Recovering articulated 4D representations of animals from monocular videos remains challenging due to the large diversity of quadruped morphologies and lack of animal 4D supervision data. Existing learning-based reconstruction methods operate on individual images and rely on synthetic or model-fitted 3D supervision, wh...
Xu Hu, Francesco Palandra, Shang-Zhe Wu et al.· 0 citations
Kirin is introduced, a framework that reconstructs motion from video, learns motion priors at scale, and generates realistic motion that can be directly applied to animated assets that establishes a new foundation for large-scale, text and image conditioned animal motion generation and animation.
Brian Nlong Zhao, Zhuo-Yang Pan, J. Rehg et al.· 1 citation
This paper reconsiders intelligence from a vision-centered perspective and examines whether intelligence emerging from visual experience and learning may provide a pathway toward AGI. In the language domain, beginning with the introduction of the Transformer architecture, the GPT series has demonstrated transfer to uns...
Hirokatsu Kataoka, Yoshihiro Fukuhara, Yonglong Tian et al.· 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.
Swarnim Jain, Shang-Zhe Wu· 5 citations
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