Full Body

Research on full-body analysis focuses on accurately representing and manipulating the human form in images and videos, aiming to extract detailed information like anthropometric measurements, pose, and clothing style. Current efforts leverage deep learning, particularly convolutional neural networks (CNNs), transformers, and diffusion models, to achieve tasks such as 3D human mesh recovery, pose estimation, and realistic image generation and manipulation, often incorporating techniques like attention mechanisms and latent space manipulation. These advancements have significant implications for diverse fields, including healthcare (e.g., malnutrition monitoring, skin lesion tracking), fashion (e.g., virtual try-on), and entertainment (e.g., avatar creation, motion capture). The development of large-scale datasets is also a key focus, enabling the training of more robust and accurate models.