Fine Back Surfaces Oriented Human Reconstruction for Single RGB-D Images

Abstract

Current single RGB-D image based human surface reconstruction methods generally take both the RGB images and the captured frontal depth maps together so that the 3D cues from the frontal surfaces can help infer the full surface geometries. However, we observe that the back surfaces can often be quite different from the frontal surfaces and, therefore, current methods can mess the recovery process by adopting such 3D cues, especially for the unseen back surfaces. We need to do the back surface inference without the frontal depth map. Consequently, a novel human reconstruction framework is proposed, so that human models with fine geometric details, especially for the back surfaces, can be obtained. In this approach, a progressive estimation method is introduced to effectively recover the unseen back depth maps. The coarse back depth maps are recovered by the parametric models of the subjects, with the fine ones further obtained by the normal-maps conditioned GAN. This framework also includes a cross-attention based denoising method for the frontal depth maps. This method adopts the cross attention between the features of the last two layers encoded from the frontal depth maps and thus suppresses the noise for fine depth maps by the attentions of features from the low-noise and globally-structured highest layer. Experimental results show the efficacies of the proposed ideas.