Abstract. This paper proposes the use of an end-to-end Convolutional Neural Network for direct reconstruction of the 3D geometry of humans via volumetric regression. The proposed method does not require the fitting of a shape model and can be trained to work from a variety of input types, whether it be landmarks, images or segmentation masks. Additionally, non-visible parts, either self-occluded or otherwise, are still reconstructed, which is not the case with depth map regression. We present results that show that our method can handle both pose variation and detailed reconstruction given appropriate datasets for training. Keywords: 3D Reconstruction, Human Body Reconstruction, Volumetric Regression, VRN, Single Image Reconstruction Fig. 1 . Some example results using our method when trained with a high quality detailed training set.
Jackson et al. REF reconstruct the 3D geometry of humans via volumetric regression and show the ability to output fine-scale details.
3D Human Body Reconstruction from a Single Image via Volumetric Regression
ArXiv
ArXiv
Computer Science