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arXiv · 2605.04527

Velox: Learning Representations of 4D Geometry and Appearance

Anagh Malik·Dorian Chan·Xiaoming Zhao·David B. Lindell·Oncel Tuzel·Jen-Hao Rick Chang
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Abstract

We introduce a framework for learning latent representations of 4D objects which are descriptive, faithfully capturing object geometry and appearance; compressive, aiding in downstream efficiency; and accessible, requiring minimal input, i.e., an unstructured dynamic point cloud, to construct. Specifically, Velox trains an encoder to compress spatiotemporal color point clouds into a set of dynamic shape tokens. These tokens are supervised using two complementary decoders: a 4D surface decoder, which models the time-varying surface distribution capturing the geometry; and a Gaussian decoder, which maps the tokens to 3D Gaussians, helping learn appearance. To demonstrate the utility of our representation, we evaluate it across three downstream tasks -- video-to-4D generation, 3D tracking, and cloth simulation via image-to-4D generation -- and observe strong performances in all settings.

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Anagh Malik, Dorian Chan, Xiaoming Zhao, David B. Lindell, Oncel Tuzel, Jen-Hao Rick Chang. 2026-05-06. Velox: Learning Representations of 4D Geometry and Appearance. https://arxiv.org/abs/2605.04527

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