Heng (Henry) Yu

I am a CS PhD student at Stanford University, advised by Prof. Ehsan Adeli in the STAI Lab. My research sits at the intersection of computer vision, 3D generative modeling, and AI for healthcare.

Before Stanford, I received my master's degree from the CMU Robotics Institute, where I worked on 3D vision with Prof. Laszlo Jeni. I also collaborated closely with Prof. Berkin Bilgic at Harvard Medical School on MRI reconstruction, and with Prof. Cheng Jin at Shanghai Jiao Tong University on medical vision.

I obtained my bachelor's degree from Tsinghua University, majoring in Automation with a second major in Economics and Management. My long-term goal is to build AI systems that are both technically strong and practically useful, especially in embodied perception, visual generation, and healthcare settings.

I enjoy working with motivated people across academia and industry. If you'd like to collaborate, feel free to reach out.

yuheng[at]stanford.edu

CV  /  Google Scholar  /  GitHub  /  LinkedIn

I am primarily interested in video generation and understanding, world modeling, and Embodied AI. More broadly, I want to build visual intelligence systems that can model dynamic environments, understand how the world evolves over time, and support decision-making and interaction in open-world settings.

• Sep 2024: 4Real paper is accepted by NeurIPS 2024.
• Feb 2024: CoGS paper is accepted by CVPR 2024.
• Feb 2023: DyLiN paper is accepted by CVPR 2023.
• Feb 2023: SubZero abstract is accepted by ISMRM 2023 as a power pitch.
• Dec 2022: CoNFies paper is nominated as a best paper candidate.

Reviewer for CVPR, ICCV, ECCV, NeurIPS, SIGGRAPH, MICCAI, ISBI, Computer Graphics Forum, and ISMRM.

* indicates co-first author. Please see my Google Scholar for the full publication list.

We propose 4Real, the first photorealistic text-to-4D scene generation pipeline.

CoGS enables controllable Gaussian Splatting for dynamic scenes with direct scene manipulation and real-time control.

DyLiN extends light field networks to dynamic, non-rigid scenes with strong visual fidelity and efficiency.

CoNFies is a fully automatic controllable neural representation for face self-portraits.

SubZero improves subspace-based zero-shot self-supervised MRI reconstruction with a parallel architecture and attention mechanism.

eRAKI accelerates RAKI by directly learning a coil-combined target for robust and efficient MRI reconstruction.

A multi-task deep learning framework for tumor segmentation and treatment response prediction from longitudinal medical images.

Website template adapted from Jon Barron.