Geometry-Aware Preference Learning for 3D Texture Generation

• URL: http://arxiv.org/abs/2506.18331v1
• Date: Mon, 23 Jun 2025 06:24:12 GMT
• Title: Geometry-Aware Preference Learning for 3D Texture Generation
• Authors: AmirHossein Zamani, Tianhao Xie, Amir G. Aghdam, Tiberiu Popa, Eugene Belilovsky,
• Abstract summary: We propose an end-to-end differentiable preference learning framework that back-propagates human preferences through the entire 3D generative pipeline.<n>We demonstrate the effectiveness of our framework using four proposed novel geometry-aware reward functions.
• Score: 8.953379216683732
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in 3D generative models have achieved impressive results but 3D contents generated by these models may not align with subjective human preferences or task-specific criteria. Moreover, a core challenge in the 3D texture generation domain remains: most existing approaches rely on repeated calls to 2D text-to-image generative models, which lack an inherent understanding of the 3D structure of the input 3D mesh object. To address this, we propose an end-to-end differentiable preference learning framework that back-propagates human preferences, represented by differentiable reward functions, through the entire 3D generative pipeline, making the process inherently geometry-aware. We demonstrate the effectiveness of our framework using four proposed novel geometry-aware reward functions, offering a more controllable and interpretable pathway for high-quality 3D content creation from natural language.

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