DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation
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arXiv
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| Format: | Preprint |
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2024
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| author | Peng, Yuang Cui, Yuxin Tang, Haomiao Qi, Zekun Dong, Runpei Bai, Jing Han, Chunrui Ge, Zheng Zhang, Xiangyu Xia, Shu-Tao |
| author_facet | Peng, Yuang Cui, Yuxin Tang, Haomiao Qi, Zekun Dong, Runpei Bai, Jing Han, Chunrui Ge, Zheng Zhang, Xiangyu Xia, Shu-Tao |
| contents | Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive ability to creatively generate personalized content across various contexts. However, current evaluations either are automated but misalign with humans or require human evaluations that are time-consuming and expensive. In this work, we present DreamBench++, a human-aligned benchmark that advanced multimodal GPT models automate. Specifically, we systematically design the prompts to let GPT be both human-aligned and self-aligned, empowered with task reinforcement. Further, we construct a comprehensive dataset comprising diverse images and prompts. By benchmarking 7 modern generative models, we demonstrate that DreamBench++ results in significantly more human-aligned evaluation, helping boost the community with innovative findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation Peng, Yuang Cui, Yuxin Tang, Haomiao Qi, Zekun Dong, Runpei Bai, Jing Han, Chunrui Ge, Zheng Zhang, Xiangyu Xia, Shu-Tao Computer Vision and Pattern Recognition Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive ability to creatively generate personalized content across various contexts. However, current evaluations either are automated but misalign with humans or require human evaluations that are time-consuming and expensive. In this work, we present DreamBench++, a human-aligned benchmark that advanced multimodal GPT models automate. Specifically, we systematically design the prompts to let GPT be both human-aligned and self-aligned, empowered with task reinforcement. Further, we construct a comprehensive dataset comprising diverse images and prompts. By benchmarking 7 modern generative models, we demonstrate that DreamBench++ results in significantly more human-aligned evaluation, helping boost the community with innovative findings. |
| title | DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.16855 |