A Scalable Attention-Based Approach for Image-to-3D Texture Mapping

• URL: http://arxiv.org/abs/2509.05131v1
• Date: Fri, 05 Sep 2025 14:18:52 GMT
• Title: A Scalable Attention-Based Approach for Image-to-3D Texture Mapping
• Authors: Arianna Rampini, Kanika Madan, Bruno Roy, AmirHossein Zamani, Derek Cheung,
• Abstract summary: High-quality textures are critical for realistic 3D content creation.<n>Existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image.<n>We propose a transformer-based framework that predicts a 3D texture field directly from a single image and a mesh.
• Score: 3.8476192001237597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image. To address these challenges, we propose a transformer-based framework that predicts a 3D texture field directly from a single image and a mesh, eliminating the need for UV mapping and differentiable rendering, and enabling faster texture generation. Our method integrates a triplane representation with depth-based backprojection losses, enabling efficient training and faster inference. Once trained, it generates high-fidelity textures in a single forward pass, requiring only 0.2s per shape. Extensive qualitative, quantitative, and user preference evaluations demonstrate that our method outperforms state-of-the-art baselines on single-image texture reconstruction in terms of both fidelity to the input image and perceptual quality, highlighting its practicality for scalable, high-quality, and controllable 3D content creation.

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