This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a dynamic 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 will be available at https://zju3dv.github.io/animatable_nerf/.

中文翻译：

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用于人体建模的可动画神经辐射场

本文解决了从多视点视频重建可动画动物模型的挑战。最近的一些工作提出将动态场景分解为规范的神经辐射场和一组将观察空间点映射到规范空间的变形场，从而使他们能够从图像中学习动态场景。但是，它们将变形场表示为平移矢量场或SE（3）场，这使优化受到了严重的约束。而且，这些表示不能通过输入运动来明确控制。相反，我们引入神经混合权重字段以产生变形场。基于骨骼驱动的变形，混合权重字段与3D人体骨骼一起使用，以生成观察到规范和规范到观测的对应关系。由于3D人体骨骼更易观察，因此它们可以规范变形场的学习。此外，可以将学习到的混合权重字段与输入的骨骼运动相结合，以生成新的变形场来对人体模型进行动画处理。实验表明，我们的方法明显优于最近的人工合成方法。该代码将在https://zju3dv.github.io/animatable_nerf/上提供。