ViewWeaver: Geometry-Grounded Generative Rendering for 3D-Aware Image Customization

摘要

Image customization is a fundamental image editing task that synthesizes an instruction-following image of a specific subject conditioned on reference images. However, most existing methods rely on a single reference image, which makes it difficult to preserve the subject’s underlying 3D structure and maintain multi-view consistency. We therefore study 3D-aware image customization: given an arbitrary multi-view reference set, the model generates an instruction-following image from a user-specified target camera viewpoint, while preserving both subject identity and 3D geometry. This formulation enables a practical “capture-then-customize” workflow and reduces the viewpoint drift commonly observed in 2D-conditioned editors. We propose ViewWeaver, a geometry-grounded generative rendering framework that anchors synthesis with explicit target-view rendering cues aligned with camera geometry, and aggregates multi-view evidence through a lightweight Mixture-of-Views module inside a rectified-flow DiT generator. To facilitate scalable training and systematic evaluation, we build a large-scale data engine and introduce GSO12, a comprehensive benchmark assessing visual fidelity, instruction adherence, and 3D consistency across different reference-view budgets and diverse scene contexts. Extensive experiments show that ViewWeaver significantly improves 3D consistency and viewpoint controllability while maintaining strong identity preservation, enabling accurate and flexible view-controllable customization.