  Neural fields have revolutionized the area of 3D reconstruction and novel
view synthesis of rigid scenes. A key challenge in making such methods
applicable to articulated objects, such as the human body, is to model the
deformation of 3D locations between the rest pose (a canonical space) and the
deformed space. We propose a new articulation module for neural fields,
Fast-SNARF, which finds accurate correspondences between canonical space and
posed space via iterative root finding. Fast-SNARF is a drop-in replacement in
functionality to our previous work, SNARF, while significantly improving its
computational efficiency. We contribute several algorithmic and implementation
improvements over SNARF, yielding a speed-up of $150\times$. These improvements
include voxel-based correspondence search, pre-computing the linear blend
skinning function, and an efficient software implementation with CUDA kernels.
Fast-SNARF enables efficient and simultaneous optimization of shape and
skinning weights given deformed observations without correspondences (e.g. 3D
meshes). Because learning of deformation maps is a crucial component in many 3D
human avatar methods and since Fast-SNARF provides a computationally efficient
solution, we believe that this work represents a significant step towards the
practical creation of 3D virtual humans.
