Zijie Ye

I'm a 5th-year PhD candidate at Department of Computer Science and Technology (DCST), Tsinghua University. My advisor is Prof. Jia Jia. I received my B.Eng. degree from DCST, Tsinghua University in 2020.

I am working on 3D Human Motion Generation & Processing. I am actively looking for full-time positions. Please send an email if you are interested.

Email / Google Scholar / Github

Selected Publications

	Skinned Motion Retargeting with Dense Geometric Interaction Perception Zijie Ye, Jia-Wei Liu, Jia Jia, Shikun Sun, Mike Zheng Shou NeurIPS, Spotlight, 2024 PDF / Project Page / Code We introduce MeshRet, a pioneering solution that facilitates geometric interaction-aware motion retargeting across varied mesh topologies in a single pass. We present the SCS and the novel DMI field to guide the training of MeshRet, effectively encapsulating both contact and non-contact interaction semantics.
	Semantics2Hands: Transferring Hand Motion Semantics between Avatars Zijie Ye, Jia Jia, Junliang Xing ACM MM Oral, Brave New Idead Award 2023 PDF / Project Page / arXiv / Code Given a source hand motion and a target hand model, our method can retarget realistic hand motions with high fidelity to the target while preserving intricate motion semantics.
	Salient Co-Speech Gesture Synthesizing with Discrete Motion Representation Zijie Ye, Jia Jia, Haozhe Wu, Shuo Huang, Shikun Sun, Junliang Xing ICASSP, 2023 PDF / Code We propose to synthesize co-speech gestures using discrete motion representation (DMR). By learning a DMR space for gesture motions and modeling the distribution of DMR, our approach generates more high-quality salient motions.
	Human motion modeling with deep learning: A survey Zijie Ye, Haozhe Wu, Jia Jia AI Open, 2022 Paper We present a comprehensive survey of recent human motion modeling researches with deep learning techniques.
	ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit Zijie Ye, Haozhe Wu, Jia Jia, Yaohua Bu, Wei Chen, Fanbo Meng, Yanfeng Wang ACM MM Oral, 2020 PDF / arXiv We propose to formalize the human choreography knowledge by defining CAU and introduce it into music-to-dance synthesis. We propose a two-stage framework ChoreoNet to implement the music-CAU-skeleton mapping. Experiments demonstrate the effectiveness of our meth

Misc

Gold Medal, China Chemistry Olympiad, 2015
Tsinghua University Freshman Scholarship, 2016
Excellent Undergraduate of Dept. of CST, 2020
84 Innovation Scholarship of Dept. of CST, 2021

Based on Jon Barron's website.