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, Bokui Shen, Yulin Liu, Ruoxi Shi, Linqi Zhou, Connor Z. Lin, Jiayuan Gu,
Hao Su, Gordon Wetzstein, Leonidas Guibas
arXiv, 2024
3D-Adapter enables high-quality 3D generation using a 3D feedback module attached to a base image diffusion model for enhanced geometry consistency.
Hansheng Chen, Ruoxi Shi, Yulin Liu, Bokui Shen, Jiayuan Gu, Gordon Wetzstein, Hao Su,
Leonidas Guibas
arXiv, 2024
MVEdit is a training-free 3D-Adapter that enables 3D generation/editing using off-the-shelf 2D Stable Diffusion models. An updated version of MVEdit has been merged into the 3D-Adapter project.
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, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, Hao Su
ICCV, 2023
With 3D diffusion models and NeRFs trained in a single stage, SSDNeRF learns powerful 3D generative prior from multi-view images, which can be exploited for unconditional generation and image-based 3D reconstruction.
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.