DreamEdit3D: 多视角扩散模型的3D编辑个性化
DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing
AI总结：
本文提出DreamEdit3D，通过自然语言实现多视角扩散模型的3D编辑个性化，通过提取语义组件并学习不同组件的token嵌入，实现多视角一致的高质量3D编辑。
摘要：
尽管2D扩散模型在保持身份的个性化方面取得了显著成功，但将其能力扩展到3D资产仍是一个重大挑战，因为多视角一致性和空间控制的复杂性。受这些2D进展的启发，我们提出了一种新的个性化方法，用于文本引导的3D编辑，通过自然语言实现组合性和对象级控制。给定一个3D输入，我们渲染正交视图并提取对象级分割掩码以隔离语义组件。然后通过定制的两阶段优化策略学习每个组件的distinct token embeddings：多视角文本倒置与注意力对齐，随后对多视角扩散模型进行完整微调。在推理过程中，这些解耦的tokens与编辑提示无缝组合，生成多视角一致的图像，随后提升为高保真纹理3D网格。在多样化的编辑场景中的广泛评估表明，我们的方法成功地将2D个性化的优势转移到3D中，相比现有基线，在编辑忠实度和身份保持方面取得了最先进的成果。
英文摘要：
While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a significant challenge due to the complexities of multi-view consistency and spatial control. Inspired by these 2D advancements, we present a novel personalization method for text-guided 3D editing that enables compositional, object-level control through natural language. Given a 3D input, we render orthogonal views and extract object-level segmentation masks to isolate semantic components. We then learn distinct token embeddings for each component through a tailored two-phase optimization strategy: multi-view textual inversion with attention alignment, followed by full fine-tuning of multi-view diffusion model. During inference, these disentangled tokens seamlessly compose with editing prompts to generate multi-view consistent images, which are subsequently lifted into high-fidelity textured 3D meshes. Extensive evaluations across diverse editing scenarios demonstrate that our method successfully transfers the flexibility of 2D personalization to 3D, achieving state-of-the-art edit faithfulness and identity preservation compared to existing baselines.
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。
