MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis
Dewei Zhou, You Li, Fan Ma, Zongxin Yang, Yi Yang

TL;DR
This paper introduces MIGC++ and associated algorithms for precise multi-instance image synthesis, enabling detailed control over position, attributes, and quantity of multiple objects within a single image, addressing key challenges in attribute leakage and consistency.
Contribution
The paper presents MIGC++, a novel multi-instance generation controller that supports diverse attribute and position controls, along with algorithms ensuring consistency during iterative modifications.
Findings
Outperforms existing methods on COCO-MIG and Multimodal-MIG benchmarks.
Achieves precise control over position, attributes, and quantity.
Demonstrates robustness in iterative instance modifications.
Abstract
We introduce the Multi-Instance Generation (MIG) task, which focuses on generating multiple instances within a single image, each accurately placed at predefined positions with attributes such as category, color, and shape, strictly following user specifications. MIG faces three main challenges: avoiding attribute leakage between instances, supporting diverse instance descriptions, and maintaining consistency in iterative generation. To address attribute leakage, we propose the Multi-Instance Generation Controller (MIGC). MIGC generates multiple instances through a divide-and-conquer strategy, breaking down multi-instance shading into single-instance tasks with singular attributes, later integrated. To provide more types of instance descriptions, we developed MIGC++. MIGC++ allows attribute control through text \& images and position control through boxes \& masks. Lastly, we introduced…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · Advanced Vision and Imaging · Advanced Image and Video Retrieval Techniques
