Christian Theobalt

TL;DR: This work proposes a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network, and uses a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images.

TL;DR: Face2Face addresses the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling and convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination.

TL;DR: A combined hardware and software solution for markerless reconstruction of non-rigidly deforming physical objects with arbitrary shape in real-time, an order of magnitude faster than state-of-the-art methods, while matching the quality and robustness of many offline algorithms.

TL;DR: This work introduces Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering that is over 10 times faster than the state-of-the-art (namely, NeRF) at inference time while achieving higher quality results.

TL;DR: The novelty of the approach lies in the transfer and photorealistic re-rendering of facial deformations and detail into the target video in a way that the newly-synthesized expressions are virtually indistinguishable from a real video.