The recent advancements in multimodal AI have significantly enhanced the capabilities of generating expressive and synchronized outputs across various domains. The field is witnessing a shift towards more integrated and flexible models that can handle complex tasks involving multiple modalities, such as audio, video, and text. Innovations in diffusion models and transformer architectures are enabling more controlled and expressive talking head generation, with notable improvements in the synthesis of realistic and emotionally rich video content. Additionally, there is a growing focus on synchronizing verbal, nonverbal, and visual elements to support more effective communication, particularly in academic and professional settings. The integration of learnable attention mechanisms in streaming generation models is also advancing the field, providing more adaptive and efficient solutions for tasks requiring non-monotonic alignments. Furthermore, the development of multimodal models for speech synthesis from video inputs is opening new avenues for cross-lingual applications and enhancing the realism of generated speech. Overall, the trend is towards more sophisticated, multimodal models that offer greater control, expressiveness, and synchronization across different types of data.