BoxCtrl: 3D-Aware Visual Prompting for Geometric Image Editing

摘要

As instruction-based editing models and multimodal large language models continue to advance, a wide range of image editing tasks have become feasible. However, achieving precise and consistent geometric image editing, such as translating, scaling and rotating in 3D space, remains a major challenge. In this work, we introduce BoxCtrl, a 3D-aware visual prompting framework to address this problem. Unlike text-only or coarse 2D-guided approaches, our method introduces informative RGB 3D bounding boxes projected onto 2D images as a visual prompting. Each box is outlined and its three orthogonal faces are individually painted with different RGB colors, which simultaneously encode its position, size and orientation, providing a compact and intuitive in-context visual example. The key to BoxCtrl’s success lies in its well-designed bounding boxes, which serve as visual prompting that decouple geometric control from appearance control. This design enables the model to learn consistent correspondences between faces with the same color in the latent space, leading to a more precise understanding of geometric intention and more accurate editing result. We introduce a two-stage training paradigm comprising Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL). To address the scarcity of paired training data, we first construct a large-scale synthetic dataset for the SFT stage, which equips the model with fundamental editing capabilities. However, the generalization of the SFT model is constrained by the synthetic-to-real domain gap. To bridge this, we incorporate an online RL stage leveraging unpaired real-world data. Guided by a comprehensive reward function evaluating both geometric accuracy and visual fidelity, our SFT-RL strategy significantly enhances geometric precision while maintaining photorealistic quality. Extensive experiments demonstrate that BoxCtrl achieves state-of-the-art performance across translation, rotation, scaling, and composite editing tasks. Our code is available at https://github.com/beaglew/BoxCtrl.