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-to-canonical 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. The code and
supplementary materials are available at
this https URL

该论文提出了一种通过神经融合权重场和骨骼驱动变形的方法，从多视角视频中重建可动画的人体模型，相较于现有的人体合成方法，该方法具有显著优势。