MTVDiff: Multimodal Conditional Latent Diffusion for Enhanced Thermal-to-Visible Face Translation

2026-07-22 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

MTVDiff is a novel multimodal latent diffusion framework designed to enhance thermal-to-visible face translation by addressing challenges like geometric discontinuities, semantic attribute mismatches, and identity degradation. This framework integrates depth and textual information to preserve identity characteristics. Its core technical contributions include a Dual-Branch Cross-Attention Fusion (DBCAF) module for multi-scale thermal-depth feature extraction, a Gated Text-to-Visual Feature Alignment mechanism for semantically-guided generation, and Spatial Feature Transformations (SFT) for adaptive multimodal prior integration. Extensive experiments on the MCXFace and SpeakingFaces datasets demonstrate MTVDiff significantly outperforms existing GAN-based and diffusion-based methods. It achieves substantial improvements in image quality and face verification, with FID reductions up to 48.3% and Rank-1 accuracy improvements up to 8.9%. This work provides a robust solution for face recognition systems operating under varying illumination conditions.

Key takeaway

For Computer Vision Engineers developing robust face recognition systems, especially those operating under varying illumination, you should consider adopting multimodal latent diffusion frameworks. MTVDiff's integration of depth and textual information significantly improves thermal-to-visible face translation, reducing FID by up to 48.3% and boosting Rank-1 accuracy by 8.9%. This approach offers a powerful method to enhance identity preservation and overall system reliability in challenging cross-spectral scenarios.

Key insights

Multimodal latent diffusion, integrating depth and text, significantly improves thermal-to-visible face translation and identity preservation.

Principles

• Multimodal data fusion enhances cross-spectral image translation.
• Identity preservation is crucial for face recognition systems.
• Semantic guidance improves generative model output quality.

Method

MTVDiff employs a Dual-Branch Cross-Attention Fusion (DBCAF) module, Gated Text-to-Visual Feature Alignment, and Spatial Feature Transformations (SFT) within a latent diffusion framework.

In practice

• Integrate depth and text data for robust cross-spectral face recognition.
• Employ DBCAF for effective multi-scale thermal-depth feature fusion.
• Apply SFT for adaptive multimodal prior integration in diffusion models.

Topics

• Thermal-to-Visible Translation
• Latent Diffusion Models
• Multimodal Fusion
• Face Recognition
• Computer Vision
• Cross-Spectral Imaging

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.