Bin Xia

Bin Xia, Bohao Peng, Jiyang Liu et al.

Recently unified generation and editing models have achieved remarkable success with their impressive performance. These models rely mainly on text prompts for instruction-based editing and generation, but language often fails to capture users intended edit locations and fine-grained visual details. To this end, we propose two tasks: scribble-based editing and generation, that enables more flexible creation on graphical user interface (GUI) combining user textual, images, and freehand sketches. We introduce DreamOmni3, tackling two challenges: data creation and framework design. Our data synthesis pipeline includes two parts: scribble-based editing and generation. For scribble-based editing, we define four tasks: scribble and instruction-based editing, scribble and multimodal instruction-based editing, image fusion, and doodle editing. Based on DreamOmni2 dataset, we extract editable regions and overlay hand-drawn boxes, circles, doodles or cropped image to construct training data. For scribble-based generation, we define three tasks: scribble and instruction-based generation, scribble and multimodal instruction-based generation, and doodle generation, following similar data creation pipelines. For the framework, instead of using binary masks, which struggle with complex edits involving multiple scribbles, images, and instructions, we propose a joint input scheme that feeds both the original and scribbled source images into the model, using different colors to distinguish regions and simplify processing. By applying the same index and position encodings to both images, the model can precisely localize scribbled regions while maintaining accurate editing. Finally, we establish comprehensive benchmarks for these tasks to promote further research. Experimental results demonstrate that DreamOmni3 achieves outstanding performance, and models and code will be publicly released.

Xiaoyu Jin, Yuan Shi, Bin Xia et al.

Multi-modal Large Language Models (MLLMs) have a significant impact on various tasks, due to their extensive knowledge and powerful perception and generation capabilities. However, it still remains an open research problem on applying MLLMs to low-level vision tasks. In this paper, we present a simple MLLM-based Image Restoration framework to address this gap, namely Multi-modal Large Language Model based Restoration Assistant (LLMRA). We exploit the impressive capabilities of MLLMs to obtain the degradation information for universal image restoration. By employing a pretrained multi-modal large language model and a vision language model, we generate text descriptions and encode them as context embedding with degradation information for the degraded image. Through the proposed Context Enhance Module (CEM) and Degradation Context based Transformer Network (DC-former), we integrate these context embedding into the restoration network, contributing to more accurate and adjustable image restoration. Based on the dialogue with the users, our method leverages image degradation priors from MLLMs, providing low-level attributes descriptions of the input low-quality images and the restored high-quality images simultaneously. Extensive experiments demonstrate the superior performance of our LLMRA in universal image restoration tasks.