Motion Transfer

Motion transfer aims to realistically animate a target image or 3D model based on the motion from a reference video, often without requiring explicit correspondences between the source and target. Current research focuses on improving the accuracy and realism of motion transfer, particularly for complex motions and diverse object types, employing techniques like neural networks (including diffusion models and transformers), physical simulation, and keypoint-based approaches. This field is significant for applications in film, gaming, virtual reality, and even medical imaging, offering efficient and creative ways to generate dynamic content and enhance data analysis.