Motion Reconstruction

Motion reconstruction aims to accurately capture and represent 3D human movement from various input sources, such as video, sparse sensor data (e.g., IMUs), or even audio. Current research heavily utilizes deep learning, employing architectures like diffusion models, transformers, and graph neural networks to address challenges like occlusion, noise, and sparse data. These advancements are improving the accuracy, robustness, and real-time capabilities of motion reconstruction, with significant implications for virtual reality, animation, healthcare, and human-computer interaction.