I am a first-year Ph.D. student at MMLab, The Hong Kong University of Science and Technology (HKUST), jointly advised by Prof. Anyi Rao and Prof. Huamin Qu. I received my bachelor’s degree from Nanjing University (NJU), advised by Prof. Xun Cao. I interned at SenseTime guided by Lei Yang and Xiangyu Fan, and at the Beijing Academy of Artificial Intelligence (BAAI) and AIR, Tsinghua University guided by Prof. Hao Zhao. These internships gave me valuable research experience, and I am grateful to all my mentors and collaborators for their guidance and support!

My current research interests include visual content creation, image and video generation, and unified multimodal models. Feel free to reach out if you are interested in my research or would like to discuss potential collaborations!

🔥 News

  • 2026: 🎉 1 paper accepted to ECCV 2026!
  • 2026: 🎉 2 papers accepted to SIGGRAPH 2026 (including 1 ACM TOG paper)!
  • 2026: 🎉 1 paper accepted to CVPR 2026 as a Highlight!
  • 2026: 🎉 2 papers accepted to ICLR 2026!

📝 Selected Papers

arXiv 2026
MT Forcing results comparing image quality and consistency across multiple editing turns

MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion

Houyuan Chen*, Fanzhou Wang*, Xiangyu Fan, Zesong Qiu, Yukun Wei, Songlin Yang, Zhitao Yang, Lvmin Zhang, Pei Yang, Hao Zhao, Dahua Lin, Lei Yang, Anyi Rao

  • A training framework that improves visual quality and content consistency for multi-turn image editing, where models iteratively build on their own outputs.
arXiv 2026
MTEdit overview comparing packed trajectory canvas generation with sequential rollout, dataset scale, and multi-turn editing results

MTEdit: Packed Trajectories Beat Rollouts for Multi-Turn Image Editing

Houyuan Chen*, Fanzhou Wang*, Xiangyu Fan, Songlin Yang, Zhitao Yang, Lvmin Zhang, Pei Yang, Hao Zhao, Dahua Lin, Lei Yang, Anyi Rao

  • MTEdit uses packed trajectory canvases to construct 500K multi-turn image editing trajectories, alongside MTEdit-Bench and Qwen-Image-MTEdit for evaluating and improving multi-turn editing.
SIGGRAPH 2026 · ACM TOG
UniVidX framework for conditional generation across visual modalities

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors

Houyuan Chen, Hong Li, Xianghao Kong, Tianrui Zhu, Shaocong Xu, Weiqing Xiao, Yuwei Guo, Chongjie Ye, Lvmin Zhang, Hao Zhao, Anyi Rao

  • A unified framework that uses video diffusion priors for generation across RGB, intrinsic maps, and alpha layers, supporting tasks including relighting, decomposition, and matting.
ICLR 2026
LINO-UniPS results showing surface normal reconstruction under unknown lighting

Light of Normals: Unified Feature Representation for Universal Photometric Stereo

Houyuan Chen*, Hong Li*, Chongjie Ye, Zhaoxi Chen, Bohan Li, Shaocong Xu, Xianda Guo, Xuhui Liu, Yikai Wang, Baochang Zhang, Satoshi Ikehata, Boxin Shi, Anyi Rao, Hao Zhao

  • Decoupling lighting and geometry for detailed surface normal estimation under arbitrary, unknown illumination, with the PS-Verse training dataset.

🎓 Education

  • 2026 - Present, Ph.D. Student, MMLab, The Hong Kong University of Science and Technology (HKUST)
    • Current Research Directions: Visual Content Creation, Image and Video Generation, Unified Multimodal Models
    • Supervisors: Prof. Anyi Rao, Prof. Huamin Qu
  • 2022 - 2026, Bachelor’s Degree in EE, Nanjing University (NJU)

💻 Internships

🎖 Awards

  • National Scholarship
  • China Mobile Scholarship
  • Some High-Value Scholarships at Nanjing University

🕴️ Services

  • Organizer of the CVEU Workshop at SIGGRAPH 2025, SIGGRAPH 2026, and SIGGRAPH Asia 2026.
  • Conference Reviewer for CVPR, ECCV, ICLR, and SIGGRAPH Asia.

💻 Open Source

  • Awesome-Relight GitHub stars for Awesome-Relight: A curated collection of relighting papers, datasets, benchmarks, demos, and tools.

🎤 Talks