DecoEdit: Beyond Flat Control for 3D-Aware Unified Image Editing

While recent advances in multimodal image editing have achieved impressive results, current frameworks remain largely confined to “flat" 2D controls (e.g., text prompts, bounding boxes, or masks). Precise 3D-aware geometric manipulation—such as coordinating an object's location, physical scale, and 3D orientation while strictly preserving its fine-grained identity—remains an elusive challenge, typically suffering from severe detail degradation or requiring expensive per-instance fine-tuning. In this paper, we present DecoEdit, an all-in-one image editing framework that goes beyond flat control by seamlessly integrating 1D textual, 2D spatial, and 3D volumetric inputs under a unified architecture. First, to power this unified model, we establish a data curation pipeline and curate a large-scale dataset, namely DecoDataset, containing 1.6 million samples, encompassing diverse multi-dimensional (1D/2D/3D) tasks and their complex compositions. Furthermore, we establish a comprehensive image editing benchmark named DecoBench to systematically evaluate multi-task editing performance. Finally, to address the fundamental trade-off between complex spatial transformations and identity preservation, we propose a novel decoupled pipeline: DecoEdit factorizes the editing process by utilizing structural guidance from SAM-3D to anchor the target geometry, scale, and spatial orientation, while concurrently leveraging a detail-preservation path to lock in high-fidelity textures. Extensive experiments demonstrate that DecoEdit achieves state-of-the-art results. Notably, on the challenging 3D spatial pose and orientation control tasks, DecoEdit significantly outperforms leading closed-source models such as Seedream 5.0 in both geometric accuracy and identity consistency.