3D Character Motion Authoring From Temporally-Sparse Photos

This paper presents a neural network-based learning approach that enables seamless generation of 3D human motion in-between photos to accelerate the process of 3D character motion authoring. This new approach allows users to freely edit (replace, insert, or delete) input photos and specify the transition length to generate a kinematically coherent sequence of 3D human poses and shapes in-between the given photos. We demonstrate through qualitative and subjective evaluations that our approach is capable of generating high-fidelity, natural 3D pose and shape transitions.