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Beijing Institute for General Artificial Intelligence

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arXiv Nov 4, 2026
Humanoid soccer stops juggling separate controllers: SoccerBot unifies four skills on a human motion prior and transfers zero-shot to a real robot
Humanoid soccer robots have either covered only a few skills or skipped motion priors entirely and moved unnaturally. SoccerBot distills large-scale human soccer motion-capture data into a structured latent motion prior, letting a single skill-conditioned policy learn moving, dribbling, trapping and kicking in one latent space. On the Unitree G1, the policy transfers zero-shot to hardware, with simulation success rates of 97.04% for kicking and 89.88% for dribbling, while real-robot kicking direction error rises from 4.09 to 10.41 degrees.
Humanoid soccer robots have either covered only a few skills or skipped motion priors entirely and moved unnaturally. SoccerBot distills large-scale human soccer motion-capture data into a structured latent motion prior, letting a single skill-conditioned policy learn moving, dribbling, trapping and kicking in one latent space. On the Unitree G1, the policy transfers zero-shot to hardware, with simulation success rates of 97.04% for kicking and 89.88% for dribbling, while real-robot kicking direction error rises from 4.09 to 10.41 degrees.
Shanghai Jiao Tong University Beijing Institute for General Artificial Intelligence Peking University
Haoyu Shao（First author）
SoccerBot Latent Motion Prior Humanoid Soccer Hierarchical Reinforcement Learning Sim-to-Real Transfer
arXiv Oct 14, 2026
Video attention often confuses different scene elements: GAD distills geometry-model attention, lifting Wan2.2 5B cross-frame matching by at least 30%
Video diffusion models lose scene geometry when the camera moves or objects move, because cross-frame attention often spreads its response over several regions instead of locking onto the same scene element. GAD extracts correspondence targets from the attention of a frozen geometry foundation model, VGGT-, on clean videos, and routes supervision by noise timestep to the layer where scene structure is forming, training video attention with pairwise and trackwise objectives. On 15-second long-range generation with RealEstate10K and DL3DV-10K, adding GAD to Wan2.2 5B raises cross-frame matching hit rate by at least 30% relative on both datasets, with no extra inference modules or computation.
Video diffusion models lose scene geometry when the camera moves or objects move, because cross-frame attention often spreads its response over several regions instead of locking onto the same scene element. GAD extracts correspondence targets from the attention of a frozen geometry foundation model, VGGT-, on clean videos, and routes supervision by noise timestep to the layer where scene structure is forming, training video attention with pairwise and trackwise objectives. On 15-second long-range generation with RealEstate10K and DL3DV-10K, adding GAD to Wan2.2 5B raises cross-frame matching hit rate by at least 30% relative on both datasets, with no extra inference modules or computation.
Alibaba Group (China)Beijing Institute for General Artificial Intelligence Tsinghua University
Junfeng Ni（First author）
GAD (Geometry Attention Distillation)Attention Distillation Camera-Controlled Video Generation VGGT Video Diffusion Model

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