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
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper5
- MaterialMVP: Illumination-Invariant Material Generation via Multi-View PBR DiffusionZebin He, Mingxin Yang, Shuhui Yang, Yixuan Tang 等ICCV 2025 · 被引用 3 次
- GaussianGrow: Geometry-aware Gaussian Growing from 3D Point Clouds with Text GuidanceWeiqi Zhang, Junsheng Zhou, Haotian Geng, Kanle Shi 等CVPR 2026 · 被引用 2 次
- GAP: Gaussianize Any Point Clouds with Text GuidanceWeiqi Zhang, Junsheng Zhou, Haotian Geng, Wenyuan Zhang 等ICCV 2025 · 被引用 2 次
- CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference CustomizationWeilin Chen, Jiahao Rao, Wenhao Wang, Xinyang Li 等CVPR 2026 · 被引用 1 次
- MV2UV: Generating High-quality UV Texture Maps with Multiview PromptsZheng Zhang, Qinchuan Zhang, Yuteng Ye, Zhi Chen 等CVPR 2026
它引用的顶会 Paper24
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 被引用 11,743 次
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
- Adding Conditional Control to Text-to-Image Diffusion ModelsLvmin Zhang, Anyi Rao, Maneesh AgrawalaICCV 2023 · 被引用 6,759 次
相关 Paper
- HDLayout: Hierarchical and Directional Layout Planning for Arbitrary Shaped Visual Text GenerationTonghui Feng, Chunsheng Yan, Qianru Wang, Jiangtao Cui 等AAAI 2025
- Text2Tex: Text-driven Texture Synthesis via Diffusion ModelsDave Zhenyu Chen, Yawar Siddiqui, Hsin-Ying Lee, Sergey Tulyakov 等ICCV 2023 · 被引用 262 次
- FreeInpaint: Tuning-free Prompt Alignment and Visual Rationality Enhancement in Image InpaintingChao Gong, Dong Li, Yingwei Pan, Jingjing Chen 等AAAI 2026
- TEXTure: Text-Guided Texturing of 3D ShapesElad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes 等SIGGRAPH 2023 · 被引用 196 次
- TexSliders: Diffusion-Based Texture Editing in CLIP SpaceJulia Guerrero-Viu, Milos Hasan, Arthur Roullier, Midhun Harikumar 等SIGGRAPH 2024 · 被引用 18 次
