The field of personalized image generation is moving towards achieving fine-grained independent control over multiple subjects in synthesized images. Recent developments have focused on addressing the challenges of preserving subject fidelity and preventing cross-subject attribute leakage. Notable advancements include the use of dynamic semantic correspondence, adaptive focus allocation, and disentanglement techniques to improve the quality and controllability of generated images. These innovations have enabled state-of-the-art performance on various benchmarks and have opened up new possibilities for complex multi-subject synthesis applications. Noteworthy papers include: FocusDPO, which substantially enhances the performance of existing pre-trained personalized generation models, achieving state-of-the-art results on both single-subject and multi-subject personalized image synthesis benchmarks. MOSAIC, which achieves state-of-the-art performance on multiple benchmarks and maintains high fidelity with 4+ reference subjects, opening new possibilities for complex multi-subject synthesis applications.