Masked Motion

Masked motion research focuses on leveraging incomplete or masked motion data to achieve various goals, primarily in video and animation processing. Current approaches utilize masked autoencoders and transformers, often incorporating motion-guided masking strategies or integrating text-based guidance to improve the accuracy and efficiency of motion prediction and inpainting. This work is significant for advancing self-supervised learning in video analysis, enabling more robust and efficient motion correction in medical imaging (like MRI), and creating more versatile and interactive character animation in virtual environments.