Hansheng Chen 陈涵晟

About me

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.

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Selected Publications
Gaussian Mixture Flow Matching Models

Hansheng Chen, Kai Zhang, Hao Tan, Zexiang Xu, Fujun Luan, Leonidas Guibas, Gordon Wetzstein, Sai Bi
arXiv, 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.

arXiv GitHub
3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation

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.

project page Demo arXiv GitHub
Zero123++: A Single Image to Consistent Multi-view Diffusion Base Model

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.

Demo arXiv GitHub
Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

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.

project page arXiv GitHub
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

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.

arXiv GitHub video