  Text-to-image diffusion models have significantly advanced in conditional
image generation. However, these models usually struggle with accurately
rendering images featuring humans, resulting in distorted limbs and other
anomalies. This issue primarily stems from the insufficient recognition and
evaluation of limb qualities in diffusion models. To address this issue, we
introduce AbHuman, the first large-scale synthesized human benchmark focusing
on anatomical anomalies. This benchmark consists of 56K synthesized human
images, each annotated with detailed, bounding-box level labels identifying
147K human anomalies in 18 different categories. Based on this, the recognition
of human anomalies can be established, which in turn enhances image generation
through traditional techniques such as negative prompting and guidance. To
further boost the improvement, we propose HumanRefiner, a novel plug-and-play
approach for the coarse-to-fine refinement of human anomalies in text-to-image
generation. Specifically, HumanRefiner utilizes a self-diagnostic procedure to
detect and correct issues related to both coarse-grained abnormal human poses
and fine-grained anomaly levels, facilitating pose-reversible diffusion
generation. Experimental results on the AbHuman benchmark demonstrate that
HumanRefiner significantly reduces generative discrepancies, achieving a 2.9x
improvement in limb quality compared to the state-of-the-art open-source
generator SDXL and a 1.4x improvement over DALL-E 3 in human evaluations. Our
data and code are available at https://github.com/Enderfga/HumanRefiner.
