  We introduce You Only Train Once (YOTO), a dynamic human generation
framework, which performs free-viewpoint rendering of different human
identities with distinct motions, via only one-time training from monocular
videos. Most prior works for the task require individualized optimization for
each input video that contains a distinct human identity, leading to a
significant amount of time and resources for the deployment, thereby impeding
the scalability and the overall application potential of the system. In this
paper, we tackle this problem by proposing a set of learnable identity codes to
expand the capability of the framework for multi-identity free-viewpoint
rendering, and an effective pose-conditioned code query mechanism to finely
model the pose-dependent non-rigid motions. YOTO optimizes neural radiance
fields (NeRF) by utilizing designed identity codes to condition the model for
learning various canonical T-pose appearances in a single shared volumetric
representation. Besides, our joint learning of multiple identities within a
unified model incidentally enables flexible motion transfer in high-quality
photo-realistic renderings for all learned appearances. This capability expands
its potential use in important applications, including Virtual Reality. We
present extensive experimental results on ZJU-MoCap and PeopleSnapshot to
clearly demonstrate the effectiveness of our proposed model. YOTO shows
state-of-the-art performance on all evaluation metrics while showing
significant benefits in training and inference efficiency as well as rendering
quality. The code and model will be made publicly available soon.
