In this paper, we introduce a neural rendering pipeline for transferring the
facial expressions, head pose, and body movements of one person in a source
video to another in a target video. We apply our method to the challenging case
of Sign Language videos: given a source video of a sign language user, we can
faithfully transfer the performed manual (e.g., handshape, palm orientation,
movement, location) and non-manual (e.g., eye gaze, facial expressions, mouth
patterns, head, and body movements) signs to a target video in a
photo-realistic manner. Our method can be used for Sign Language Anonymization,
Sign Language Production (synthesis module), as well as for reenacting other
types of full body activities (dancing, acting performance, exercising, etc.).
We conduct detailed qualitative and quantitative evaluations and comparisons,
which demonstrate the particularly promising and realistic results that we
obtain and the advantages of our method over existing approaches.Comment: Accepted at AI4CC Workshop at CVPR 202