Christian Theobalt

TL;DR: A novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video) that addresses the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling and re-render the manipulated output video in a photo-realistic fashion.

TL;DR: In this paper, a fully-convolutional pose formulation was proposed to regress 2D and 3D joint positions jointly in real-time and does not require tightly cropped input frames.

TL;DR: In this paper, a robust pose estimation strategy is proposed for real-time, high-quality, 3D scanning of large-scale scenes using RGB-D input with an efficient hierarchical approach, which removes heavy reliance on temporal tracking and continually localizes to the globally optimized frames instead.

TL;DR: A system that uses multi-view synchronized video footage of an actor's performance to estimate motion parameters and to interactively re-render the actor's appearance from any viewpoint, yielding a highly naturalistic impression of the actor.

TL;DR: This work presents the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera and shows that the approach is more broadly applicable than RGB-D solutions, i.e., it works for outdoor scenes, community videos, and low quality commodity RGB cameras.