{'fraction_non_alphanumeric': 0.021739130434782608, 'fraction_numerical': 0.005636070853462158, 'mean_word_length': 5.033980582524272, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Face Denoising and 3D Reconstruction from A Single Depth Image

The reconstruction of 3D face shapes and expressions from a single depth image obtained by a consumer depth camera is a challenging issue considering device-specific noise, the data missing, and the lack of textual constraints. In order to relieve the computationally-intensive nonlinear optimization of traditional template-fitting-based methods, we aim to build an end-to-end regression framework between a depth image and a 3D face encoded by the identity, the expression, and the pose parameters. Concerning the lack of paired depth images and 3D faces, we utilize the unsupervised CycleGAN-based network to adapt the regression model learned from the synthetic data to the real-captured noisy depth images. Instead of separate depth image denoising and 3D face inference, we present a task-specific coupled loss for end-to-end 3D face estimation. We propose a three-tier constraint for the shape consistency in the joint embedding, the depth image, and the surface space to avoid shape distortions in the unsupervised domain adaptation network. We report promising qualitative results for the task of the face denoising and 3D face reconstruction from a single depth image.