GaussCtrl：多视角一致的文本驱动3D高斯泼溅编辑
GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing
AI总结：
本研究提出文本驱动的3D场景编辑方法GaussCtrl，通过深度条件编辑与注意力潜码对齐实现多视角一致编辑，相比现有方法编辑速度更快、视觉效果更优。
摘要：
我们提出GaussCtrl，一种用于编辑经3D高斯泼溅（3DGS）重建的3D场景的文本驱动方法。我们的方法首先利用3DGS渲染一组图像，再基于输入提示词使用预训练的2D扩散模型ControlNet编辑这些图像，随后用编辑后的图像优化3D模型。我们的核心贡献是多视角一致编辑，支持同时编辑所有图像，而非像以往工作那样迭代编辑单张图像同时更新3D模型，这既提升了编辑速度，也带来了更高的视觉质量。该特性通过两项机制实现：（a）深度条件编辑，利用天然一致的深度图保障多视角图像间的几何一致性；（b）基于注意力的潜码对齐，通过图像潜表示之间的自注意力与跨视角注意力，将编辑过程锚定到若干参考视角，统一编辑后图像的外观。实验表明，与现有最优方法相比，我们的方法编辑速度更快，视觉效果更优。
英文摘要：
We propose GaussCtrl, a text-driven method to edit a 3D scene reconstructed by the 3D Gaussian Splatting (3DGS). Our method first renders a collection of images by using the 3DGS and edits them by using a pre-trained 2D diffusion model (ControlNet) based on the input prompt, which is then used to optimise the 3D model. Our key contribution is multi-view consistent editing, which enables editing all images together instead of iteratively editing one image while updating the 3D model as in previous works. It leads to faster editing as well as higher visual quality. This is achieved by the two terms: (a) depth-conditioned editing that enforces geometric consistency across multi-view images by leveraging naturally consistent depth maps. (b) attention-based latent code alignment that unifies the appearance of edited images by conditioning their editing to several reference views through self and cross-view attention between images' latent representations. Experiments demonstrate that our method achieves faster editing and better visual results than previous state-of-the-art methods.
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。
