Holistic 3D Body Reconstruction From a Blurred Single Image

Abstract

Holistic human pose and shape reconstruction receive huge interest since it restores detailed human pose and shape including facial expression and finger-level hand shape. Existing deep 3D holistic human pose and shape reconstruction methods utilize sharp images as their input which leads to inaccurate human mesh when given a blurred image. Although there exist lots of image deblurring methods, a simple cascaded approach could not produce satisfactory results. In this paper, we introduce D2R (Deblurring-to-Reconstruction), a novel joint framework of human motion deblurring and 3D holistic body reconstruction to solve both problems simultaneously. In addition, we generate a new large-scale dataset that contains sharp/blur image pairs and corresponding 3D body pose/shape. We train the proposed joint framework in an alternating scheme to refine each module’s performance by utilizing an additional structure-aware module. Experimental results show that the proposed method achieves outperforming results 3D holistic human reconstruction qualitatively as well as quantitatively while input image is deblurred.

Keywords

• Holistic 3D body
• dataset
• deep learning
• blurred image