University of Washington（华盛顿大学）；Stanford University（斯坦福大学）；Adobe Research（Adobe 研究院）
Roy Or-El, Soumyadip Sengupta, Ohad Fried, Eli Shechtman, Ira Kemelmacher-Shlizerman
备注：
ECCV 2020Camera-Ready version. Main Changes: 1. Added Ethics & Bias statement in the supplementary material 2. Comparison figures to PyGAN [46] and S2GAN [13] were removed due to copyright issues. These figures can be found in the project's webpage (link is provided in the paper). 3. Added links to the code and dataset (Github)
We address the problem of single photo age progression and regression-the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. This limits the applicability of previous methods to aging of adults to slightly older adults, and application of those methods to photos of children does not produce quality results. We propose a novel multi-domain image-to-image generative adversarial network architecture, whose learned latent space models a continuous bi-directional aging process. The network is trained on the FFHQ dataset, which we labeled for ages, gender, and semantic segmentation. Fixed age classes are used as anchors to approximate continuous age transformation. Our framework can predict a full head portrait for ages 0-70 from a single photo, modifying both texture and shape of the head. We demonstrate results on a wide variety of photos and datasets, and show significant improvement over the state of the art.
University of Surrey（萨里大学）；iFlyTek-Surrey Joint Research Centre on Artificial Intelligence（科大讯飞-萨里人工智能联合研究中心）；Leibniz University Hannover（汉诺威莱布尼茨大学）；University of Twente（屯特大学）
A lifespan face synthesis (LFS) model aims to generate a set of photo-realistic face images of a person's whole life, given only one snapshot as reference. The generated face image given a target age code is expected to be age-sensitive reflected by bio-plausible transformations of shape and texture, while being identity preserving. This is extremely challenging because the shape and texture characteristics of a face undergo separate and highly nonlinear transformations w.r.t. age. Most recent LFS models are based on generative adversarial networks (GANs) whereby age code conditional transform…
From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
VCIP, College of Computer Science, Nankai University（VCIP，计算机科学学院，南开大学）；Samsung Research China - Beijing (SRC-B)（三星中国北京研究院）；School of Electrical and Information Engineering, Zhengzhou University（电气信息学院，郑州大学）；School of Computer , Zhengzhou University of Aeronautics（计算机学院，郑州航空大学）；Program of Computer Science, City University of Hong Kong (Dongguan)（计算机科学项目，香港城市大学（东莞））；City University of Hong Kong（香港城市大学）；Computer Vision Center, Barcelona（巴塞罗那计算机视觉中心）
Nanjing University of Information Science and Technology（南京信息工程大学）；Newcastle University（纽卡斯尔大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image. In this paper, we propose a novel framework taking two images as inputs, named dual-reference age synthesis (DRAS), which approaches the task differently; instead of using "hard" age information, i.e. a fixed number, our model determines the target age in a "soft" way, by employing a second reference image. Specifically, the proposed framework consists of an identity agent, an age agent and a generative adversarial network. It takes two images as input - an identity…
University of Warwick（华威大学）；Deakin University（迪肯大学）
The vanilla Generative Adversarial Networks (GAN) are commonly used to generate realistic images depicting aged and rejuvenated faces. However, the performance of such vanilla GANs in the age-oriented face synthesis task is often compromised by the mode collapse issue, which may result in the generation of faces with minimal variations and a poor synthesis accuracy. In addition, recent age-oriented face synthesis methods use the L1 or L2 constraint to preserve the identity information on synthesized faces, which implicitly limits the identity permanence capabilities when these constraints are …
Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院智能感知与计算研究中心、模式识别国家重点实验室、自动化研究所、脑科学与智能技术卓越创新中心）；University of Chinese Academy of Sciences（中国科学院大学）
Age synthesis is a challenging task due to the complicated and non-linear transformation in human aging process. Aging information is usually reflected in local facial parts, such as wrinkles at the eye corners. However, these local facial parts contribute less in previous GAN based methods for age synthesis. To address this issue, we propose a Wavelet-domain Global and Local Consistent Age Generative Adversarial Network (WaveletGLCA-GAN), in which one global specific network and three local specific networks are integrated together to capture both global topology information and local texture…
Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis
University of California, Los Angeles（加州大学洛杉矶分校）；Tencent Hunyuan（腾讯混元）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）
Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a challenge. Even though there is a large anatomical variability for children during their growth, the training datasets for pediatric CT scans are especially hard to obtain due to risks of radiation to children. In this paper, we propose a method to conditionally synthesize realistic pediatric CT images using a new auxiliary classifier generative adversarial network (ACGAN) architecture by taking age information into accou…
Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associated with developmental and aging processes, existing brain tissue segmentation methods are only viable for specific age groups. Consequently, methods developed for one age group may fail for another. In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning…
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Age estimation is a technique for predicting human ages from digital facial images, which analyzes a person's face image and estimates his/her age based on the year measure. Nowadays, intelligent age estimation and age synthesis have become particularly prevalent research topics in computer vision and face verification systems. Age synthesis is defined to render a facial image aesthetically with rejuvenating and natural aging effects on the person's face. Age estimation is defined to label a facial image automatically with the age group (year range) or the exact age (year) of the person's face…
Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age groups into the same age group, called face age synthesis (FAS); however, the former lacks visual results for model interpretation while the latter suffers from artifacts compromising downstream recognition. Therefore, this paper proposes a unified, multi-task framework to joint…
Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR lacks visual results for model interpretation and FAS compromises downstream recognition due to artifacts. Therefore, we propose a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn the age-invar…
