- RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
- SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
- Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
- Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories
- ResFields: Residual Neural Fields for Spatiotemporal Signals
- Dynamic Point Fields: Towards Efficient and Scalable Dynamic Surface Representations
- HARP: Personalized Hand Reconstruction from a Monocular RGB Video
Research Experience
Position: Senior Scientist
Location: CNB G 103.1
Email: sergey.prokudin@inf.ethz.ch
Background
Research Interests: Developing robust and efficient algorithms for the analysis, synthesis, and prediction of complex 3D real-world phenomena. Particularly interested in the area that combines classic computer graphics with modern deep learning approaches.