AI RESEARCH
HDRFace: Rethinking Face Restoration with High-Dimensional Representation
arXiv CS.CV
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ArXi:2605.14821v1 Announce Type: new Face restoration under complex degradations still remains an ill-posed inverse problem due to severe information loss. Although diffusion models benefit from strong generative priors, most methods still condition only on low-quality inputs, making it difficult to recover identity-critical details under heavy degradations. In this work, we propose HDRFace, a High-Dimensional Representation conditioned Face restoration framework that injects semantically rich priors into the conditional flow without modifying the generative backbone.