The field of music-driven animation and human motion generation is rapidly evolving, with a focus on creating more realistic and engaging experiences. Researchers are exploring innovative approaches to generate high-quality animations that capture the nuances of human movement and emotion. One key area of development is the use of audio signals as conditioning inputs to generate motions that align with the semantics of the audio. This has led to the creation of more natural and intuitive communication methods, enabling users to interact with animations in a more immersive and expressive way. Another significant trend is the integration of machine learning and computer vision techniques to improve the quality and control of animated characters. This includes the development of novel architectures for music-driven 3D dance generation, which can decouple choreographic consistency into dance generality and genre specificity, resulting in more realistic and diverse animations. Noteworthy papers in this area include MEGADance, which proposes a novel architecture for music-driven 3D dance generation, and Neural Face Skinning, which enables intuitive control and detailed expression cloning across diverse face meshes. MMGT also presents a Motion Mask-Guided Two-Stage Network for co-speech gesture video generation, demonstrating improved video quality, lip-sync, and gesture. Hallo4 introduces a human-preference-aligned diffusion framework for dynamic portrait animation, showcasing obvious improvements in lip-audio synchronization and expression vividness.