Scholar
Fangneng Zhan
Google Scholar ID: 8zbcfzAAAAAJ
MIT
Citations & Impact
Citations
5,350
H-index
32
i10-index
43
Publications
20
Co-authors
17
list available
Publications
Resume (English only)
Academic Achievements
- [07/2025] Invited talk at ETH Zurich: “Learning to Represent and Render the 3D World”
- [03/2025] Invited talk at Imperial College London on the same topic
- [02/2025] Invited talk at Peking University on the same topic
- [04/2024] Invited talks at Harvard University’s Visual Computing Group and Stanford University’s Jiajun Wu Group
- [04/2024] Two papers accepted to SIGGRAPH 2024 and ACM TOG
- [03–02/2024] Invited talks at Tsinghua SIGS and HKU IDS on “Autonomous Rendering Intelligence”
- [12/2023] Organizing two CVPR 2024 workshops: “Neural Rendering Intelligence” and “2nd Generative Models for Computer Vision”
- [09/2023] One paper accepted to IJCV, one to NeurIPS 2023
- [08/2023] One Generative AI paper accepted to TPAMI 2023
- [07/2023] Two papers accepted to ICCV 2023
- Authored/co-authored multiple preprints including “Evolutive Rendering Models,” “PAGE-4D,” “Advances in Feed-Forward 3D Reconstruction and View Synthesis,” and “3DPR,” with collaborators from MIT, Harvard, Max Planck Institute, and others
Background
- Inspired by Richard Feynman’s dictum “What I cannot create, I do not understand,” his research focuses on developing and understanding intelligence emerging from visual generation and rendering processes (i.e., Generative AI).
- Using Neural Rendering and Generative Models as general-purpose learning machines, his research directions include:
- – Rendering-induced intelligence (e.g., Evolutive Rendering, Neural Gauge Fields)
- – Generative 3D intelligence (e.g., UNITE, IQ-VAE, SF-GAN)
- – 3D for Robotics and Science (e.g., 3D-OVS)
