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Yang Zhou Hi, I'm a first year PhD student at the University of Toronto, supervised by Prof. Steven Waslander in the Toronto Robotics and AI Lab (TRAILab). Previously, I received my B.Eng. in Computer Science from Hunan University, working with Prof. Jianxin Lin. My research lies at the intersection of multimodal learning, generative models and autonomous systems, with a focus on world models, video generation, and motion planning. Email / Google Scholar / Twitter / GitHub |
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Research |
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DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving
Yang Zhou*, Hao Shao*, Letian Wang, Zhuofan Zong, Hongsheng Li, Steven L. Waslander ICLR, 2026 [Project Page] [Paper] [Code] The first comprehensive benchmark for generative driving world models. |
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VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping
Hao Shao, Shulun Wang, Yang Zhou, Guanglu Song, Dailan He, Zhuofan Zong, Shuo Qin, Yu Liu, Hongsheng Li NeurIPS, 2025 [Project Page] [Paper] [Code] A diffusion-based framework for video face swapping with superior identity preservation and temporal consistency. |
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SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
Yang Zhou*, Hao Shao*, Letian Wang*, Steven L. Waslander, Hongsheng Li, Yu Liu ICLR, 2025 [Paper] [Code] A general and scalable self-supervised pretraining framework for motion prediction, designed to be both model-agnostic and dataset-agnostic. |
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SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
Yang Zhou*, Hao Shao*, Letian Wang, Steven L. Waslander, Hongsheng Li, Yu Liu CVPR, 2024 [Paper] [Code] A scenario-adaptive multi-round refinement strategy that boosts trajectory prediction with minimal extra compute. |
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Template inspired by Jon Barron. |