicit signed distance representation to the explicit surface mesh representation.

Model Configuration

DreamFusion models can be instantiated using the same class as DreamFusion DreamFusion. However, the following changes to the training pipeline are necessary:

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_target_: nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion defaults: - nerf: torchngp - background: torchngp - material: basic_shading - renderer: nvdiffrast # (1) - guidance: sd_huggingface - optim: adan - loss: dmtet # (2) - data: data - _self_ ### model options resume_from_checkpoint: "/results/DreamFusion/checkpoints/DreamFusion-step\=10000-last.ckpt" # (3) prompt: 'a hamburger' negative_prompt: '' front_prompt: ', front view' side_prompt: ', side view' back_prompt: ', back view' update_extra_interval: 16 guidance_scale: 100 export_video: False iters: ${trainer.max_steps} latent_iter_ratio: 0.0 albedo_iter_ratio: 0 min_ambient_ratio: 0.1 textureless_ratio: 0.2 data: train_dataset: width: 512 # (4) height: 512 # (4) val_dataset: width: 800 height: 800 test_dataset: width: 800 height: 800

We note the following changes: 1. The rendering module was changed from a volumetric based one to a rasterization based one (nvdiffrast). 2. The model loss is changed to account for the changes in the geometry representation. 3. DreamFusion-DMTet finetunes a pretrained DreamFusion model, the pretrained checkpoint is provided using resume_from_checkpoint. 4. The training shape is incrased to 512x512

Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: text-to-3d using 2d diffusion. 2022. URL: https://arxiv.org/abs/2209.14988.

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