3D virtual try-on enjoys many potential applications and hence has attracted wide attention. However, it remains a challenging task that has not been adequately solved. Existing 2D virtual try-on methods cannot be directly extended to 3D since they lack the ability to perceive the depth of each pixel. Besides, 3D virtual try-on approaches are mostly built on the fixed topological structure and with heavy computation. To deal with these problems, we propose a Decomposed Implicit garment transfer network (DI-Net), which can effortlessly reconstruct a 3D human mesh with the newly try-on result and preserve the texture from an arbitrary perspective. Specifically, DI-Net consists of two modules: 1) A complementary warping module that warps the reference image to have the same pose as the source image through dense correspondence learning and sparse flow learning; 2) A geometry-aware decomposed transfer module that decomposes the garment transfer into image layout based transfer and texture based transfer, achieving surface and texture reconstruction by constructing pixel-aligned implicit functions. Experimental results show the effectiveness and superiority of our method in the 3D virtual try-on task, which can yield more high-quality results over other existing methods.

翻译：三维虚拟试衣具有许多潜在应用前景，因而引起了广泛关注。然而，这一任务至今仍未得到充分解决，依然充满挑战。现有的二维虚拟试衣方法无法直接扩展到三维场景，因为它们缺乏对每个像素深度信息的感知能力。此外，三维虚拟试衣方法大多基于固定拓扑结构构建，且计算量巨大。为解决这些问题，我们提出了一种分解式隐式服装迁移网络（DI-Net），该网络能够轻松重建出带有新试衣效果的三维人体网格，并保留任意视角下的纹理信息。具体而言，DI-Net由两个模块组成：1）互补形变模块——通过密集对应学习和稀疏流学习，将参考图像形变至与源图像相同的姿态；2）几何感知分解式迁移模块——将服装迁移分解为基于图像布局的迁移和基于纹理的迁移，通过构建像素对齐的隐式函数实现表面与纹理的重建。实验结果表明，我们的方法在三维虚拟试衣任务中具有有效性和优越性，相较于现有其他方法能生成更高质量的结果。