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From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
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Proposes Cradle2Cane, a two-pass few-step T2I diffusion (SDXL-Turbo + LoRA) face aging framework that first adjusts age via text-guided adaptive noise injection and then restores identity with SVR-ArcFace and Rotate-CLIP embeddings, trained end-to-end.
Problem
Face aging suffers from an Age-ID trade-off: methods either achieve accurate age transformation at the cost of identity or preserve identity with weak aging, especially for large age gaps and in-the-wild faces.
Approach
Decouple age and identity into two diffusion passes: a first pass with age-count-dependent noise injection guided by age/gender prompts for aging, and a second pass conditioned on singular-value-reweighted ArcFace and slerp-shifted CLIP embeddings to restore identity on the aged image.
Key results
- On CelebA-HQ under Qwen-VL evaluation, Cradle2Cane achieves age difference ±4.62 and HCS 78.33, the best among compared methods, with 0.56s inference time and 10K training images.
- Under Face++ evaluation on CelebA-HQ, Cradle2Cane reports age difference ±7.47 (best) and HCS 81.33 (best), though its ID similarity (81.34) trails HRFAE (94.32).
- Ablation on Qwen-VL shows the adaptive noise injection alone reduces age difference from ±8.87 to ±3.94, while the full configuration reaches HCS 78.33.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the two-pass Cradle2Cane framework instead of the strongest baseline | face aging methods · CelebA-HQ test (Qwen-VL) | Age Diff.: ±6.31 -> ±4.62 | Helped |
| Use the two-pass Cradle2Cane framework instead of the strongest baseline | face aging methods · CelebA-HQ test (Qwen-VL) | HCS: 77.84 -> 78.33 | Helped |
| Add adaptive noise injection (ablation) | Cradle2Cane · CelebA-HQ (Qwen-VL) | Age Diff.: ±8.87 -> ±3.94 | Helped |
| Add adaptive noise injection (ablation) | Cradle2Cane · CelebA-HQ (Qwen-VL) | ID Sim.: 68.92 -> 59.70 | Hurt |
| Add SVR-ArcFace (ablation) | Cradle2Cane with adaptive noise injection · CelebA-HQ (Qwen-VL) | ID Sim.: 59.70 -> 63.38 | Helped |
| Add Rotate-CLIP (ablation) | Cradle2Cane with adaptive noise injection and SVR-ArcFace · CelebA-HQ (Qwen-VL) | HCS: 71.92 -> 78.33 | Helped |
| Use thresholds {20, 40} instead of {5, 20} (ablation) | Cradle2Cane adaptive noise injection · CelebA-HQ test (threshold ablation) | ID Sim.: 73.73 -> 78.55 | Helped |
| Use thresholds {20, 40} instead of {5, 20} (ablation) | Cradle2Cane adaptive noise injection · CelebA-HQ test (threshold ablation) | Age Diff.: 4.92 -> 6.71 | Hurt |
| Apply Carvekit background-removal preprocessing (ablation) | Cradle2Cane · CelebA-HQ-in-the-wild | Age Diff.: 7.00 -> 6.76 | Helped |
| Apply Carvekit background-removal preprocessing (ablation) | Cradle2Cane · CelebA-HQ-in-the-wild | ID Sim.: 62.27 -> 62.00 | Hurt |
| Apply Carvekit background-removal preprocessing (ablation) | Cradle2Cane · CelebA-HQ-in-the-wild | Img. Quality: 91.60 -> 91.75 | Helped |
| Apply Carvekit background-removal preprocessing (ablation) | Cradle2Cane · CelebA-HQ-in-the-wild | HCS: 70.97 -> 71.02 | No clear effect |
Surprising findings
- Identity similarity of Cradle2Cane is not the highest; HRFAE and SAM/FADING score higher ID similarity on several protocols, so the gains come mainly from balance (HCS) rather than identity alone.
- Removing Carvekit background removal changes in-the-wild performance only marginally, suggesting robustness comes from the backbone and embeddings.
Limitations
- Accessories such as eyeglasses, earrings, or clothing color may not be faithfully retained for extreme age transformations.
- Full dataset and training code are not released due to privacy and company policy.
- Identity similarity is lower than some baselines (e.g., HRFAE) on several protocols.
Experimental setup
Datasets: FFHQ CelebA-HQ AgeDB-30 CACD FG-NET
Benchmarks: CelebA-HQ CelebA-HQ (in-the-wild) AgeDB-30 CACD FG-NET
Metrics: age difference (MAE) identity similarity image quality Harmonic Consistency Score (HCS) inference time
Baselines: Lifespan HRFAE SAM CUSP FADING IPFE StyleCLIP FaceDNeRF
Builds on: SDXL-Turbo Adversarial Diffusion Distillation ArcFace CLIP LoRA
Closest papers by meaning
- When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework (CVPR 2021 · similarity 0.90)
- Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition (AAAI 2019 · similarity 0.90)
- Face Aging With Identity-Preserved Conditional Generative Adversarial Networks (CVPR 2018 · similarity 0.89)
- A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark (CVPR 2024 · similarity 0.87)
- AQUAFace: Age-Invariant Quality Adaptive Face Recognition for Unconstrained Selfie vs ID Verification (AAAI 2025 · similarity 0.86)
- FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation (NeurIPS 2026 · similarity 0.84)
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