{"ID":2847797,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00293","arxiv_id":"2511.00293","title":"MagicView: Multi-View Consistent Identity Customization via Priors-Guided In-Context Learning","abstract":"Recent advances in personalized generative models have demonstrated impressive capabilities in producing identity-consistent images of the same individual across diverse scenes. However, most existing methods lack explicit viewpoint control and fail to ensure multi-view consistency of generated identities. To address this limitation, we present MagicView, a lightweight adaptation framework that equips existing generative models with multi-view generation capability through 3D priors-guided in-context learning. While prior studies have shown that in-context learning preserves identity consistency across grid samples, its effectiveness in multi-view settings remains unexplored. Building upon this insight, we conduct an in-depth analysis of the multi-view in-context learning ability, and design a conditioning architecture that leverages 3D priors to activate this capability for multi-view consistent identity customization. On the other hand, acquiring robust multi-view capability typically requires large-scale multi-dimensional datasets, which makes incorporating multi-view contextual learning under limited data regimes prone to textual controllability degradation. To address this issue, we introduce a novel Semantic Correspondence Alignment loss, which effectively preserves semantic alignment while maintaining multi-view consistency. Extensive experiments demonstrate that MagicView substantially outperforms recent baselines in multi-view consistency, text alignment, identity similarity, and visual quality, achieving strong results with only 100 multi-view training samples.","short_abstract":"Recent advances in personalized generative models have demonstrated impressive capabilities in producing identity-consistent images of the same individual across diverse scenes. However, most existing methods lack explicit viewpoint control and fail to ensure multi-view consistency of generated identities. To address t...","url_abs":"https://arxiv.org/abs/2511.00293","url_pdf":"https://arxiv.org/pdf/2511.00293v2","authors":"[\"Hengjia Li\",\"Jianjin Xu\",\"Keli Cheng\",\"Lei Wang\",\"Ning Bi\",\"Boxi Wu\",\"Fernando De la Torre\",\"Deng Cai\"]","published":"2025-10-31T22:21:28Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}