Motion Interpolation

Motion interpolation aims to generate realistic and smooth sequences of movement between given keyframes or poses, a crucial task in computer animation, robotics, and video processing. Current research focuses on developing deep learning models, including transformers, variational autoencoders, and diffusion models, to achieve this, often incorporating techniques like point cloud representations and hierarchical approaches to handle complex motions and diverse skeletal structures. These advancements improve the quality and efficiency of motion generation, enabling applications such as realistic character animation, motion editing for video enhancement, and the creation of novel motion sequences from limited input data.