Patch-based Representation and Learning for Efficient Deformation Modeling

• URL: http://arxiv.org/abs/2601.05035v1
• Date: Thu, 08 Jan 2026 15:43:57 GMT
• Title: Patch-based Representation and Learning for Efficient Deformation Modeling
• Authors: Ruochen Chen, Thuy Tran, Shaifali Parashar,
• Abstract summary: We present a patch-based representation of surfaces, PolyFit, which is obtained by fitting jet functions locally on surface patches.<n>Using PolyFit, the surfaces can be efficiently deformed by updating a compact set of jet coefficients.<n>We train a self-supervised, mesh- and garment-agnostic model that generalizes across resolutions and garment types, delivering up to an order-of-magnitude faster inference than strong baselines.
• Score: 4.103736487425219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a patch-based representation of surfaces, PolyFit, which is obtained by fitting jet functions locally on surface patches. Such a representation can be learned efficiently in a supervised fashion from both analytic functions and real data. Once learned, it can be generalized to various types of surfaces. Using PolyFit, the surfaces can be efficiently deformed by updating a compact set of jet coefficients rather than optimizing per-vertex degrees of freedom for many downstream tasks in computer vision and graphics. We demonstrate the capabilities of our proposed methodologies with two applications: 1) Shape-from-template (SfT): where the goal is to deform the input 3D template of an object as seen in image/video. Using PolyFit, we adopt test-time optimization that delivers competitive accuracy while being markedly faster than offline physics-based solvers, and outperforms recent physics-guided neural simulators in accuracy at modest additional runtime. 2) Garment draping. We train a self-supervised, mesh- and garment-agnostic model that generalizes across resolutions and garment types, delivering up to an order-of-magnitude faster inference than strong baselines.

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