Locally Adaptive Neural 3D Morphable Models

• URL: http://arxiv.org/abs/2401.02937v1
• Date: Fri, 5 Jan 2024 18:28:51 GMT
• Title: Locally Adaptive Neural 3D Morphable Models
• Authors: Michail Tarasiou, Rolandos Alexandros Potamias, Eimear O'Sullivan, Stylianos Ploumpis, Stefanos Zafeiriou
• Abstract summary: We present the Locally Adaptive Morphable Model (LAMM), a framework for learning to generate and manipulate 3D meshes. A very efficient computational graph allows our network to train with only a fraction of the memory required by previous methods. We further leverage local geometry control as a primitive for higher level editing operations and present a set of derivative capabilities.
• Score: 38.38400553022714
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present the Locally Adaptive Morphable Model (LAMM), a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes. We train our architecture following a simple self-supervised training scheme in which input displacements over a set of sparse control vertices are used to overwrite the encoded geometry in order to transform one training sample into another. During inference, our model produces a dense output that adheres locally to the specified sparse geometry while maintaining the overall appearance of the encoded object. This approach results in state-of-the-art performance in both disentangling manipulated geometry and 3D mesh reconstruction. To the best of our knowledge LAMM is the first end-to-end framework that enables direct local control of 3D vertex geometry in a single forward pass. A very efficient computational graph allows our network to train with only a fraction of the memory required by previous methods and run faster during inference, generating 12k vertex meshes at $>$60fps on a single CPU thread. We further leverage local geometry control as a primitive for higher level editing operations and present a set of derivative capabilities such as swapping and sampling object parts. Code and pretrained models can be found at https://github.com/michaeltrs/LAMM.

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