Human Motion Transfer

Human motion transfer aims to realistically animate a person's appearance with the movements of another, creating synthetic videos. Recent research focuses on improving the realism and temporal consistency of these animations, employing techniques like neural networks (including Vision Transformers and GANs) that leverage 3D representations and learn from both global and local motion features to address challenges like pose variations and artifacts. These advancements are significant for applications in film, animation, and virtual reality, as well as for advancing our understanding of human motion representation and synthesis. The development of new evaluation metrics, such as identity scores, further enhances the rigor and objectivity of this rapidly evolving field.