Velox：学习4D几何与外观的表示

research area Computer Visionconference CVPR

content type paperpublished May 2026

Velox: Learning Representations of 4D Geometry and Appearance

AuthorsAnagh Malik†, Dorian Chan, Xiaoming Zhao, David B. Lindell†, Oncel Tuzel, Jen-Hao Rick Chang

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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.

• † University of Toronto

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链接抓取：https://arxiv.org/abs/2605.04527

Computer Science > Computer Vision and Pattern Recognition

arXiv:2605.04527 (cs)

[Submitted on 6 May 2026]

Title:Velox: Learning Representations of 4D Geometry and Appearance

Authors:Anagh Malik, Dorian Chan, Xiaoming Zhao, David B. Lindell, Oncel Tuzel, Jen-Hao Rick Chang

View a PDF of the paper titled Velox: Learning Representations of 4D Geometry and Appearance, by Anagh Malik and 5 other authors

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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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From: Anagh Malik [view email]
[v1] Wed, 6 May 2026 06:12:19 UTC (8,753 KB)

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