DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation

Yuang Peng, cyx, Haomiao Tang, Zekun Qi, Runpei Dong, baijing, Chunrui Han, Zheng Ge, Xiangyu Zhang, Shu-Tao Xia

DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation: 57 upvotes on Hugging Face Daily Papers, #1 of 24 papers on 2024-06-25. Day-by-day upvote history.

Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content. 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 automated by advanced multimodal GPT models. 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.

Paper page on Hugging Face · arXiv

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