This work studies how to learn anticipation of how objects move during human-object interaction from ordinary monocular videos of human-object interaction, and shows that it can be efficiently re-purpose video priors into explicit geometric forecasts for embodied intelligence.
This work studies how to learn anticipation of how objects move during human-object interaction from ordinary monocular videos of human-object interaction, and shows that it can efficiently re-purpose video priors into explicit geometric forecasts for embodied intelligence.
Experiments show that RESELF outperforms state-of-the-art methods designed for the individual tasks across depth estimation, camera tracking, and full-body motion estimation.
Kai Guan, Minchao Jiang, Ruichen WangLi et al.· 0 citations
GaussianWAM is proposed, a training-time representation-enhancement framework that organizes geometric and semantic supervision through a 3D Gaussian field and improves performance on standard LIBERO and shows positive transfer trends on RoboTwin and real-world manipulation.
Zi-Jian Zhang, Yu-Qing Jiang, Wei-Tao Zhou et al.· 3 citations
A biologically inspired framework for learning object-centric visual representations in an unsupervised manner, suggesting that motion-induced object representations offer a compelling alternative to existing vision foundation models, capturing a crucial but overlooked level of abstraction: the visual instance.
Hao Liang, Xiao-Hui Wang, Zhi-Chao Li et al.· Neural Information Processin...· 0 citations
RididBench is introduced, a simulator-grounded benchmark that compares a generated continuation with a reference rollout from the same initial frame and motion description, with per-frame masks, depth, 6-DoF trajectories, and contacts available for scoring.
ABot-Recon is presented, a simple streaming model that caches KV features from only the preceding 11 frames and predicts a point map in the current camera coordinate system together with an adjacent-frame relative pose that remains equivariant under changes of reference frame.
Jiarong Han, Jincheng Xiong, Yu-Zhou Liu et al.· 2 citations· ⚡1
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