DragTex：基于点的生成式 3D 网格纹理编辑
DragTex: Generative Point-Based Texture Editing on 3D Mesh
AI总结：
本文提出 DragTex，通过扩散模型融合多视角局部纹理、微调解码器减少非拖拽区域误差，并利用多视角图像训练 LoRA，实现直观且局部一致的 3D 网格纹理拖拽编辑。
摘要：
近年来，利用生成式人工智能创建带纹理的 3D 网格受到了广泛关注。现有方法虽然支持在 3D 网格上进行基于文本的生成式纹理生成或编辑，但往往难以通过更直观的交互精确控制纹理图像的像素。尽管二维图像可以通过拖拽交互进行生成式编辑，但将这类方法直接应用于 3D 网格纹理仍会导致多视角之间缺乏局部一致性、误差累积以及训练时间长等问题。为应对这些挑战，我们提出了一种名为 DragTex 的基于点的生成式 3D 网格纹理编辑方法。该方法利用扩散模型，在不同视角之间融合变形轮廓附近区域中局部不一致的纹理，从而实现局部一致的纹理编辑。此外，我们微调了一个解码器，以减少非拖拽区域的重建误差，进而缓解整体误差累积。同时，我们使用多视角图像训练 LoRA，而不是对每个视角单独训练，这显著缩短了训练时间。实验结果表明，我们的方法有效实现了在 3D 网格上拖拽纹理，并生成了符合拖拽交互预期意图的合理纹理。
英文摘要：
Creating 3D textured meshes using generative artificial intelligence has garnered significant attention recently. While existing methods support text-based generative texture generation or editing on 3D meshes, they often struggle to precisely control pixels of texture images through more intuitive interaction. While 2D images can be edited generatively using drag interaction, applying this type of methods directly to 3D mesh textures still leads to issues such as the lack of local consistency among multiple views, error accumulation and long training times. To address these challenges, we propose a generative point-based 3D mesh texture editing method called DragTex. This method utilizes a diffusion model to blend locally inconsistent textures in the region near the deformed silhouette between different views, enabling locally consistent texture editing. Besides, we fine-tune a decoder to reduce reconstruction errors in the non-drag region, thereby mitigating overall error accumulation. Moreover, we train LoRA using multi-view images instead of training each view individually, which significantly shortens the training time. The experimental results show that our method effectively achieves dragging textures on 3D meshes and generates plausible textures that align with the desired intent of drag interaction.
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。
