The field of multimodal animation and generation is witnessing significant advancements, driven by innovative approaches that leverage cutting-edge techniques from computer vision, graphics, and machine learning. Researchers are focusing on developing more realistic and controllable animation systems, particularly in the context of human-computer interaction and accessibility. A key direction is the integration of multimodal inputs, such as audio, text, and visual cues, to generate more nuanced and expressive animations. This includes the use of perceptual losses, disentangled embedding spaces, and spatial-temporal graph models to improve the quality and diversity of generated animations. Another area of interest is the democratization of high-fidelity animation generation, with efforts to create more efficient, accessible, and scalable methods. Noteworthy papers in this area include: VisualSpeaker, which proposes a novel method for visually-guided 3D avatar lip synthesis, and MEDTalk, which presents a framework for multimodal controlled 3D facial animation with dynamic emotions. Additionally, the Spatial-Temporal Graph Mamba and Democratizing High-Fidelity Co-Speech Gesture Video Generation papers introduce innovative approaches to music-guided dance video synthesis and co-speech gesture video generation, respectively.