Dynamic Human

Dynamic human modeling focuses on accurately representing and reconstructing the movement and appearance of humans in 3D space, often from limited visual input like monocular videos. Current research emphasizes developing efficient and robust methods for reconstructing dynamic human geometry and appearance, leveraging neural networks (including transformers, diffusion models, and graph neural networks) and incorporating physical priors and geometric constraints to improve accuracy and realism. These advancements have significant implications for fields like animation, robotics, virtual reality, and education, enabling more realistic simulations, improved human-computer interaction, and personalized learning experiences.