Agent-to-Sim: Learning Interactive Behavior Models from Casual Longitudinal Videos
Gengshan Yang Andrea Bajcsy Shunsuke Saito Angjoo Kanazawa
We present Agent-to-Sim (ATS), a framework for learning interactive behavior models of 3D agents from casual longitudinal video collections. Different from prior works that rely on marker-based tracking and multiview cameras, ATS learns natural behaviors of animal agents non-invasively through video observations recorded over a long time-span (e.g. a month) in a single environment.
Modeling 3D behavior of an agent requires persistent 3D tracking (e.g., knowing which point corresponds to which) over a long time period. To obtain such data, we develop a coarse-to-fine registration method that tracks the agent and the camera over time through a canonical 3D space, resulting in a complete and persistent spacetime 4D representation. We then train a generative model of agent behaviors using paired data of perception and motion of an agent queried from the 4D reconstruction. ATS enables real-to-sim transfer from video recordings of an agent to an interactive behavior simulator. We demonstrate results on animals given monocular RGBD videos captured by a smartphone. Project page: gengshan-y.github.io/agent2sim-www.
dynamic 3d reconstruction; multi-video registration; motion generation
From monocular videos collected across a long time horizon (e.g., 1 month), we learn interactive behavior models of an agent grounded in 3D.