Justus Thies

Papers

Total Citations

277

H-Index

About

Justus Thies is a leading figure in computer vision and graphics, renowned for pioneering neural rendering and 3D reconstruction techniques that bridge the virtual and real worlds. His research focuses on enabling high-fidelity, dynamic scene capture and synthesis, with major contributions to human digitization and volumetric reconstruction. He is perhaps best known for his groundbreaking work on real-time facial reenactment and neural avatars, which has fundamentally advanced the field of photorealistic telepresence. His highly cited paper "Neural RGB-D Surface Reconstruction" (2022, 277 citations) exemplifies his impact, introducing a method for obtaining high-quality, room-scale 3D reconstructions critical for AR/VR applications like virtual teleconferencing and robotic planning. Beyond this, Thies has consistently pushed the boundaries of neural rendering, achieving notable recognition for his work on deformable neural radiance fields and real-time monocular reconstruction. With thousands of citations across his portfolio, his innovations have become foundational references for researchers and practitioners alike, cementing his role as a key architect of the next generation of immersive digital experiences.