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.

该论文提出了一种基于静态基于点的辐射场的新颖可控人体动作合成方法，通过编码点云转换来应用变形，并利用奇异值分解估计局部旋转以使渲染结果与规范空间训练一致。实验表明，我们的方法在精细级复杂变形方面明显优于现有技术，且可推广到除人物之外的其他 3D 角色。