Abstract
This research aims to study a self-supervised 3D clothing reconstruction method, which recovers the geometry shape, and texture of human clothing from a single 2D image. Compared with existing methods, we observe that three primary challenges remain: (1) the conventional template-based methods are limited to modeling non-rigid clothing objects, e.g., handbags and dresses, which are common in fashion images; (2) 3D ground-truth meshes of clothing are usually inaccessible due to annotation difficulties and time costs. (3) It remains challenging to simultaneously optimize four reconstruction factors, i.e., camera viewpoint, shape, texture, and illumination. The inherent ambiguity compromises the model training, such as the dilemma between a large shape with a remote camera or a small shape with a close camera. In an attempt to address the above limitations, we propose a causality-aware self-supervised learning method to adaptively reconstruct 3D non-rigid objects from 2D images without 3D annotations. In particular, to solve the inherent ambiguity among four implicit variables, i.e., camera position, shape, texture, and illumination, we study existing works and introduce an explainable structural causal map (SCM) to build our model. The proposed model structure follows the spirit of the causal map, which explicitly considers the prior template in the camera estimation and shape prediction. When optimization, the causality intervention tool, i.e., two expectation-maximization loops, is deeply embedded in our algorithm to (1) disentangle four encoders and (2) help the prior template update. Extensive experiments on two 2D fashion benchmarks, e.g., ATR, and Market-HQ, show that the proposed method could yield high-fidelity 3D reconstruction. Furthermore, we also verify the scalability of the proposed method on a fine-grained bird dataset, i.e., CUB.
Abstract (translated)
URL
https://arxiv.org/abs/2204.13096
