SyncNoise：用于基于文本的3D场景编辑的几何一致噪声预测
SyncNoise: Geometrically Consistent Noise Prediction for Text-based 3D Scene Editing
AI总结：
本文提出SyncNoise，一种几何引导的多视角一致噪声编辑方法，通过同步编辑多视角并强制几何一致性，结合锚点视角传播与深度监督，实现了高保真且尊重文本指令的3D场景编辑。
摘要：
基于文本的2D扩散模型在图像生成和编辑方面展现了令人印象深刻的能力。同时，2D扩散模型在3D编辑任务中也展现出巨大潜力。然而，如何实现跨多视角的一致性编辑仍是一个挑战。虽然迭代数据集更新方法能够实现全局一致性，但它存在收敛缓慢和纹理过度平滑的问题。我们提出了SyncNoise，一种新颖的几何引导多视角一致噪声编辑方法，用于高保真3D场景编辑。SyncNoise利用2D扩散模型同步编辑多个视角，同时强制多视角噪声预测在几何上保持一致，从而确保语义结构和低频外观的全局一致性。为了进一步增强高频细节的局部一致性，我们设置了一组锚点视角，并通过跨视角重投影将其传播到相邻帧。为了提高多视角对应的可靠性，我们在训练期间引入深度监督，以增强精确几何结构的重建。通过在噪声和像素层面增强几何一致性，我们的方法实现了尊重文本指令的高质量3D编辑结果，特别是在具有复杂纹理的场景中。
英文摘要：
Text-based 2D diffusion models have demonstrated impressive capabilities in image generation and editing. Meanwhile, the 2D diffusion models also exhibit substantial potentials for 3D editing tasks. However, how to achieve consistent edits across multiple viewpoints remains a challenge. While the iterative dataset update method is capable of achieving global consistency, it suffers from slow convergence and over-smoothed textures. We propose SyncNoise, a novel geometry-guided multi-view consistent noise editing approach for high-fidelity 3D scene editing. SyncNoise synchronously edits multiple views with 2D diffusion models while enforcing multi-view noise predictions to be geometrically consistent, which ensures global consistency in both semantic structure and low-frequency appearance. To further enhance local consistency in high-frequency details, we set a group of anchor views and propagate them to their neighboring frames through cross-view reprojection. To improve the reliability of multi-view correspondences, we introduce depth supervision during training to enhance the reconstruction of precise geometries. Our method achieves high-quality 3D editing results respecting the textual instructions, especially in scenes with complex textures, by enhancing geometric consistency at the noise and pixel levels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
