A Human Ear Reconstruction Autoencoder
This addresses ear reconstruction for computer vision applications, but it is incremental as it adapts an existing face reconstruction method to ears.
The paper tackles 3D ear reconstruction from 2D images using a self-supervised autoencoder, achieving results without supervision to 3D parameters and evaluating with 2D landmark localization and 3D appearance.
The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision. Inspired by previous work on monocular 3D face reconstruction using an autoencoder structure to achieve self-supervised learning, we aim to utilise such a framework to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. To make our approach cover the variance for in-the-wild images, even grayscale images, we propose an in-the-wild ear colour model. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears.