Abstract
Natural language offers a highly intuitive interface for enabling localized fine-grained edits of 3D shapes. However, prior works face challenges in preserving global coherence while locally modifying the input 3D shape. In this work, we introduce an inpainting-based framework for editing shapes represented as point clouds. Our approach leverages foundation 3D diffusion models for achieving localized shape edits, adding structural guidance in the form of a partial conditional shape, ensuring that other regions correctly preserve the shape's identity. Furthermore, to encourage identity preservation also within the local edited region, we propose an inference-time coordinate blending algorithm which balances reconstruction of the full shape with inpainting at a progression of noise levels during the inference process. Our coordinate blending algorithm seamlessly blends the original shape with its edited version, enabling a fine-grained editing of 3D shapes, all while circumventing the need for computationally expensive and often inaccurate inversion. Extensive experiments show that our method outperforms alternative techniques across a wide range of metrics that evaluate both fidelity to the original shape and also adherence to the textual description.
Abstract (translated)
自然语言提供了一种高度直观的界面，用于对3D形状进行本地化的精细编辑。然而，之前的工作在局部修改输入3D形状的同时保持全局一致性方面面临挑战。在这项工作中，我们提出了一种基于图像修复（inpainting）的框架，用于编辑表示为点云的形状。我们的方法利用基础3D扩散模型来实现局部形状编辑，并以部分条件形状的形式添加结构指导，确保其他区域正确地保留形状的身份特征。 为了进一步鼓励在本地编辑区域内保持身份特征，我们提出了一种推理时间坐标融合算法，在推理过程中通过逐步调整噪声水平平衡完整形状的重建和图像修复。我们的坐标融合算法可以无缝地将原始形状与其编辑版本结合在一起，从而实现对3D形状进行精细控制的编辑，并且避免了计算成本高昂且通常不准确的逆向过程。 广泛的实验表明，我们提出的方法在评估原始形状保真度和文本描述符合性的广泛指标上均优于其他技术。
URL
https://arxiv.org/abs/2507.15399
