基于点的辐射场用于可控人体运动合成
Point-Based Radiance Fields for Controllable Human Motion Synthesis
AI总结：
针对现有可编辑神经辐射场难以实现复杂3D人体编辑的问题，提出基于静态点辐射场的可控人体运动合成方法，通过形变MLP编码点云位移、SVD估计局部旋转保障渲染一致性，精细复杂形变性能优于SOTA且可推广至其他3D角色。
摘要：
本文提出了一种新颖的可控人体运动合成方法，该方法基于静态的基于点的辐射场实现精细层级的形变。尽管以往的可编辑神经辐射场方法能够在新视角合成方面取得出色效果，并支持简单形变，但鲜有算法能够实现诸如正向运动学这类复杂的3D人体编辑。我们的方法利用显式点云来训练静态3D场景，并通过形变MLP（多层感知机）对点云位移进行编码以施加形变。为确保渲染结果与规范空间训练保持一致，我们采用SVD（奇异值分解）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上。大量实验表明，我们的方法在精细层级复杂形变任务上的性能显著优于现有最优方法，且该方法可推广至人体之外的其他3D角色。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
