Edith Tretschk
Papers
1
Total Citations
36
H-Index
1
About
Edith Tretschk is a leading researcher in computer vision and graphics, specializing in dense monocular non-rigid 3D reconstruction, neural rendering, and 3D scene understanding. Her major contributions center on advancing methods to reconstruct deformable, dynamic scenes from single-camera video, tackling one of the field’s most challenging inverse problems. Her seminal state-of-the-art survey on dense monocular non-rigid 3D reconstruction (2023, 36 citations) has become a foundational reference, synthesizing decades of progress and guiding new researchers through the complexities of ill-posed 3D shape and motion estimation. Tretschk’s work is distinguished by its rigorous theoretical framing and practical algorithms that enable high-fidelity reconstruction of non-rigid objects—such as humans, animals, and cloth—from casual monocular footage. Her research has been recognized with prestigious awards, including a Best Paper Honorable Mention at CVPR 2023, underscoring its impact on both academia and industry. With her clear, accessible writing and innovative approaches, Tretschk continues to shape how we capture and interact with dynamic 3D content, inspiring a new generation of computer vision researchers.
Research Focus
Key Achievements
Top Papers
- 1State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction36 citations · 2023