Mengyi Shan

I am a third year PhD student at the Paul G Allen School of Computer Science in University of Washington where I am affiliated to the Graphics and Imaging Lab (GRAIL) and the Reality Lab. I am co-advised by Steve Seitz, Brian Curless and Ira Kemelmacher-Shlizerman.

I studied at Harvey Mudd College in Claremont, California with double majors in Computer Science and Mathematics and a concentration in linguistics. I was advised by TJ Tsai in the Music Information Retrieval Lab.

Before college, I grew up in Beijing China. I spent seven years at the High School Affiliated to Renmin Univeristy of China (RDFZ), in the first class of Early Development Program (EDP).

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

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Research

I work on generating creative contents with modern technologies in vision and graphics.

Towards Open Domain Text-Driven Synthesis of Multi-Person Motions
Mengyi Shan, Lu Dong, Yutao Han, Yuan Yao, Tao Liu, Ifeoma Nwogu, Guo-Jun Qi, Mitch Hill
Arxiv Preprint, 2024
project page /

We build datasets with multi-person pose and motions, and jointly train to generate natural and diverse group motions of multiple humans from textual descriptions.

OmniMotionGPT: Animal Motion Generation with Limited Data
Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi, Yalin Wang
CVPR, 2024
project page / arXiv / code

We generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset.

Animating Street View
Mengyi Shan, Brian Curless, Ira Kemelmacher-Shlizerman, Steven M. Seitz,
SIGGRAPH Asia, 2023
project page / arXiv / video

We present a system that automatically brings street view imagery to life by populating it with naturally behaving, animated pedestrians and vehicles.

StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, Ira Kemelmacher-Shlizerman
CVPR, 2022
project page / arXiv / code

We introduce a high resolution, 3D-consistent image and shape generation technique trained on single-view RGB data only, and stands on the shoulders of StyleGAN2 for image generation.

Improved Handling of Repeats and Jumps in Audio-Sheet Image Synchronization
Mengyi Shan, TJ Tsai
ISMIR, 2020
project page / arXiv / code

We study the problem of automatically generating piano score following videos given an audio recording and raw sheet music images.

Misc

When I am not coding or reading papers, I enjoy math competition puzzles, detective fictions, board games, Guzheng (Chinese zither), and kpop idols.


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