Re-Aging GAN++: Temporally Consistent Transformation of Faces in Videos

Abstract

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 consistency. This task is further complicated by the scarcity of video datasets featuring specific individuals across various age groups. To address these issues, we introduce Re-Aging GAN++ (RAGAN++), a unified framework designed to perform facial age transformation in videos utilizing an innovative GAN-based model trained on still image data. Initially, the modulation process acquires multi-scale personalized age features to depict the attributes of the target age group. Subsequently, the encoder applies Gaussian smoothing at each scale, ensuring a seamless frame-to-frame transition that accounts for inter-frame variations, such as facial motion within the camera’s field of view. Remarkably, the proposed model demonstrates the ability to perform facial age transformation in videos despite being trained exclusively on image data. Our proposed method exhibits exceptional spatio-temporal consistency concerning facial identity, expression, and pose while maintaining natural variations across diverse age groups.

Keywords

• Video generation
• age manipulation
• GAN
• spatio-temporal consistency