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.

翻译：本文提出一种基于静态点云辐射场的可控人体运动合成新方法，用于实现精细级别的变形控制。尽管现有可编辑神经辐射场方法在新视角合成方面取得了显著成果且支持简单变形，但很少有算法能实现前向运动学等复杂三维人体编辑。该方法利用显式点云训练静态三维场景，并通过变形MLP编码点云平移量来施加变形。为确保渲染结果与规范空间训练保持一致，我们采用SVD估计局部旋转，并将逐点旋转插值到预训练辐射场的查询视角方向。大量实验表明，本方法在精细级别复杂变形任务上显著优于现有最优技术，且可推广至人体以外的其他三维角色。