AnimatableNeRF
AnimatableNeRF
: Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies#
Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, Hujun Bao
NeRF
SMPL
ICCV 2021
TPAMI 2024
zju3dv/animatable_nerf
Code for “Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos” TPAMI 2024, ICCV 2021
Python
507
51
Abstract#
This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. Instead, we introduce neural blend weight fields to produce the deformation fields. Based on the skeleton-driven deformation, blend weight fields are used with 3D human skeletons to generate observation-tocanonical and canonical-to-observation correspondences. Since 3D human skeletons are more observable, they can regularize the learning of deformation fields. Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. Experiments show that our approach significantly outperforms recent human synthesis methods.
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Papers Published @ 2021 - This article is part of a series.
Part 3: This Article