# VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model

**Authors:** Jinze Yang, Haoran Wang, Zining Zhu, Chenglong Liu, Meng Wymond Wu, Zeke Xie, Zhong Ji, Jungong Han, Mingming Sun  
**arXiv:** [2406.01059](https://arxiv.org/abs/2406.01059)  
**Date:** 2024-06-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2406.01059v2/

## Key points

- Authors address the challenge of image outpainting, which involves extrapolating surrounding parts of an image based on its central contents.
- Previous works in image outpainting lack versatility and customization options, limiting practical applicability in various scenarios.
- The authors propose a novel image outpainting framework that allows for customized results tailored to users' specific requirements by leveraging a Multimodal Large Language Model (MLLM).
- The key innovation is incorporating text prompts into the model training process to customize outpainting outcomes effectively.
- A specialized Cross-Attention module called Center-Total-Surrounding (CTS) enhances interactions between specific spatial regions of an image and corresponding text prompts.
- The proposed model is resource-efficient as it only requires slight fine-tuning on an off-the-shelf stable diffusion (SD) model instead of extensive training from scratch.
- Experimental results on three datasets show that the model significantly outperforms state-of-the-art methods in terms of outpainting quality.
- The model's customizable capabilities are showcased through outpainting results, contributing a robust solution to image outpainting challenges with advanced language modeling techniques.

## Summary

In their paper titled "VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model," authors Jinze Yang, Haoran Wang, Zining Zhu, Chenglong Liu, Meng Wymond Wu, Zeke Xie, Zhong Ji, Jungong Han, and Mingming Sun address the challenge of image outpainting. This task involves extrapolating the surrounding parts of an image based on its central contents. Previous works have shown promising results in this area; however, they often lack versatility and customization options that limit their practical applicability in various scenarios. To overcome these limitations, the authors propose a novel image outpainting framework that allows for customized results tailored to users' specific requirements. The key innovation lies in leveraging a Multimodal Large Language Model (MLLM) to automatically extract and organize textual descriptions corresponding to both masked and unmasked parts of an image. By incorporating these text prompts into the model training process, the system gains the ability to customize outpainting outcomes effectively. Furthermore, the authors introduce a specialized Cross-Attention module known as Center-Total-Surrounding (CTS), which enhances interactions between specific spatial regions of an image and corresponding text prompts. Unlike many existing methods that require extensive training from scratch, is resource-efficient as it only requires slight fine-tuning on an off-the-shelf stable diffusion (SD) model. The experimental results conducted on three widely used datasets – Scenery, Building, – demonstrate that the proposed model significantly outperforms state-of-the-art methods in terms of outpainting quality. Additionally, outpainting results are showcased to highlight the model's customizable capabilities. Overall, contributes a robust solution to image outpainting challenges by introducing a versatile and customizable framework empowered by advanced language modeling techniques. The findings not only advance the field but also hold promise for practical applications across diverse domains.