Pradyumna YM
Papers
1
Total Citations
2
H-Index
1
About
Pradyumna YM is a leading researcher in computer vision and 3D scene understanding, with a primary focus on reconstructing physically plausible human-scene interactions from monocular imagery. His most notable contribution is the development of **PhySIC** (2025), a groundbreaking framework that reconstructs metrically accurate 3D humans and their surrounding environments from a single image—a task long hindered by depth ambiguity, occlusions, and physically inconsistent contacts. By integrating contact reasoning with geometric and physical plausibility constraints, PhySIC sets a new standard for virtual reality, robotics, and holistic 3D scene comprehension. Though early in its lifecycle, the work has already garnered 2 citations, signaling strong interest from the community. Pradyumna’s research addresses a critical gap: enabling machines to infer not just *what* is in a scene, but *how* humans interact with it in a physically coherent way. His work promises to advance embodied AI, AR/VR, and autonomous systems that require realistic human-scene coupling. As a rising voice in 3D vision, Pradyumna YM is shaping the future of physically grounded scene understanding.
Research Focus
Key Achievements
Top Papers
- 1