Curriculum-Driven Degradation-Aware Diffusion Transformers for Real-World Old-Photo Face Restoration

Real-world old-photo face restoration is severely hindered by the complexity of unknown physical damages and the synthetic-to-real domain gap. While recent Diffusion Transformer (DiT) architectures exhibit powerful generative priors, their static training paradigms treat all degradation levels equally. This inherently leads to a sub-optimal trade-off: over-smoothing on mild degradations and severe identity hallucination on complex physical damages. In this paper, we propose CurrDiT, a curriculum-driven DiT that dynamically adapts to degradation severity. First, we introduce a feedback-driven curriculum sampling strategy built upon a generative degradation process, which dynamically reweights degradation difficulty according to the evolving training state to progressively shift training from simple degradations to complex physical damages, thereby guiding the model to gradually unlock its untapped restoration potential. Second, CurrDiT adopts a dual-stream architecture that integrates degradation-aware local repair with global appearance guidance, where the Damage Restoration Branch (DRB) leverages damage-specific local cues to restore structurally corrupted regions, while the Global Degradation Restoration Branch (GDRB) exploits reliable non-damaged regions to enhance global appearance consistency. We further develop a generative degradation synthesis pipeline tailored to old-photo face restoration and construct a dedicated VintagePortraits benchmark for real-world evaluation of naturally aged old-photo portraits. Extensive experiments on old-photo face and blind face restoration benchmarks demonstrate that CurrDiT achieves superior perceptual quality, structural fidelity, and identity consistency.