I am a second-year PhD student at Stanford University, co-advised by Prof. Leonidas Guibas and Prof. Gordon Wetzstein. My research is generously supported by the Qualcomm Innovation Fellowship.
I am passionate about generative models and their applications in vision and graphics, with a current focus on diffusion models and 3D generation. Previously, I worked on image-based 6DoF pose estimation, and my work EPro-PnP was awarded the CVPR 2022 Best Student Paper.
Hansheng Chen, Kai Zhang, Hao Tan, Zexiang Xu, Fujun Luan, Leonidas Guibas, Gordon Wetzstein, Sai Bi
ICML, 2025
GMFlow generalizes diffusion/flow matching models by predicting Gaussian mixture denoising distributions. It introduces novel GM-SDE/ODE solvers for precise few-step sampling and probabilistic guidance for high-quality generation.
Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, Hao Su
Technical report, 2023
Zero123++ transforms a single RGB image of any object into high-quality multiview images with superior 3D consistency, serving as a strong base model for image-to-3D generative tasks.
Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, Hao Li
CVPR, 2022 (Best Student Paper)
We present a probabilistic PnP layer for end-to-end 6DoF pose learning. The layer outputs the pose distribution with differentiable probability density, so that the 2D-3D correspondences can be learned flexibly by backpropagating the pose loss.
Hansheng Chen, Wei Tian, Pichao Wang, Fan Wang, Lu Xiong, Hao Li
TPAMI, 2024
The updated paper features improved models with better results on both the LineMOD and nuScenes benchmark. Morever, we have added more discussions on the loss functions, which are supported by rigorous ablation studies.