Long Sequence Motion

Long sequence motion generation aims to create realistic and consistent animations or reconstructions of movement over extended periods, addressing limitations of previous methods that struggled with temporal coherence and computational cost. Current research focuses on novel neural network architectures, including transformers enhanced with specialized memory mechanisms (like Mamba memory) and state-space models, to efficiently handle the large datasets required for long sequences. These advancements are improving the quality and length of generated motions, with applications in fields such as animation, robotics, and computer vision, particularly in tasks like 4D scene reconstruction from video.