  Human pose estimation faces hurdles in real-world applications due to factors
like lighting changes, occlusions, and cluttered environments. We introduce a
unique RGB-Thermal Nearly Paired and Annotated 2D Pose Dataset, comprising over
2,400 high-quality LWIR (thermal) images. Each image is meticulously annotated
with 2D human poses, offering a valuable resource for researchers and
practitioners. This dataset, captured from seven actors performing diverse
everyday activities like sitting, eating, and walking, facilitates pose
estimation on occlusion and other challenging scenarios. We benchmark
state-of-the-art pose estimation methods on the dataset to showcase its
potential, establishing a strong baseline for future research. Our results
demonstrate the dataset's effectiveness in promoting advancements in pose
estimation for various applications, including surveillance, healthcare, and
sports analytics. The dataset and code are available at
https://github.com/avinres/LWIRPOSE
