M
Marc Stamminger
Researcher at University of Erlangen-Nuremberg
Publications - 169
Citations - 9098
Marc Stamminger is an academic researcher from University of Erlangen-Nuremberg. The author has contributed to research in topics: Rendering (computer graphics) & Radiosity (computer graphics). The author has an hindex of 36, co-authored 158 publications receiving 7293 citations. Previous affiliations of Marc Stamminger include Bauhaus University, Weimar & French Institute for Research in Computer Science and Automation.
Papers
More filters
Proceedings ArticleDOI
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
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.
Journal ArticleDOI
Real-time 3D reconstruction at scale using voxel hashing
TL;DR: An online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure.
Proceedings ArticleDOI
Demo of Face2Face: real-time face capture and reenactment of RGB videos
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.
Proceedings ArticleDOI
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
TL;DR: It is shown that relatively simple visual artifacts can be already quite effective in exposing such manipulations, including Deepfakes and Face2Face.
Posted Content
Face2Face: Real-time Face Capture and Reenactment of RGB Videos
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.