Rishabh Dabral
Papers
5
Total Citations
178
H-Index
4
About
Rishabh Dabral is a researcher working at the intersection of computer vision, computer graphics, and human-computer interaction, with particular expertise in human motion synthesis, 3D reconstruction, and wearable robotic systems. His most notable contribution, MoFusion, introduced a denoising-diffusion-based framework that fundamentally advanced the field of conditional human motion synthesis, elegantly resolving the long-standing tension between motion diversity and quality that plagued earlier approaches. The work has garnered over 127 citations since 2023, signaling its rapid adoption by the research community. Dabral has also made meaningful strides in dense monocular non-rigid 3D reconstruction, contributing a comprehensive state-of-the-art survey that serves as an essential reference for researchers tackling this notoriously ill-posed problem. Beyond purely computational work, his research extends into embodied human augmentation, where he has developed tools for the computational design of personalized wearable robotic limbs and investigated naturalistic human-robot handovers using supernumerary robotic limbs through the 3HANDS dataset. Together, these contributions position Dabral as a versatile and forward-thinking researcher whose work bridges generative AI, 3D vision, and physical human augmentation.
Research Focus
Key Achievements
Top Papers
- 1MoFusion: A Framework for Denoising-Diffusion-Based Motion Synthesis127 citations · 2023
- 2State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction36 citations · 2023
- 3Computational Design of Personalized Wearable Robotic Limbs8 citations · 2023
- 4MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis5 citations · 2022
- 5