T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional
  Text-to-image Generation
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/.
64b0a50a2cf7a79f71465e4a
Kaiyi Huang
admin_assigned
2024-12-06T17:50:24.946Z
637cba13b8e573d75be96ea6
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Nina
user
NinaKarine
64b0a50a2cf7a79f71465e4b
Kaiyue Sun
claimed_verified
2023-07-24T07:44:22.927Z
63640ce5ff4b318d1b7b6f5c
/avatars/9336d1dab1491f3c8e84b1ea287eb891.svg
Kaiyue Sun
user
Kaiyue
64b0a50a2cf7a79f71465e4c
Enze Xie
admin_assigned
2023-07-17T11:21:30.037Z
616961bdd3f656f79ad18ec1
https://cdn-avatars.huggingface.co/v1/production/uploads/616961bdd3f656f79ad18ec1/NYpxas378lDevRuUDepJJ.jpeg
xieenze
user
xieenze
64b0a50a2cf7a79f71465e4d
Zhenguo Li
64b0a50a2cf7a79f71465e4e
Xihui Liu
claimed_verified
2024-06-03T18:30:44.803Z
65d5ec74cd05bc1eaa125040
/avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
Xihui Liu
user
XihuiLiu
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional
  Text-to-image Generation
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/.
T2I-CompBench is a benchmark for assessing compositional text-to-image generation, introducing evaluation metrics and GORS, a reward-driven fine-tuning method.
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