3D Clothed Human Reconstruction in the Wild
This work addresses the robustness challenge in 3D human reconstruction for computer vision applications, though it appears incremental as it builds on existing methods to handle domain gaps.
The paper tackles the problem of 3D clothed human reconstruction from in-the-wild images, which often fail due to domain gaps between synthetic training data and real-world datasets, and proposes ClothWild, a framework that uses weakly supervised training with 2D supervision and a DensePose-based loss to achieve more accurate and robust results than state-of-the-art methods on public datasets.
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.