# ReCorD: Reasoning and Correcting Diffusion for HOI Generation

arXiv: https://arxiv.org/abs/2407.17911 · HTML: https://mlsanity.com/paper/2407.17911/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 72 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Visual Semantic Role Labeling](https://mlsanity.com/paper/1505.04474/) (2015)
- [VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning](https://mlsanity.com/paper/2309.15091/) (2023)
- [Controllable Text-to-Image Generation with GPT-4](https://mlsanity.com/paper/2305.18583/) (2023)
- [A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation](https://mlsanity.com/paper/2310.16656/) (2023)

## Built on by (most cited first, 6 of 6)

- [Egocentric World Model for Photorealistic Hand-Object Interaction Synthesis](https://mlsanity.com/paper/2603.13615/) (2026)
- [DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation](https://mlsanity.com/paper/2503.13957/) (2025)
- [Mask2IV: Interaction-Centric Video Generation via Mask Trajectories](https://mlsanity.com/paper/2510.03135/) (2025)
- [AI-generated Image Quality Assessment in Visual Communication](https://mlsanity.com/paper/2412.15677/) (2024)
- [VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness](https://mlsanity.com/paper/2503.16406/) (2025)
- [Reference-free Human-Object Interaction Editing](https://mlsanity.com/paper/2503.09130/) (2025)

## Abstract (arXiv)

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/ .