The field of human motion and animation is rapidly advancing, with a focus on generating high-fidelity and realistic motions. Researchers are exploring new architectures and techniques, such as integrating dynamic embedding regularization into vision transformers, to model co-speech motion dynamics and enhance gesture naturalness. Another area of focus is on audio-driven talking head generation, where novel frameworks are being proposed to capture the complex interaction between audio and facial dynamics. Noteworthy papers in this area include ReCoM, which achieves state-of-the-art performance in motion realism, and Dual Audio-Centric Modality Coupling, which effectively integrates content and dynamic features from audio inputs. Additionally, techniques such as spatial-temporal semantic alignment and articulated kinematics distillation are being developed to improve synthesis stability and motion quality. Overall, the field is moving towards more realistic and controllable human motion and animation, with applications in virtual avatars, digital media, and beyond. Notable papers include: ReCoM, which reduces the Fréchet Gesture Distance by 86.7%, demonstrating significant improvement in motion realism. DreamActor-M1, which achieves expressive and robust human image animation with hybrid guidance, outperforming state-of-the-art works.