Update README.md

README.md

@@ -41,14 +41,20 @@ Below is a detailed illustration of class definitions with visualized examples f
 If you use our work in your research, please cite our paper:
 
 ```bibtex
-@misc{fang2024humanrefinerbenchmarkingabnormalhuman,
-      title={HumanRefiner: Benchmarking Abnormal Human Generation and Refining with Coarse-to-fine Pose-Reversible Guidance}, 
-      author={Guian Fang and Wenbiao Yan and Yuanfan Guo and Jianhua Han and Zutao Jiang and Hang Xu and Shengcai Liao and Xiaodan Liang},
-      year={2024},
-      eprint={2407.06937},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2407.06937}, 
+@InProceedings{10.1007/978-3-031-73411-3_12,
+author="Guian Fang, Wenbiao Yan, Yuanfan Guo, Jianhua Han, Zutao Jiang, Hang Xu, Shengcai Liao, Xiaodan Liang",
+editor="Leonardis, Ale{\v{s}}
+and Ricci, Elisa
+and Roth, Stefan
+and Russakovsky, Olga
+and Sattler, Torsten
+and Varol, G{\"u}l",
+title="HumanRefiner: Benchmarking Abnormal Human Generation and Refining with Coarse-to-fine Pose-Reversible Guidance",
+booktitle="Computer Vision -- ECCV 2024",
+year="2025",
+publisher="Springer Nature Switzerland",
+abstract="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 AbHuman dataset is available at https://github.com/Enderfga/HumanRefiner.",
+isbn="978-3-031-73411-3"
 }
 ```