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

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, extended

Summary

MTVDiff, a novel multimodal latent diffusion framework, addresses thermal-to-visible (T2V) face translation challenges like geometric discontinuities, semantic attribute mismatches, and identity degradation by integrating thermal images, depth maps, and text descriptions. It features a Dual-Branch Cross-Attention Fusion (DBCAF) module for thermal-depth feature extraction, a Gated Text-to-Visual Feature Alignment mechanism for semantic guidance, and Spatial Feature Transformations (SFT) for adaptive multimodal prior integration. Experiments on MCXFace and SpeakingFaces datasets show MTVDiff significantly outperforms existing GAN-based and diffusion-based methods, achieving FID reductions of up to 48.3% and Rank-1 accuracy improvements of up to 8.9% over DiffV2IR. The model, with 1.7B total parameters (334M trainable), trains on 4xNVIDIA RTX 4090 GPUs in approximately 48 hours.

Key takeaway

For AI Scientists or Machine Learning Engineers developing cross-spectral face recognition systems, MTVDiff offers a robust solution for enhanced identity verification in low-light conditions. You should consider integrating depth and structured textual descriptions into your diffusion models to achieve superior image quality and face verification accuracy. Be aware that reliance on auxiliary vision-language models for text generation introduces a dependency, and inaccurate prompts can degrade performance.

Key insights

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

Principles

• Multimodal input improves cross-spectral translation fidelity.
• Depth data is crucial for accurate 3D geometric facial structure.
• Textual semantics require geometric grounding for quality generation.

Method

MTVDiff uses a conditional latent diffusion pipeline with dual ResNet-18 encoders for thermal/depth, DBCAF for fusion, Gated Text-to-Visual Feature Alignment for text, and SFT to inject features into UNet residual blocks.

In practice

• Utilize depth maps for robust 3D facial structure generation.
• Employ structured text prompts for precise semantic control.
• Implement modality dropout during training for deployment robustness.

Topics

• Thermal-to-Visible Translation
• Latent Diffusion Models
• Multimodal Fusion
• Face Recognition
• Cross-Spectral Imaging
• Deep Learning Architectures

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

Related on AIssential

• MTVDiff: Multimodal Conditional Latent Diffusion for Enhanced Thermal-to-Visible Face Translation — Multimodal latent diffusion, integrating depth and text, significantly improves thermal-to-visible face translation and identity preservation.
• PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models — Multimodal diffusion language models enable efficient parallel visual perception, overcoming autoregressive MLLM limitations.
• Time-Reversed Imaging: A Multimodal Benchmark and Framework for Reconstructing Past Human-Environment Interactions — Multimodal physical traces (thermal, UV, RGB) enable VLM-guided reconstruction of past human-environment interactions.
• DVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration — DVFace uses a one-step, dual-prior diffusion framework for efficient, high-quality video face restoration.
• TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model — TCAM-Diff uses triplane representation and cross-attention diffusion to efficiently generate high-resolution 3D medical images.
• LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation — LatRef-Diff uses style codes and a novel modulation module for precise facial attribute and style manipulation.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.