Deformable Neural Radiance Fields for Non-Rigid View Synthesis
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Solution Overview
Problem
Conventional neural radiance fields (NeRFs) struggle to accurately synthesize views of non-rigidly deforming objects, such as people, due to their inability to account for subject movement and challenging materials like hair and glasses, leading to artifacts in rendered images.
Innovation Solution
A deformable neural radiance field (D-NeRF) is generated by deriving a deformation model that maps positions between an observation frame and a canonical frame, using latent deformation codes and multilayer perceptrons (MLPs), incorporating elastic and background loss functions to handle non-rigid deformations and maintain image coherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural radiance fields are used to synthesize views of non-rigidly deforming objects, then the rendering process is simple, but the accuracy of synthesized views deteriorates due to inability to account for subject movement and challenging materials
Solution Approach 1:
The model segments the scene into a canonical frame (template) and observation frames, separating the static structure from dynamic deformations. The deformation model divides the transformation into position mapping and appearance mapping components, allowing independent optimization of each part to improve accuracy without overwhelming complexity
Solution Approach 2:
A deformation model acts as an intermediary between the canonical frame and observation frames. This intermediary component (deformation field) bridges the static template and dynamic observations, enabling accurate synthesis of non-rigid objects by mediating the transformation through learned position and appearance mappings
2Reliability
If a deformation model is introduced to handle non-rigid deformations, then the accuracy of capturing subject movements improves, but the complexity of the rendering system increases
Solution Approach 1:
The model transitions from a static NeRF to a dynamic D-NeRF by introducing time-varying deformation fields. The deformation model adapts to non-rigid movements by learning dynamic position mappings from the canonical frame to observation frames, enabling accurate capture of movements while maintaining a structured approach to complexity
Solution Approach 2:
The model changes parameters by introducing deformation-specific parameters (position offset, appearance code) alongside the original NeRF parameters. This parameter expansion allows the system to capture movements accurately by adding controlled complexity through specific deformation parameters rather than redesigning the entire system
3Adaptability or versatility
If latent deformation codes are used to represent non-rigid deformations, then the flexibility in handling various materials improves, but the computational complexity increases
Solution Approach 1:
The model adds another dimension by introducing latent deformation codes that encode material-specific deformation characteristics. This additional dimensional representation (latent space) provides flexibility in handling various materials like hair and glasses by capturing their unique deformation patterns without requiring separate models for each material type
Solution Approach 2:
The model creates copies of the canonical frame appearance in different observation frames through the appearance mapping function. By copying and transforming the template appearance using learned deformation patterns, the system achieves versatility in handling various materials while computing only what is necessary for each specific material type
Data Source
AI summary
Techniques of image synthesis using a neural radiance field (NeRF) includes generating a deformation model of movement experienced by a subject in a non-rigidly deforming scene. For example, when an image synthesis system uses NeRFs, the system takes as input multiple poses of subjects for training data. In contrast to conventional NeRFs, the technical solution first expresses the positions of the subjects from various perspectives in an observation frame. The technical solution then involves deriving a deformation model, i.e., a mapping between the observation frame and a canonical frame in which the subject's movements are taken into account. This mapping is accomplished using latent deformation codes for each pose that are determined using a multilayer perceptron (MLP). A NeRF is then derived from positions and casted ray directions in the canonical frame using another MLP. New poses for the subject may then be derived using the NeRF.