Motion Retrieval

Motion retrieval focuses on efficiently accessing and retrieving human motion data based on textual or visual queries, aiming to bridge the gap between symbolic descriptions and complex spatio-temporal motion sequences. Current research emphasizes developing robust models, often employing transformer-based architectures and contrastive learning, to handle diverse datasets and improve cross-modal retrieval (text-to-motion, motion-to-text, and even video-motion). This field is significant for applications in animation, virtual reality, and human-computer interaction, enabling more intuitive and efficient manipulation of large motion databases. Furthermore, advancements in motion representation and alignment are improving the accuracy and generalizability of retrieval across different character morphologies and datasets.