Single-View Clothed Human Reconstruction With Multi-View Consistency Representation

For single-view clothed human reconstruction, the fashionable PIFu-like framework depends on the pixel-aligned feature essentially, while this leads to depth ambiguity and inaccuracy of representation. Additionally, this task faces the inherent problem of the lack of invisible information. To solve these two problems, we propose depth-guided pixel-aligned feature and multi-view consistency prior to constrain representation learning of the single-view reconstruction task. The difference, between the depth values of points and estimated depth map, is used to filter pixel-aligned features. Thus, the image encoder can focus on capturing the feature of the visible part which is more effective in feature representation. The method introduces contrastive learning and masked autoencoder to achieve consistency of SMPL vertex features in each view which helps model to imagine invisible information. The experimental results show that the proposed method enables the feature to represent surface details more efficiently, thus achieves more reasonable and accurate representation learning. The qualitative and quantitative evaluations on the public and commercial datasets show that the proposed method can achieve better performance than previous implicit representation based methods.