The field of human motion generation and sign language recognition is moving towards more efficient and realistic models. Recent developments have focused on improving the quality and diversity of generated motions, as well as reducing the computational resources required for sign language recognition. Notably, innovative approaches such as diffusion-based models and graph retrieval algorithms have been proposed to address the challenges of generating long-form content and synchronizing gestures with audio. Additionally, new frameworks have been designed to streamline the process of sign language generation and improve the accuracy of text conversion. Overall, these advancements have the potential to significantly improve the realism and effectiveness of human-computer interaction systems. Some noteworthy papers in this area include: ReactionMamba, which generates competitive long-sequence motions with substantial improvements in inference speed. TalkingPose, which produces long-form temporally consistent human upper-body animations with a stable diffusion backbone. Stable Signer, which redefines the sign language production task as a hierarchical generation end-to-end task and achieves improved performance compared to current state-of-the-art methods.