单眼到3D虚拟试用与生成语义关节字段的虚拟试用.
概括
本研究介绍了一种新的生成单眼到3D虚拟试用网络 (G3D-VTON),可以从单个2D图像中创建现实的3D虚拟试用结果. 该方法简化了数据需求,并提高了虚拟时尚应用程序的可扩展性.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 现有的3D虚拟试用方法通常需要昂贵的3D扫描或伪深度地图.
- 这些要求限制了虚拟试用应用程序的数据集创建和模型可扩展性.
研究的目的:
- 引入一个新的单眼到3D虚拟试用网络 (G3D-VTON),能够从单个单眼图像中合成多视图试用结果.
- 开发一个 3D 意识的条件生成对抗网络 (3D-GAN),通过仅在 2D 图像上进行训练来简化数据集构建,提高模型可扩展性.
主要方法:
- 生成单眼到3D虚拟试用网络 (G3D-VTON) 集成了一个3D感知条件解析模块 (3DPM),一个U-Net精制模块 (URM) 和一个基于流程的2D虚拟试用模块 (FTM).
- 3DPM使用条件生成语义关节字段生成3D表示,并通过反向皮肤利用3D SMPL预先学习签名距离函数 (SDF).
- 推迟姿势指导将风格和姿势脱为可视化可控生成的条件,而URM和FTM则为准确和现实的细节完善输出.
主要成果:
- 拟议的G3D-VTON有效地从单眼图像中生成忠实的3D人类外观,穿着所需的服装.
- 该方法在生成高质量的虚拟试用结果方面优于现有的3D-GAN和基于深度的3D方法.
- 与以前的方法相比,G3D-VTON在3D和2D输出中实现了卓越的视觉真实性.
结论:
- G3D-VTON提供了一个可扩展和有效的解决方案,用于单眼到3D的虚拟试用,利用在2D图像上训练的3D感知GAN.
- 集成模块成功地解决了以前方法的局限性,产生更准确和更现实的虚拟试用结果.
- 这种方法简化了数据集要求,并在合成多视图试用体验方面取得了重大进展.
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