# T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models

arXiv: https://arxiv.org/abs/2209.08814 · HTML: https://mlsanity.com/paper/2209.08814/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 20 of 20)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [Dual Diffusion Implicit Bridges for Image-to-Image Translation](https://mlsanity.com/paper/2203.08382/) (2022)
- [HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment](https://mlsanity.com/paper/2005.05005/) (2020)
- [Dual Variational Generation for Low-Shot Heterogeneous Face Recognition](https://mlsanity.com/paper/1903.10203/) (2019)
- [Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks](https://mlsanity.com/paper/1812.05155/) (2018)
- [A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset](https://mlsanity.com/paper/2101.02637/) (2021)
- [Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis](https://mlsanity.com/paper/1904.07344/) (2019)
- [Cross-Spectral Face Hallucination via Disentangling Independent Factors](https://mlsanity.com/paper/1909.04365/) (2019)
- [Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN](https://mlsanity.com/paper/2104.06534/) (2021)
- [A Synthesis-Based Approach for Thermal-to-Visible Face Verification](https://mlsanity.com/paper/2108.09558/) (2021)

## Built on by (most cited first, 17 of 17)

- [T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models](https://mlsanity.com/paper/2504.15512/) (2025)
- [Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.15733/) (2023)
- [T-FAKE: Synthesizing Thermal Images for Facial Landmarking](https://mlsanity.com/paper/2408.15127/) (2024)
- [When Visible-to-Thermal Facial GAN Beats Conditional Diffusion](https://mlsanity.com/paper/2302.09395/) (2023)
- [DiffX: Guide Your Layout to Cross-Modal Generative Modeling](https://mlsanity.com/paper/2407.15488/) (2024)
- [CM-Diff: A Single Generative Network for Bidirectional Cross-Modality Translation Diffusion Model Between Infrared and Visible Images](https://mlsanity.com/paper/2503.09514/) (2025)
- [Vista-Morph: Unsupervised Image Registration of Visible-Thermal Facial Pairs](https://mlsanity.com/paper/2306.06505/) (2023)
- [TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models](https://mlsanity.com/paper/2307.01064/) (2023)
- [Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition](https://mlsanity.com/paper/2312.05907/) (2023)
- [MTVDiff: Multimodal Conditional Latent Diffusion for Enhanced Thermal-to-Visible Face Translation](https://mlsanity.com/paper/2607.19886/) (2026)
- [Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition](https://mlsanity.com/paper/2312.05908/) (2023)
- [TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation](https://mlsanity.com/paper/2408.14227/) (2024)
- [LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images](https://mlsanity.com/paper/2503.16376/) (2025)
- [ThermalDiffusion: Visual-to-Thermal Image-to-Image Translation for Autonomous Navigation](https://mlsanity.com/paper/2506.20969/) (2025)
- [LoGoColor: Local-Global 3D Colorization for 360deg Scenes](https://mlsanity.com/paper/2512.09278/) (2025)
- [Multi-Attribute guided Thermal Face Image Translation based on Latent Diffusion Model](https://mlsanity.com/paper/2512.21032/) (2025)
- [SC-Diff: Semantically Calibrated Diffusion for Visible-to-Infrared Image Translation](https://mlsanity.com/paper/2608.08555/) (2026)

## Abstract (arXiv)

Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using visible spectrum images. But, acquiring images in the visible spectrum is impractical in scenarios of low-light and nighttime conditions, and often images are captured in an alternate domain such as the thermal infrared domain. Facial verification in thermal images is often performed after retrieving the corresponding visible domain images. This is a well-established problem often known as the Thermal-to-Visible (T2V) image translation. In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images. During training, the model learns the conditional distribution of visible facial images given their corresponding thermal image through the diffusion process. During inference, the visible domain image is obtained by starting from Gaussian noise and performing denoising repeatedly. The existing inference process for DDPMs is stochastic and time-consuming. Hence, we propose a novel inference strategy for speeding up the inference time of DDPMs, specifically for the problem of T2V image translation. We achieve the state-of-the-art results on multiple datasets. The code and pretrained models are publically available at http://github.com/Nithin-GK/T2V-DDPM