Pose Probability Modeling

Pose probability modeling focuses on accurately estimating and representing the probability distribution of poses, crucial for applications like human motion capture, object recognition, and robotics. Current research emphasizes improving pose estimation accuracy from limited or noisy data, employing techniques like neural networks incorporating motion priors, physics-based denoising, and transformer architectures for feature disentanglement to handle occlusions. These advancements are driving progress in areas such as virtual reality interaction, video-based exercise analysis, and robust object manipulation in industrial settings.