# Chain-of-Cooking:Cooking Process Visualization via Bidirectional Chain-of-Thought Guidance

arXiv: https://arxiv.org/abs/2507.21529 · HTML: https://mlsanity.com/paper/2507.21529/

## Key facts

- Cited by 3 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 38 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step](https://mlsanity.com/paper/2501.13926/) (2025)
- [MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation](https://mlsanity.com/paper/2502.01572/) (2025)
- [ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation](https://mlsanity.com/paper/2502.09411/) (2025)
- [CookingDiffusion: Cooking Procedural Image Generation with Stable Diffusion](https://mlsanity.com/paper/2501.09042/) (2025)
- [FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation](https://mlsanity.com/paper/2312.03540/) (2023)

## Built on by (most cited first, 3 of 3)

- [SMRABooth: Subject and Motion Representation Alignment for Customized Video Generation](https://mlsanity.com/paper/2512.12193/) (2025)
- [PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents](https://mlsanity.com/paper/2605.10341/) (2026)
- [Disco-LoRA: Disentangled Composition of Content, Style, and Motion for Multi-concept Video Customization](https://mlsanity.com/paper/2606.26668/) (2026)

## Abstract (arXiv)

Cooking process visualization is a promising task in the intersection of image generation and food analysis, which aims to generate an image for each cooking step of a recipe. However, most existing works focus on generating images of finished foods based on the given recipes, and face two challenges to visualize the cooking process. First, the appearance of ingredients changes variously across cooking steps, it is difficult to generate the correct appearances of foods that match the textual description, leading to semantic inconsistency. Second, the current step might depend on the operations of previous step, it is crucial to maintain the contextual coherence of images in sequential order. In this work, we present a cooking process visualization model, called Chain-of-Cooking. Specifically, to generate correct appearances of ingredients, we present a Dynamic Patch Selection Module to retrieve previously generated image patches as references, which are most related to current textual contents. Furthermore, to enhance the coherence and keep the rational order of generated images, we propose a Semantic Evolution Module and a Bidirectional Chain-of-Thought (CoT) Guidance. To better utilize the semantics of previous texts, the Semantic Evolution Module establishes the semantical association between latent prompts and current cooking step, and merges it with the latent features. Then the CoT Guidance updates the merged features to guide the current cooking step remain coherent with the previous step. Moreover, we construct a dataset named CookViz, consisting of intermediate image-text pairs for the cooking process. Quantitative and qualitative experiments show that our method outperforms existing methods in generating coherent and semantic consistent cooking process.