Vinedresser3D: 基于代理的文本引导3D编辑
Vinedresser3D: Agentic Text-guided 3D Editing
AI总结：
Vinedresser3D通过多模态大语言模型和潜在空间编辑技术实现高质量文本引导的3D编辑，提升编辑精度与一致性。
摘要：
文本引导的3D编辑旨在利用自然语言指令修改现有的3D资产。当前方法难以联合理解复杂的提示、自动在3D中定位编辑并保持未编辑内容。我们引入了Vinedresser3D，这是一种高质量的文本引导3D编辑框架，直接在原生3D生成模型的潜在空间中运行。给定一个3D资产和一个编辑提示，Vinedresser3D使用多模态大语言模型来推断原始资产的丰富描述，识别编辑区域和编辑类型（添加、修改、删除），并生成分解的结构和外观级文本指导。代理随后选择一个有信息量的视角，并应用图像编辑模型以获得视觉指导。最后，一个基于倒置的rectified-flow修复管道，配以交错采样模块，在3D潜在空间中执行编辑，强制提示对齐的同时保持3D一致性和未编辑区域。在多样化的3D编辑实验中，Vinedresser3D在自动指标和人类偏好研究中均优于先前的基线方法，同时实现了精确、一致且无掩码的3D编辑。
英文摘要：
Text-guided 3D editing aims to modify existing 3D assets using natural-language instructions. Current methods struggle to jointly understand complex prompts, automatically localize edits in 3D, and preserve unedited content. We introduce Vinedresser3D, an agentic framework for high-quality text-guided 3D editing that operates directly in the latent space of a native 3D generative model. Given a 3D asset and an editing prompt, Vinedresser3D uses a multimodal large language model to infer rich descriptions of the original asset, identify the edit region and edit type (addition, modification, deletion), and generate decomposed structural and appearance-level text guidance. The agent then selects an informative view and applies an image editing model to obtain visual guidance. Finally, an inversion-based rectified-flow inpainting pipeline with an interleaved sampling module performs editing in the 3D latent space, enforcing prompt alignment while maintaining 3D coherence and unedited regions. Experiments on diverse 3D edits demonstrate that Vinedresser3D outperforms prior baselines in both automatic metrics and human preference studies, while enabling precise, coherent, and mask-free 3D editing.
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。
