DiffTV: Identity-Preserved Thermal-to-Visible Face Translation via Feature Alignment and Dual-Stage Conditions

摘要

The thermal-to-visible (T2V) face translation task is essential for enabling face verification in low-light or dark conditions by converting thermal infrared faces into their visible counterparts. However, this task faces two primary challenges. First, the inherent differences between the modalities hinder the effective use of thermal information to guide RGB face reconstruction. Second, translated RGB faces often lack the identity details of the corresponding visible faces, such as skin color. To tackle these challenges, we introduce DiffTV, the first Latent Diffusion Model (LDM) specifically designed for T2V facial image translation with a focus on preserving identity. Our approach proposes a novel heterogeneous feature alignment strategy that bridges the modal gap and extracts both coarse-and fine-grained identity features consistent with visible images. Furthermore, a dual-stage condition injection strategy introduces control information to guide identity-preserved translation. Experimental results demonstrate the superior performance of DiffTV, particularly in scenarios where maintaining identity integrity is critical.