Relightable Neural Head Rendering via Decoupled Geometry and Lighting
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Solution Overview
Problem
Existing techniques for generating 3D representations of scenes, such as neural radiance fields (NeRF) and Mixture of Volumetric Primitives (MVP), struggle to produce realistic renderings under arbitrary lighting conditions and from arbitrary viewpoints, as they encode scene illumination characteristics from specific training conditions.
Innovation Solution
The technique involves receiving 3D input meshes and virtual camera and lighting positions, and using a trained machine learning model to generate rendered frames that depict objects from arbitrary viewpoints and under arbitrary lighting conditions, allowing for the creation of animatable and relightable 3D scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing NeRF or MVP representations are trained on 2D representations captured under specific illumination conditions, then the model can generate 3D representations from those specific viewpoints and lighting conditions, but the output cannot be realistically rendered under different illumination conditions or arbitrary viewpoints
Solution Approach 1:
The invention segments the 3D scene representation into separate components: geometry (via mesh), material properties (via texture maps), and illumination characteristics. By decoupling these elements, the system can render the same 3D scene under different lighting conditions without retraining, as the illumination is treated as a separate controllable parameter rather than being baked into the neural representation.
Solution Approach 2:
The system enables parameter changes by allowing arbitrary modification of lighting parameters (light position, intensity, color temperature) and camera parameters (viewpoint, focal length) after training. The trained model learns the scene's geometric and material properties independently of specific lighting conditions, enabling realistic rendering when these parameters are changed to match new environmental conditions.
2Measurement precision
If NeRF models are trained to capture scene illumination characteristics from specific training conditions, then the model achieves accurate rendering for those specific conditions, but fails to generalize to different environments with different illumination conditions
Solution Approach 1:
The invention extracts and separates the illumination characteristics from the core 3D scene representation. By using a mesh-based geometric model combined with material properties, the system removes the dependency on specific lighting conditions being embedded in the representation. The illumination is then applied as a separate rendering parameter, allowing the model to generalize across different environments while maintaining accurate scene geometry and material properties.
3Loss of information
If existing techniques encode scene illumination present in training 2D representations, then the model learns the characteristics of scene illumination, but the output cannot be realistically rendered within another environment that has different illumination conditions
Solution Approach 1:
The system performs preliminary action by training the model on diverse illumination conditions during the training phase, enabling the model to learn robust geometric and material properties that are invariant to specific lighting. This preliminary exposure to various lighting scenarios allows the model to generalize effectively when deployed in different environments, as the core representation has already been conditioned to handle illumination variations.
Data Source
AI summary
The present invention sets forth techniques for generating an animation sequence. The techniques include receiving one or more three-dimensional (3D) input meshes, wherein each input mesh includes a representation of an object included in a 3D scene. The techniques also include receiving, for each of the 3D input meshes, a virtual camera position associated with the 3D input mesh and one or more virtual lighting positions associated with the 3D input mesh. The techniques further include generating, for each of the 3D input meshes and via a trained machine learning model, one or more rendered frames associated with the 3D input mesh, wherein each rendered frame includes a two-dimensional (2D) representation of the object as viewed from the virtual camera position and illuminated by one or more virtual lights located at the one or more virtual lighting positions, and generating an output animation sequence based on the rendered frames.