Dual-Diffusion Grid Texture Generation Based on Multimodal Geometric Perception
Abstract
Abstract Existing 3D texture generation methods typically map 2D image textures onto 3D mesh surfaces.However,this approach often suffers from issues such as UV mapping distortion, limited texture resolution, and difficulty in maintaining geometric consistency, all of which restrict the quality of the generated 3D textures.To address these issues, this paper proposes a dual-diffusion mesh texture generation method based on multimodal geometric awareness. By deeply integrating 2D images, textual descriptions, and 3D geometric information, the proposed method accurately captures the detailed features of complex surfaces, enabling the generation of texture maps with high resolution, visual realism, and geometric consistency.This design effectively mitigates geometric distortion by UV mapping and produces detailed textures that are highly consistent with the mesh?s geometric structure.Experimental results demonstrate that, compared to existing mainstream methods, the proposed method achieves significant improvements in both texture quality (14.13% improvement in FID and 28.53% improvement in KID) and diversity, enabling the generation of 3D models with more photorealistic visual effects and finer details.
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