论文标题
人耳朵重建自动编码器
A Human Ear Reconstruction Autoencoder
论文作者
论文摘要
与计算机视觉领域相比，与人脸相比，耳朵作为人头的重要组成部分。受到以前在单眼3D面部重建的工作的启发，我们旨在利用这种框架来解决3D耳朵重建任务，其中2D耳朵输入图像上存在更多的微妙和困难的曲线和功能。我们的人耳朵重建自动编码器（HERA）系统可以预测3D耳罩的3D耳姿势和形状参数，而无需对这些参数进行任何监督。为了使我们的方法涵盖野外图像的差异，即使是灰度图像，我们提出了一种野生耳朵颜色模型。然后，以2D地标性能和重建的3D耳朵的出现来评估构造的端到端自我监督模型。
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.
