  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. Project website is
available at https://alberthkyhky.github.io/ReCorD/ .
