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不配对的3D形状到形状转换通过梯度引导三平面扩散
IEEE transactions on visualization and computer graphics
|March 3, 2025
概括
本研究引入了一种新的三平面扩散模型,用于未配对的形状到形状转换,克服了以前基于GAN的方法的局限性. 该方法通过扩散和消除形状表示产生多样化,高质量的3D形状转换.
科学领域:
- 计算机视觉 计算机视觉
- 3D形状分析 3D形状分析
- 生成型模型 生成型模型
背景情况:
- 未配对的形状到形状翻译可以在没有配对数据的情况下转换3D形状.
- 现有的基于GAN的方法由于"模式崩"而难以实现多样性和质量.
研究的目的:
- 开发一种新方法,用于高保真性,多样化的未配对形状到形状的翻译.
- 解决现有的生成对抗网络 (GAN) 方法的局限性.
主要方法:
- 使用三平面扩散模型将3D对象分解为三平面表示.
- 应用前向扩散工艺来平滑形状结构和反向扩散工艺来消除噪音.
- 包含基于梯度的指导机制,以提高翻译准确性.
主要成果:
- 三平面扩散模型实现了卓越的形状忠实性和高质量的结果.
- 与最先进的基线相比,表现出更好的世代多样性.
- 在保持结构相似性的同时,成功地将不同领域的形状转化为不同的形状.
结论:
- 拟议的三平面扩散模型为未配对的形状到形状转换提供了一个强大的解决方案.
- 基于梯度的引导有效地提高了形状转换的准确性和质量.
- 这种方法推进了用于形状操纵的3D生成建模领域.
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