TalkinNeRF: Animatable Neural Fields for Full-Body Talking Humans
Authors: name: Aggelina Chatziagapi, name: Bindita Chaudhuri, name: Amit Kumar, name: Rakesh Ranjan, name: Dimitris Samaras, name: Nikolaos Sarafianos

We introduce a novel framework that learns a dynamic neural radiance field
(NeRF) for full-body talking humans from monocular videos. Prior work
represents only the body pose or the face. However, humans communicate with
their full body, combining body pose, hand gestures, as well as facial
expressions. In this work, we propose TalkinNeRF, a unified NeRF-based network
that represents the holistic 4D human motion. Given a monocular video of a
subject, we learn corresponding modules for the body, face, and hands, that are
combined together to generate the final result. To capture complex finger
articulation, we learn an additional deformation field for the hands. Our
multi-identity representation enables simultaneous training for multiple
subjects, as well as robust animation under completely unseen poses. It can
also generalize to novel identities, given only a short video as input. We
demonstrate state-of-the-art performance for animating full-body talking
humans, with fine-grained hand articulation and facial expressions.