发表机构
College of Computing and Data Science, Nanyang Technological University; Tencent AIPD（南洋理工大学计算与数据科学学院; 腾讯人工智能产品部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D对象局部编辑难题，EditVerse3D框架输入3D对象、粗略边界框和参考2D图像，利用区域感知自适应损失及数据增强技术，输出高质量编辑对象，实验证明其性能优于现有方法。
AI 中文摘要
3D对象的局部编辑一直是个挑战。人类与3D内容交互时倾向指定粗略修改区域而非精确边界，而先前方法存在差距。为此提出EditVerse3D框架，输入待编辑3D对象、指示目标区域的粗略3D边界框及描述所需修改的参考2D图像，输出连贯、高保真编辑3D对象。引入区域感知自适应损失并通过数据增强提高模型鲁棒性和泛化能力，构建数据集。实验表明其视觉质量和定量性能优于现有方法。
英文摘要
Local editing of 3D objects remains a long-standing challenge. When interacting with 3D content, humans naturally tend to specify a coarse region of interest for modification rather than defining precise editing boundaries. However, previous methods rely on fully edited 2D images, precise 3D masks, or redundant pipelines, which present a gap. To bridge this gap, we propose EditVerse3D, a novel 3D editing framework that enables high-quality object editing under such coarse guidance. Our approach takes as input a 3D object to be edited, a coarse 3D bounding box indicating the target region, and a reference 2D image describing the desired modification. It produces a coherent, high-fidelity edited 3D object. To facilitate this editing, we introduce a novel region-aware adaptive loss that emphasizes hard-to-learn regions and balances the objective between target and preserved areas. Complementing our loss function, we enhance model robustness and generalization through targeted data augmentations, such as training with scaled 3D masks and filtering out unrealistic editing pairs. We construct a large-scale 3D editing dataset derived from parts information. Extensive experiments demonstrate that EditVerse3D achieves superior visual quality and quantitative performance compared to existing 3D editing approaches. Please visit our project page at https://editverse3d.github.io.
CommentsAccepted to ECCV 2026. Project page: https://editverse3d.github.io/
Journal refComputer Vision - ECCV 2026, LNCS 17010, pp. 494-514 (2026)
DOI：10.1007/978-3-032-36839-3_27
