Yixuan Ren

I am a Ph.D. student in the Department of Computer Science at the University of Maryland, College Park, advised by Professor Abhinav Shrivastava.

My research focuses on image/video sythensis and editing with deep generative models. I'm also interested in implicit neural representations especially for generative tasks.

Email  /  Google Scholar  /  LinkedIn

profile photo

Publications

Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models
Yixuan Ren, Yang Zhou, Jimei Yang, Jing Shi, Difan Liu, Feng Liu, Mingi Kwon, Abhinav Shrivastava
arXiv, 2024
project page / paper / arxiv / code

Customize pre-trained video diffusion models from a single reference video and adapt its motion to new subjects and scenes with motion diversity.

Content-Adapt Image Color Editing With Auxiliary Color Restoration Tasks
Yixuan Ren, Jing Shi, Zhifei Zhang, Yifei Fan, Zhe Lin, Bo He, Abhinav Shrivastava
WACV, 2024
paper / code

Edit image color with styles adaptive to its content.

Towards Scalable Neural Representation for Diverse Videos
Bo He, Xitong Yang, Hanyu Wang, Zuxuan Wu, Hao Chen, Shuaiyi Huang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava
CVPR, 2023
project page / arxiv / code

Video INR to encode large-scale and diverse video data.

NeRV: Neural Representations for Videos
Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava
NeurIPS, 2021
project page / arxiv / code

A frame-wise neural representation for videos, achieveing good compression performance and fast decoding speed.

StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis
Moustafa Meshry, Yixuan Ren, Larry S. Davis, Abhinav Shrivastava
CVPR, 2021
project page / paper / arxiv / code

A staged MMI2I method via style encoder pre-training for simplifed losses and stablized training.

Experiences

  • Research Scientist Intern, Adobe Research, 2023
  • Research Scientist Intern, Adobe Research, 2022
  • Research Intern, ByteDance, 2021

Services

  • Reviewers: CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, TPAMI

Thank Dr. Jon Barron for sharing the awesome source code of his personal page.