The recent developments in the research area of multimodal entity linking and human motion understanding have shown significant advancements, particularly in integrating diverse data modalities and enhancing the interpretability of models. Researchers are increasingly focusing on bidirectional cross-modal interactions and the unification of verbal and non-verbal communication channels, which are crucial for creating more natural and effective human-computer interactions. Novel frameworks are being introduced to handle complex tasks such as emotion recognition from body movements and the generation of co-speech gestures and expressive talking faces, often leveraging large language models and diffusion techniques. These innovations not only improve the accuracy and efficiency of existing models but also open up new possibilities for real-world applications in fields like virtual reality and human-computer interaction. Notably, the integration of large language models for emotion recognition and the joint generation of talking faces and gestures are particularly groundbreaking, offering enhanced performance and reduced complexity.