# Automatic Face Aging in Videos via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1811.11082 · HTML: https://mlsanity.com/paper/1811.11082/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 16 of 16)

- [Stitch it in Time: GAN-Based Facial Editing of Real Videos](https://mlsanity.com/paper/2201.08361/) (2022)
- [A Latent Transformer for Disentangled Face Editing in Images and Videos](https://mlsanity.com/paper/2106.11895/) (2021)
- [The Right to Talk: An Audio-Visual Transformer Approach](https://mlsanity.com/paper/2108.03256/) (2021)
- [BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation](https://mlsanity.com/paper/2108.03267/) (2021)
- [DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition](https://mlsanity.com/paper/2203.10233/) (2022)
- [Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval](https://mlsanity.com/paper/2203.14817/) (2022)
- [VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs](https://mlsanity.com/paper/2303.15893/) (2023)
- [OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation](https://mlsanity.com/paper/2205.10738/) (2022)
- [Self-supervised Domain Adaptation in Crowd Counting](https://mlsanity.com/paper/2206.03431/) (2022)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Sketch Less Face Image Retrieval: A New Challenge](https://mlsanity.com/paper/2302.05576/) (2023)
- [Video Face Re-Aging: Toward Temporally Consistent Face Re-Aging](https://mlsanity.com/paper/2311.11642/) (2023)
- [CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation](https://mlsanity.com/paper/2304.07372/) (2023)
- [ToonAging: Face Re-Aging upon Artistic Portrait Style Transfer](https://mlsanity.com/paper/2402.02733/) (2024)
- [Towards Practicality of Sketch-Based Visual Understanding](https://mlsanity.com/paper/2210.15146/) (2022)
- [ReAge3D: Re-Aging 3D Faces with View Consistency](https://mlsanity.com/paper/2606.18156/) (2026)

## Abstract (arXiv)

This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning. The proposed method models facial structures and the longitudinal face-aging process of given subjects coherently across video frames. The approach is optimized using a long-term reward, Reinforcement Learning function with deep feature extraction from Deep Convolutional Neural Network. Unlike previous age-progression methods that are only able to synthesize an aged likeness of a face from a single input image, the proposed approach is capable of age-progressing facial likenesses in videos with consistently synthesized facial features across frames. In addition, the deep reinforcement learning method guarantees preservation of the visual identity of input faces after age-progression. Results on videos of our new collected aging face AGFW-v2 database demonstrate the advantages of the proposed solution in terms of both quality of age-progressed faces, temporal smoothness, and cross-age face verification.