FlexiTex: Enhancing Texture Generation via Visual Guidance
DaDong Jiang, Xianghui Yang, Zibo Zhao, Sheng Zhang, Jiaao Yu, Zeqiang Lai, Shaoxiong Yang, Chunchao Guo, Xiaobo Zhou, Zhihui Ke

TL;DR
FlexiTex improves texture generation by integrating visual guidance and direction-aware adaptation to produce clearer, more consistent textures from limited textual prompts, advancing the quality of generative models.
Contribution
The paper introduces FlexiTex, a novel method that enhances texture generation by embedding visual guidance and adaptive direction prompts to improve detail and consistency.
Findings
Produces higher quality textures both quantitatively and qualitatively.
Effectively reduces ambiguity and preserves high-frequency details.
Maintains global semantic consistency across different camera poses.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAugmented Reality Applications
MethodsDiffusion
