Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld interfaces often provide limited intuitive force feedback, making contact-rich demonstration collection challenging for human operators. To address this, we present FOMI, a...
Yi-Shu Shen, Yi-Ming Luo, Rui-Lin Wang et al.· 0 citations
As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requires both preserving previously observed content and ensuring coherent world evolution over time. World memory is a critical capability to evaluate alongside visual fidel...
Nan-Yi Zhou, Jianxiong Gao, Qiuyu Wang et al.· 0 citations
This work presents Zero-WAM, a causal video-action model that executes unseen tasks by following in-context human video guidance, and proposes an automatic pipeline that converts task-sampled robot trajectories into semantically matched human videos, yielding HumanGen, a dataset of 74.2K human-robot ICL pairs across 8....
Jia-Min Zhou, Qihang Zhang, Gangwei Xu et al.· 15 citations
CameraAnything is introduced, the first unified framework for camera controlled video editing that enables joint control of both intrinsic and extrinsic camera parameters, and a scalable synthetic pipeline is developed that constructs diverse dynamic scenes through structured multi-camera recording and generates synchr...
Yixuan Li, Yanhong Zeng, K. Cheng et al.· arXiv.org· 1 citation
LingBot-VA 2.0 is presented, a video-action foundation model built from the ground up for embodiment, which introduces a semantic visual-action tokenizer, which aligns visual representations with both semantics and actions, improving instruction following and action precision in subsequent policy learning.
Qihang Zhang, Lin Li, Luyao Zhang et al.· arXiv.org· 11 citations· ⚡3
The integration of an agentic harness within the domain of world modeling is pioneered, wherein a pilot agent is tasked with planning and executing character behaviors, while a director agent is responsible for synthesizing novel environmental elements as the scene progresses.
LingBot-Video is presented, a DiT-based video pretraining paradigm specifically tailored for embodied intelligence, and is contributed as the inaugural large-scale, open-source MoE video foundation model to the community, in a pioneering effort to bridge digital creativity and physical actuation.
Shuailei Ma, Jiaqi Liao, Xinyang Wang et al.· 7 citations· ⚡1
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