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FlexiTex: Enhancing Texture Generation via Visual Guidance
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Recent texture generation methods achieve impressive results due to the powerful generative prior they leverage from large-scale text-to-image diffusion models. However, abstract textual prompts are limited in providing global textural or shape information, which results in the texture generation methods producing blurry or inconsistent patterns. To tackle this, we present FlexiTex, embedding rich information via visual guidance to generate a high-quality texture. The core of FlexiTex is the Visual Guidance Enhancement module, which incorporates more specific information from visual guidance to reduce ambiguity in the text prompt and preserve high-frequency details. To further enhance the visual guidance, we introduce a Direction-Aware Adaptation module that automatically designs direction prompts based on different camera poses, avoiding the Janus problem and maintaining semantically global consistency. Benefiting from the visual guidance, FlexiTex produces quantitatively and qualitatively sound results, demonstrating its potential to advance texture generation for real-world applications.
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Cited by 2 Pith papers
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Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models
A video-diffusion pipeline conditioned on geometry maps, followed by component-wise UV inpainting, produces more coherent and seam-free textures for 3D meshes than Text2Tex, Paint3D, and Meshy in the reported tests.
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FlexPainter: Flexible and Multi-View Consistent Texture Generation
FlexPainter combines multi-view grid generation, UV-space view synchronization with a learned weighting network, and multi-modal embedding control to generate consistent, high-resolution textures from text and image prompts.
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