# A Dual-UNet Diffusion Framework for Personalized Panoramic Generation

## TL;DR

This paper introduces a new framework for generating personalized 360° panoramas using a dual-UNet diffusion model.

## Contribution

The paper proposes a decoupled feature injection mechanism and hybrid attention for customized multi-view image generation.

## Key findings

- The model effectively integrates spatial information from weakly correlated reference images.
- The framework generates coherent, high-quality customized multi-view images.
- A data augmentation strategy improves viewpoint-adaptive pose adjustments.

## Abstract

While text-to-image and customized generation methods demonstrate strong capabilities in single-image generation, they fall short in supporting immersive applications that require coherent 360° panoramas. Conversely, existing panorama generation models lack customization capabilities. In panoramic scenes, reference objects often appear as minor background elements and may be multiple in number, while reference images across different views exhibit weak correlations. To address these challenges, we propose a diffusion-based framework for customized multi-view image generation. Our approach introduces a decoupled feature injection mechanism within a dual-UNet architecture to handle weakly correlated reference images, effectively integrating spatial information by concurrently feeding both reference images and noise into the denoising branch. A hybrid attention mechanism enables deep fusion of reference features and multi-view representations. Furthermore, a data augmentation strategy facilitates viewpoint-adaptive pose adjustments, and panoramic coordinates are employed to guide multi-view attention. The experimental results demonstrate our model’s effectiveness in generating coherent, high-quality customized multi-view images.