title: T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional
    Text-to-image Generation
summary: Despite the stunning ability to generate high-quality images by recent
  text-to-image models, current approaches often struggle to effectively compose
  objects with different attributes and relationships into a complex and coherent
  scene. We propose T2I-CompBench, a comprehensive benchmark for open-world
  compositional text-to-image generation, consisting of 6,000 compositional text
  prompts from 3 categories (attribute binding, object relationships, and complex
  compositions) and 6 sub-categories (color binding, shape binding, texture
  binding, spatial relationships, non-spatial relationships, and complex
  compositions). We further propose several evaluation metrics specifically
  designed to evaluate compositional text-to-image generation. We introduce a new
  approach, Generative mOdel fine-tuning with Reward-driven Sample selection
  (GORS), to boost the compositional text-to-image generation abilities of
  pretrained text-to-image models. Extensive experiments and evaluations are
  conducted to benchmark previous methods on T2I-CompBench, and to validate the
  effectiveness of our proposed evaluation metrics and GORS approach. Project
  page is available at https://karine-h.github.io/T2I-CompBench/.
paper_authors._id: 64b0a50a2cf7a79f71465e4a
paper_authors.name: Kaiyi Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-06T17:50:24.946Z
paper_authors._id: 637cba13b8e573d75be96ea6
paper_authors.avatarUrl: /avatars/5eca230e63d66947b2a05c1ff964a96c.svg
paper_authors.fullname: Nina
paper_authors.type: user
paper_authors.user: NinaKarine
paper_authors._id: 64b0a50a2cf7a79f71465e4b
paper_authors.name: Kaiyue Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-24T07:44:22.927Z
paper_authors._id: 63640ce5ff4b318d1b7b6f5c
paper_authors.avatarUrl: /avatars/9336d1dab1491f3c8e84b1ea287eb891.svg
paper_authors.fullname: Kaiyue Sun
paper_authors.type: user
paper_authors.user: Kaiyue
paper_authors._id: 64b0a50a2cf7a79f71465e4c
paper_authors.name: Enze Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T11:21:30.037Z
paper_authors._id: 616961bdd3f656f79ad18ec1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/616961bdd3f656f79ad18ec1/NYpxas378lDevRuUDepJJ.jpeg
paper_authors.fullname: xieenze
paper_authors.type: user
paper_authors.user: xieenze
paper_authors._id: 64b0a50a2cf7a79f71465e4d
paper_authors.name: Zhenguo Li
paper_authors._id: 64b0a50a2cf7a79f71465e4e
paper_authors.name: Xihui Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-03T18:30:44.803Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_title: T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional
    Text-to-image Generation
paper_summary: Despite the stunning ability to generate high-quality images by recent
  text-to-image models, current approaches often struggle to effectively compose
  objects with different attributes and relationships into a complex and coherent
  scene. We propose T2I-CompBench, a comprehensive benchmark for open-world
  compositional text-to-image generation, consisting of 6,000 compositional text
  prompts from 3 categories (attribute binding, object relationships, and complex
  compositions) and 6 sub-categories (color binding, shape binding, texture
  binding, spatial relationships, non-spatial relationships, and complex
  compositions). We further propose several evaluation metrics specifically
  designed to evaluate compositional text-to-image generation. We introduce a new
  approach, Generative mOdel fine-tuning with Reward-driven Sample selection
  (GORS), to boost the compositional text-to-image generation abilities of
  pretrained text-to-image models. Extensive experiments and evaluations are
  conducted to benchmark previous methods on T2I-CompBench, and to validate the
  effectiveness of our proposed evaluation metrics and GORS approach. Project
  page is available at https://karine-h.github.io/T2I-CompBench/.
paper_ai_summary: T2I-CompBench is a benchmark for assessing compositional text-to-image generation, introducing evaluation metrics and GORS, a reward-driven fine-tuning method.
paper_ai_keywords: text-to-image models Generative mOdel fine-tuning with Reward-driven Sample selection GORS compositional text-to-image generation attribute binding object relationships complex compositions color binding shape binding texture binding spatial relationships non-spatial relationships