让3D编辑保持多视角一致，用户可手动控制关键视图
C3Editor: Achieving Controllable Consistency in 2D Model for 3D Editing
- 通过选择关键视图并微调2D模型实现多视角一致性
- 在真实视图上进行手动编辑，确保结果更可控
- 使用分离的LoRA模块分别优化单视图与多视图效果
基于2D提升的3D编辑方法常因缺乏视图一致的2D编辑模型，导致多视角编辑不一致。为此，我们提出C3Editor，一种可控且一致的2D提升式3D编辑框架。给定原始3D表示和文本编辑指令，该方法选择一个真实视图（GT视图）及其对应编辑图像作为优化目标，支持用户手动编辑。随后，在该GT视图及多个其他视图上微调2D编辑模型，使其对齐编辑后的图像并保持多视角一致性。为满足不同需求，我们引入独立的LoRA模块分别优化单视图拟合与多视图一致性。实验表明，本方法在定性与定量评估中均优于现有2D提升方法。
原文摘要 · Abstract (English)
Existing 2D-lifting-based 3D editing methods often encounter challenges related to inconsistency, stemming from the lack of view-consistent 2D editing models and the difficulty of ensuring consistent editing across multiple views. To address these issues, we propose C3Editor, a controllable and consistent 2D-lifting-based 3D editing framework. Given an original 3D representation and a text-based editing prompt, our method selectively establishes a view-consistent 2D editing model to achieve superior 3D editing results. The process begins with the controlled selection of a ground truth (GT) view and its corresponding edited image as the optimization target, allowing for user-defined manual edits. Next, we fine-tune the 2D editing model within the GT view and across multiple views to align with the GT-edited image while ensuring multi-view consistency. To meet the distinct requirements of GT view fitting and multi-view consistency, we introduce separate LoRA modules for targeted fine-tuning. Our approach delivers more consistent and controllable 2D and 3D editing results than existing 2D-lifting-based methods, outperforming them in both qualitative and quantitative evaluations.
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