This note describes a multi-view video dataset of a deformable object driven by a measured input, with the measured pose of every camera, and a first 3D result obtained on it with LaGSplat. LaGSplat learns from video a low-dimensional latent state that is both the generalized coordinate of a learned dissipative Lagrangian and the conditioning variable of a Gaussian Splatting decoder; fitting the decoder in 3D calls for synchronized multi-view footage, which the original paper only had from synthetic renderings. Here a robot arm moves a flexible strip by oscillating along a one-dimensional path, and four iPhones, three fixed and one handheld, film it for ten minutes. The videos are synchronized, the cameras are posed from the scene, and the position of the gripper is given per frame as the input of the system. LaGSplat fitted on this data with the gripper acceleration as a forcing term renders held-out views at 26 to 33 dB PSNR and runs as a real-time interactive demo in a web browser. Dataset, checkpoints and training code are public.
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