Ning Zhu — accepted AI-conference papers

2026

RAM: Recover Any 3D Human Motion in-the-Wild

Sen Jia, Ning Zhu, Jinqin Zhong, Jiale Zhou, Huaping Zhang, Jenq-Neng Hwang +1

CVPR 2026

Recovering 3D human motion from monocular videos in-the-wild remains challenging due to occlusions, rapid movements, and viewpoint variations. To address these challenges, we introduce **Recover-Anyone Module (RAM)**, a unified framework for real-time and accurate 3D human motion reconstruction. RAM

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2025

High-Order Flow Matching: Unified Framework and Sharp Statistical Rates

Maojiang Su, Jerry Yao-Chieh Hu, Yi-Chen Lee, Ning Zhu, Jui-Hui Chung, Shang Wu +3

NeurIPS 2025poster

Flow matching is an emerging generative modeling framework that learns continuous-time dynamics to map noise into data. To enhance expressiveness and sampling efficiency, recent works have explored incorporating high-order trajectory information. Despite the empirical success, a holistic theoretica…

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