Pose Generation

Pose generation focuses on computationally creating realistic and diverse human or object poses, aiming to improve the quality and variety of synthetic data for various applications. Current research emphasizes developing models that generate poses with greater realism and diversity, often employing techniques like generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers, and incorporating disentanglement of factors like pose and expression for finer control. This field is significant for advancing computer vision, animation, robotics, and drug discovery, enabling more realistic simulations, improved human-computer interaction, and more efficient molecular docking procedures.