MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis
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 the Consistent-MIG algorithm to enhance the iterative MIG ability of MIGC and MIGC++. This algorithm ensures consistency in unmodified regions during the addition, deletion, or modification of instances, and preserves the identity of instances when their attributes are changed. We introduce the COCO-MIG and Multimodal-MIG benchmarks to evaluate these methods. Extensive experiments on these benchmarks, along with the COCO-Position benchmark and DrawBench, demonstrate that our methods substantially outperform existing techniques, maintaining precise control over aspects including position, attribute, and quantity. Project page: https://github.com/limuloo/MIGC.
Authors
- Yi Yang (ORCID: https://orcid.org/0000-0002-0512-880X)
- Fan Ma (ORCID: https://orcid.org/0000-0002-4131-1222)
- Zongxin Yang (ORCID: https://orcid.org/0000-0001-8783-8313)
- You Li (ORCID: https://orcid.org/0000-0002-0152-1655)
- Dewei Zhou (ORCID: https://orcid.org/0000-0003-3125-046X)
Institutions
- Harvard University (US)
- Zhejiang University (CN)
Publication Details
- Journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1109/tpami.2024.3510752
- Citations
- 4
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 1.00
Funders
- National Natural Science Foundation of China