Point-Based Radiance Fields for Controllable Human Motion Synthesis
Haitao Yu Deheng Zhang Peiyuan Xie Tianyi Zhang
This paper proposes a novel controllable human motion synthesis method for
fine-level deformation based on static point-based radiance fields. Although
previous editable neural radiance field methods can generate impressive results
on novel-view synthesis and allow naive deformation, few algorithms can achieve
complex 3D human editing such as forward kinematics. Our method exploits the
explicit point cloud to train the static 3D scene and apply the deformation by
encoding the point cloud translation using a deformation MLP. To make sure the
rendering result is consistent with the canonical space training, we estimate
the local rotation using SVD and interpolate the per-point rotation to the
query view direction of the pre-trained radiance field. Extensive experiments
show that our approach can significantly outperform the state-of-the-art on
fine-level complex deformation which can be generalized to other 3D characters
besides humans.