# Synthetic Thermal and RGB Videos for Automatic Pain Assessment utilizing a Vision-MLP Architecture

arXiv: https://arxiv.org/abs/2407.19811 · HTML: https://mlsanity.com/paper/2407.19811/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://mlsanity.com/paper/2210.02579/) (2022)
- [An Image Patch is a Wave: Phase-Aware Vision MLP](https://mlsanity.com/paper/2111.12294/) (2021)
- [SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams](https://mlsanity.com/paper/2012.02961/) (2020)
- [Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors](https://mlsanity.com/paper/2407.19475/) (2024)

## Built on by (most cited first, 12 of 12)

- [Efficient Pain Recognition via Respiration Signals: A Single Cross-Attention Transformer Multi-Window Fusion Pipeline](https://mlsanity.com/paper/2507.21886/) (2025)
- [PainFormer: a Vision Foundation Model for Automatic Pain Assessment](https://mlsanity.com/paper/2505.01571/) (2025)
- [Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image](https://mlsanity.com/paper/2507.21881/) (2025)
- [Tiny-BioMoE: a Lightweight Embedding Model for Biosignal Analysis](https://mlsanity.com/paper/2507.21875/) (2025)
- [A Lightweight Transformer for Pain Recognition from Brain Activity](https://mlsanity.com/paper/2604.16491/) (2026)
- [A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities](https://mlsanity.com/paper/2607.19716/) (2026)
- [ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment](https://mlsanity.com/paper/2607.19722/) (2026)
- [Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech](https://mlsanity.com/paper/2604.11417/) (2026)
- [Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection](https://mlsanity.com/paper/2609.22095/) (2026)
- [A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2608.09088/) (2026)
- [One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment](https://mlsanity.com/paper/2605.00856/) (2026)
- [A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos](https://mlsanity.com/paper/2505.03845/) (2025)

## Abstract (arXiv)

Pain assessment is essential in developing optimal pain management protocols to alleviate suffering and prevent functional decline in patients. Consequently, reliable and accurate automatic pain assessment systems are essential for continuous and effective patient monitoring. This study presents synthetic thermal videos generated by Generative Adversarial Networks integrated into the pain recognition pipeline and evaluates their efficacy. A framework consisting of a Vision-MLP and a Transformer-based module is utilized, employing RGB and synthetic thermal videos in unimodal and multimodal settings. Experiments conducted on facial videos from the BioVid database demonstrate the effectiveness of synthetic thermal videos and underline the potential advantages of it.