Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans
Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai,, Hujun Bao, Xiaowei Zhou

TL;DR
Neural Body introduces a novel implicit neural representation with shared latent codes and a deformable mesh to improve sparse-view novel view synthesis of dynamic humans, outperforming prior methods.
Contribution
The paper proposes Neural Body, a new approach that integrates observations over video frames using shared latent codes and a deformable mesh for efficient 3D human reconstruction.
Findings
Outperforms prior methods in view synthesis quality on ZJU-MoCap dataset
Successfully reconstructs moving persons from monocular videos
Demonstrates efficiency and accuracy in sparse-view scenarios
Abstract
This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. To evaluate our approach, we create a multi-view…
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Taxonomy
TopicsAdvanced Vision and Imaging · 3D Shape Modeling and Analysis · Human Pose and Action Recognition
MethodsFast Attention Via Positive Orthogonal Random Features · Performer · Robinhood Customer Care Number +1-833-534-1729
