LCUDiff: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration

• URL: http://arxiv.org/abs/2602.04406v1
• Date: Wed, 04 Feb 2026 10:37:46 GMT
• Title: LCUDiff: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration
• Authors: Jue Gong, Zihan Zhou, Jingkai Wang, Shu Li, Libo Liu, Jianliang Lan, Yulun Zhang,
• Abstract summary: Existing methods for restoring degraded human-centric images often struggle with insufficient fidelity.<n>We propose LCUDiff, a stable one-step framework that upgrades a pre-trained latent diffusion model.<n> Experiments on synthetic and real-world datasets show competitive results with higher fidelity and fewer artifacts.
• Score: 23.264518366939825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing methods for restoring degraded human-centric images often struggle with insufficient fidelity, particularly in human body restoration (HBR). Recent diffusion-based restoration methods commonly adapt pre-trained text-to-image diffusion models, where the variational autoencoder (VAE) can significantly bottleneck restoration fidelity. We propose LCUDiff, a stable one-step framework that upgrades a pre-trained latent diffusion model from the 4-channel latent space to the 16-channel latent space. For VAE fine-tuning, channel splitting distillation (CSD) is used to keep the first four channels aligned with pre-trained priors while allocating the additional channels to effectively encode high-frequency details. We further design prior-preserving adaptation (PPA) to smoothly bridge the mismatch between 4-channel diffusion backbones and the higher-dimensional 16-channel latent. In addition, we propose a decoder router (DeR) for per-sample decoder routing using restoration-quality score annotations, which improves visual quality across diverse conditions. Experiments on synthetic and real-world datasets show competitive results with higher fidelity and fewer artifacts under mild degradations, while preserving one-step efficiency. The code and model will be at https://github.com/gobunu/LCUDiff.

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