HeadOn: Real-time Reenactment of Human Portrait Videos

TL;DR

HeadOn is a real-time system that enables photo-realistic reenactment of human portrait videos by transferring head, face, and torso motion from a source to a target using a personalized geometry proxy and advanced rendering techniques.

Contribution

This work introduces the first real-time source-to-target portrait reenactment method that captures and transfers complex head, face, and torso motions with high realism.

Findings

Achieves real-time performance for portrait reenactment.

Produces photo-realistic images under novel poses and expressions.

Demonstrates significant improvements over previous methods.

Abstract

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.