论文标题
3D在野外服装的人类重建
3D Clothed Human Reconstruction in the Wild
论文作者
论文摘要
尽管在3D衣服的人类重建中取得了很多进展，但大多数现有方法都无法从野外图像产生强大的结果，其中包含各种人类的姿势和外观。这主要是由于训练数据集和野外数据集之间存在较大的域间隙。培训数据集通常是合成数据集，其中包含来自GT 3D扫描的渲染图像。但是，与真实的野外数据集相比，此类数据集包含简单的人类姿势和较少的自然图像外观，这使其对野外图像的概括非常具有挑战性。为了解决这个问题，在这项工作中，我们提出了一个3D衣服的人类重建框架，首先解决了露天图像的稳健性。首先，为了使域间隙的鲁棒性，我们提出了一条弱监督的管道，该管道可训练有2D监督目标。其次，我们设计了基于密集的损失功能，以减少弱监督的歧义。对几个公共野外数据集进行的广泛经验测试表明，我们提议的布牛会产生比最先进的方法更准确和强大的结果。这些代码可在此处可用：https：//github.com/hygenie1228/clothwild_release。
Although much progress has been made in 3D clothed human reconstruction, most of the existing methods fail to produce robust results from in-the-wild images, which contain diverse human poses and appearances. This is mainly due to the large domain gap between training datasets and in-the-wild datasets. The training datasets are usually synthetic ones, which contain rendered images from GT 3D scans. However, such datasets contain simple human poses and less natural image appearances compared to those of real in-the-wild datasets, which makes generalization of it to in-the-wild images extremely challenging. To resolve this issue, in this work, we propose ClothWild, a 3D clothed human reconstruction framework that firstly addresses the robustness on in-thewild images. First, for the robustness to the domain gap, we propose a weakly supervised pipeline that is trainable with 2D supervision targets of in-the-wild datasets. Second, we design a DensePose-based loss function to reduce ambiguities of the weak supervision. Extensive empirical tests on several public in-the-wild datasets demonstrate that our proposed ClothWild produces much more accurate and robust results than the state-of-the-art methods. The codes are available in here: https://github.com/hygenie1228/ClothWild_RELEASE.
