ThermVision: Exploring FLUX for Synthesizing Hyper-Realistic Thermal Face Data and Animations via Image to Video Translation

摘要

High-quality thermal facial data is essential for advancing biometric recognition, surveillance, in-cabin driver monitoring, and human-computer interaction, all of which are integral for modern multimedia and interactive AI systems. In this work, we optimized the FLUX text-to-image diffusion model on diverse real-world thermal facial datasets to generate hyper-realistic 2D thermal facial images for both males and females, and propose a new dataset, ThermVision. To enhance their multimedia applicability, these images are processed through a video retargeting pipeline, where driving videos animate realistic facial expressions and head pose variations from a single 2D thermal image, producing high-fidelity thermal facial video sequences. The overall rendered dataset incorporates smart transformations, ensuring diversity across gender balance, extreme head pose variations, expressive facial dynamics, and facial accessories, making it a valuable resource for real-world applications. Additionally, we provide facial detection annotations to facilitate precise feature extraction and thermal-face analysis. To validate our synthetic dataset, we evaluate its effectiveness in thermal gender classification, as downstream machine learning task, along with thermal face localization and facial landmarks detection demonstrating its applicability in real-world scenarios. This approach significantly improves the availability, realism, and integration of thermal facial data, paving the way for more robust and immersive AI-powered thermal imaging applications. The dataset, code and associated models are available at- https://mali-farooq.github.io/ThermVision/