Neural Radiance Fields for Dynamic Scene Novel View Synthesis
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Solution Overview
Problem
Current neural radiance field (NeRF)-based models fail to render novel views of dynamic scenes, especially unseen poses, and struggle to capture fine-level details such as facial expressions and cloth wrinkles in humans in motion.
Innovation Solution
An improved method that integrates pose and appearance codes anchored to human poses and point clouds, using a temporal transformer to combine features across frames, enabling high-fidelity rendering of novel views and unseen poses in dynamic scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If NeRF-based models are used for novel-view synthesis of static scenes, then photo-realistic rendering quality is achieved, but the models fail to handle dynamic scenes and unseen poses
Solution Approach 1:
The representation is segmented into pose-dependent components and appearance components. The pose codebook stores geometry and appearance at training poses, while the appearance codebook captures pose-invariant visual features. This segmentation allows the model to handle both static and dynamic scenes by combining pose-specific information with general appearance information.
Solution Approach 2:
The model extends the standard NeRF representation by adding a pose dimension. Instead of representing scenes only in 3D space, it incorporates pose parameters (joint angles, body configuration) as additional dimensions. This allows the network to condition rendering on specific poses and generalize to unseen poses through the pose codebook.
2Adaptability or versatility
If human-pose-based representation is used to model body shape, then body shape at any time step can be modeled, but fine-level details and detailed appearance are lost
Solution Approach 1:
The model merges pose-dependent representation (for body shape) with pose-invariant appearance representation (for fine-level details). The appearance codebook stores high-resolution visual features that are independent of pose, while the pose codebook handles body configuration. During rendering, both codes are combined to produce images with accurate body shape and preserved fine details like facial expressions and cloth wrinkles.
3Duration of action of moving object
If prior NeRF-based models are applied to dynamic scenes, then some motion can be captured, but novel or unseen poses cannot be synthesized
Solution Approach 1:
The model performs preliminary encoding of appearance information at multiple training poses before encountering test poses. The appearance codebook is pre-trained on diverse poses and expressions, storing pose-invariant visual features. When synthesizing unseen poses, this pre-learned appearance information is retrieved and combined with the new pose configuration, enabling generalization without requiring explicit training on all possible poses.
Data Source
AI summary
In particular embodiments, a computing system may access a particular image frame and corresponding depth information of a dynamic scene. The depth information is used to generate a point cloud of the particular image frame. The system may generate a first latent representation based on the point cloud. The system may access a sequence of image frames of the dynamic scene and a set of key frames. The system may generate, using a temporal transformer, a second latent representation based on tracking and combining temporal relationship between the sequence of image frames and the set of key frames. The system may access camera parameters for rendering the one or more objects from a desired novel viewpoint and generate a third latent representation. The system may train an improved neural radiance fields (NeRF) based model for free-viewpoint rendering of the dynamic scene based on the first, second, and third latent representations.