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.