Learned latent space models a continuous bi-directional aging process
head shape deformation as well as appearance changes across a wide range of ages.
We propose a novel generative adversarial network architecture that consists of a single conditional generator and a single discriminator. The conditional generator is responsible for transitions across age groups, and consists of three parts: identity encoder, a mapping network, and a decoder. We assume that while a person’s appearance changes with age their identity remains fixed. Therefore, we encode age and identity in separate paths. Each age group is represented by a unique pre-defined distribution. When given a target age, we sample from the respective age group distribution and assign it a vector age code. The age code is sent to a mapping network, that maps it into a learned unified age latent space. The resulting latent space approximates continuous age transformations. The input image is processed separately by the identity encoder to extract identity features. The decoder takes the mapping network output, a target age latent vector, and injects it to the identity features using modulated convolutions, originally proposed in StyleGAN2 [19]. During training, we use an additional age encoder to relate between real and generated images to the pre-defined distributions of their respective age class.
Dataset: FFHQ-Aging -> crowd-sourcing platform to annotate gender and age cluster for all 70, 000 images on FFHQ We defined 10 age clusters that capture both geometric and appearance changes throughout a person’s life: 0–2, 3–6, 7–9, 10–14, 15–19, 20–29, 30–39, 40–49, 50–69 and 70+
Fig. 1: Given a single portrait photo (left), our generative adversarial network predicts full head bi-directional age transformation. Note the diversity of the input photos spanning ethnicity, age (baby, young, adult, senior), gender, and facial expression. Gray border marks the input class, columns 2–7 are all synthe- sized by our method.
Fig. 2: Algorithm overview.
Fig. 3: FFHQ-Aging dataset. We label 70k images from FFHQ dataset [18] for gender and age via crowd-sourcing. In addition, for each image we extracted face semantic maps as well as head pose angles, glasses type and eye occlusion score.
Fig. 4: Comparison with FaceApp filters. Note that FaceApp cannot deform the shape of the head or generate extreme ages, e.g. 0–2.
Fig. 5: Comparison w.r.t. IPCGAN [44] on the FFHQ-Aging dataset. Left: our method. Middle: IPCGAN trained on CACD. Right: IPCGAN trained on FFHQAging. The proposed framework outperforms IPCGAN, producing sharper, more detailed and more realistic outputs across all age classes.
Fig. 6: Comparison with multi-domain transfer methods. The leftmost column is the input, followed by transformations to age classes 0–2, 3–6, 7–9, 15–19, 30–39, and 50–69 respectively. Multi-domain transfer methods struggle to model the gradual head deformation associated with age progression. Our method also produces better images in terms of quality and photorealism, while correctly modeling the growth of the head compared to StarGAN [7] and STGAN [26].
Fig. 7: Comparison with InterFaceGAN [37]. Age cluster legend only applies to our method. Rows 1,3,5: results on StyleGAN generated images. Row 7: result on a real image, embedded into the StyleGAN latent space using LIA [49]. Rows 2,4,6: Our result on StyleGAN generated images. Bottom row: Our result on a real image. Existing state-of-the-art interpolation methods cannot maintain the identity (rows 1,3,5,7), gender (row 5) and ethnicity (rows 3,5) of the input image. In addition, as seen in rows 1 & 3, using the same λ values on different photos produces different age ranges.
