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arXiv · 2412.15618

3D Shape Tokenization via Latent Flow Matching

Jen-Hao Rick Chang·Yuyang Wang·Miguel Angel Bautista Martin·Jiatao Gu·Xiaoming Zhao·Josh Susskind·Oncel Tuzel
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Abstract

We introduce a latent 3D representation that models 3D surfaces as probability density functions in 3D, i.e., p(x,y,z), with flow-matching. Our representation is specifically designed for consumption by machine learning models, offering continuity and compactness by construction while requiring only point clouds and minimal data preprocessing. Despite being a data-driven method, our use of flow matching in the 3D space enables interesting geometry properties, including the capabilities to perform zero-shot estimation of surface normal and deformation field. We evaluate with several machine learning tasks, including 3D-CLIP, unconditional generative models, single-image conditioned generative model, and intersection-point estimation. Across all experiments, our models achieve competitive performance to existing baselines, while requiring less preprocessing and auxiliary information from training data.

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Jen-Hao Rick Chang, Yuyang Wang, Miguel Angel Bautista Martin, Jiatao Gu, Xiaoming Zhao, Josh Susskind, Oncel Tuzel. 2024-12-20. 3D Shape Tokenization via Latent Flow Matching. https://arxiv.org/abs/2412.15618

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