T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation

• URL: http://arxiv.org/abs/2307.06350v2
• Date: Mon, 30 Oct 2023 11:42:42 GMT
• Title: T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
• Authors: Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, Xihui Liu
• Abstract summary: T2I-CompBench is a comprehensive benchmark for open-world compositional text-to-image generation. We 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.
• Score: 62.71574695256264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 and explore the potential and limitations of multimodal LLMs for evaluation. 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/.

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