Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric
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arXiv
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| Format: | Preprint |
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2024
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| author | Xie, Jiu-Cheng Yang, Jun Wang, Wenqing Xu, Feng Xiong, Jiang Gao, Hao |
| author_facet | Xie, Jiu-Cheng Yang, Jun Wang, Wenqing Xu, Feng Xiong, Jiang Gao, Hao |
| contents | 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 remain unsettled: 1) generating diverse and plausible facial aging patterns at the target age stage; 2) measuring the rationality of identity variation between the original portrait and its syntheses with age progression or regression. In this paper, we introduce ${\rm{DLAT}}^{\boldsymbol{+}}$ to realize Diverse and Lifespan Age Transformation on human faces, where the diversity jointly manifests in the transformation of facial textures and shapes. Apart from the diversity mechanism embedded in the model, multiple consistency restrictions are leveraged to keep it away from counterfactual aging syntheses. Moreover, we propose a new metric to assess the rationality of Identity Deviation under Age Gaps (IDAG) between the input face and its series of age-transformed generations, which is based on statistical laws summarized from plenty of genuine face-aging data. Extensive experimental results demonstrate the uniqueness and effectiveness of our method in synthesizing diverse and perceptually reasonable faces across the whole lifetime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14036 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric Xie, Jiu-Cheng Yang, Jun Wang, Wenqing Xu, Feng Xiong, Jiang Gao, Hao Computer Vision and Pattern Recognition 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 remain unsettled: 1) generating diverse and plausible facial aging patterns at the target age stage; 2) measuring the rationality of identity variation between the original portrait and its syntheses with age progression or regression. In this paper, we introduce ${\rm{DLAT}}^{\boldsymbol{+}}$ to realize Diverse and Lifespan Age Transformation on human faces, where the diversity jointly manifests in the transformation of facial textures and shapes. Apart from the diversity mechanism embedded in the model, multiple consistency restrictions are leveraged to keep it away from counterfactual aging syntheses. Moreover, we propose a new metric to assess the rationality of Identity Deviation under Age Gaps (IDAG) between the input face and its series of age-transformed generations, which is based on statistical laws summarized from plenty of genuine face-aging data. Extensive experimental results demonstrate the uniqueness and effectiveness of our method in synthesizing diverse and perceptually reasonable faces across the whole lifetime. |
| title | Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.14036 |