The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

We address the need for a large-scale database of children's faces by using generative
adversarial networks (GANs) and face-age progression (FAP) models to synthesize a …

Video face re-aging deals with altering the apparent age of a person to the target age in
videos. This problem is challenging due to the lack of paired video datasets maintaining …

Face aging tasks aim to simulate changes in the appearance of faces over time. However,
due to the lack of data on different ages under the same identity, existing models are …

The challenge of transforming the apparent age of human faces in videos has not been
adequately addressed due to the complexities involved in preserving spatial and temporal …

Recent advancements in deep learning have yielded significant developments in age
manipulation techniques in the field of computer vision. To handle this task, recent …

Face re-aging is a prominent field in computer vision and graphics, with significant
applications in photorealistic domains such as movies, advertising, and live streaming …

Face aging has received continuous research attention over the past two decades. Although
previous works on this topic have achieved impressive success, two longstanding problems …

Existing face aging methods often focus on modeling either texture aging or using an
entangled shape-texture representation to achieve face aging. However, shape and texture …

Computational face aging enables predicting a person's future appearance using
algorithms, with the goal that the output age is close to the expected age and that the …