Preserving Identity: A Near-Free Lunch in Ultra-Low-Rate Face Compression

Generative codecs enable image compression at ultra-low bitrates, but on faces they exhibit identity drift: the reconstruction is perceptually plausible yet depicts a different person, a failure that standard fidelity/perceptual metrics do not capture. We build a face codec by repurposing a one-step diffusion face-restoration prior as the decoder of a compression pipeline. To this, we add an ArcFace-aware identity loss at training time. On a rate–distortion basis — with actual entropy-coded bitstreams — the loss yields a large identity gain (measured by held-out recognition networks) at an almost negligible cost on every other axis: a near-free lunch. On FFHQ, the same codec without the identity loss needs 50.9% more bitrate to match our identity, while the BD-rate on all distortion, perceptual, and distributional metrics stays within ±5%; recent generative codecs need 116–225% more bitrate for our identity. Controlled experiments show the gain is identity-specific structural alignment (drift suppression) rather than generic feature regularization or high-frequency hallucination. The identity gain transfers across codecs and datasets, and the codec extends to a single resolution-agnostic model that keeps its identity lead on in-the-wild unaligned faces, and to face-video compression — a near-free route to identity-faithful face compression. Our code is included in the supplementary material.