title: FlexiTex: Enhancing Texture Generation with Visual Guidance
summary: 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.
paper_authors._id: 66ecd7cf0d9e2833cec195b0
paper_authors.name: DaDong Jiang
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paper_authors.name: Xianghui Yang
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paper_authors.name: Zibo Zhao
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paper_authors.user: cocacola
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paper_authors.name: Sheng Zhang
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paper_authors.name: Jiaao Yu
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paper_authors.name: Zeqiang Lai
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paper_authors.fullname: Zeqiang Lai
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paper_authors.user: ZeqiangLai
paper_authors._id: 66ecd7cf0d9e2833cec195b6
paper_authors.name: Shaoxiong Yang
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paper_authors.fullname: yangshaoxiong
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paper_authors.user: ysx007
paper_authors._id: 66ecd7cf0d9e2833cec195b7
paper_authors.name: Chunchao Guo
paper_authors._id: 66ecd7cf0d9e2833cec195b8
paper_authors.name: Xiaobo Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-20T09:39:56.701Z
paper_authors._id: 64658fd5c2037e5948067a8a
paper_authors.avatarUrl: /avatars/6ead395b2560d42739b52f55a5042376.svg
paper_authors.fullname: Zhou Xiaobo
paper_authors.type: user
paper_authors.user: zouxb009
paper_authors._id: 66ecd7cf0d9e2833cec195b9
paper_authors.name: Zhihui Ke
paper_title: FlexiTex: Enhancing Texture Generation with Visual Guidance
paper_summary: 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.
paper_ai_keywords: diffusion models text-to-image visual guidance Visual Guidance Enhancement module Direction-Aware Adaptation module Janus problem high-frequency details semantically global consistency