@inproceedings{
jiang-lin2024record,
title={ReCorD: Reasoning and Correcting Diffusion for {HOI} Generation},
author={Jian-Yu Jiang-Lin and Kang-Yang Huang and Ling Lo and Yi-Ning Huang and Terence Lin and Jhih-Ciang Wu and Hong-Han Shuai and Wen-Huang Cheng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=0CwtY7FtYa}
}
Diffusion models revolutionize image generation by leveraging natural language to guide the creation of multimedia content. Despite significant advancements in such generative models, challenges persist in depicting detailed human-object interactions, especially regarding pose and object placement accuracy. We introduce a training-free method named Reasoning and Correcting Diffusion (ReCorD) to address these challenges. Our model couples Latent Diffusion Models with Visual Language Models to refine the generation process, ensuring precise depictions of HOIs. We propose an interaction-aware reasoning module to improve the interpretation of the interaction, along with an interaction correcting module to refine the output image for more precise HOI generation delicately. Through a meticulous process of pose selection and object positioning, ReCorD achieves superior fidelity in generated images while efficiently reducing computational requirements. We conduct comprehensive experiments on three benchmarks to demonstrate the significant progress in solving text-to-image generation tasks, showcasing ReCorD's ability to render complex interactions accurately by outperforming existing methods in HOI classification score, as well as FID and Verb CLIP-Score.
ReCorD: Reasoning and Correcting Diffusion for HOI Generation
Jian-Yu Jiang-Lin Kang-Yang Huang Ling Lo Yi-Ning Huang Terence Lin Jhih-Ciang Wu Hong-Han Shuai Wen-Huang Cheng