The field of human motion synthesis and 3D scene generation is rapidly advancing, with a focus on developing more realistic and diverse models. Recent research has explored the use of deterministic-to-stochastic latent feature mapping, training-free scene-aware text-to-motion generation, and generalist models that can handle multiple tasks within a single framework. These innovations have improved the accuracy and efficiency of human motion synthesis, enabling the generation of more realistic and diverse motion sequences. Additionally, advances in 3D scene generation have led to the development of more realistic and interactive environments, with applications in fields such as gaming, virtual reality, and urban planning. Notable papers include GENMO, which presents a unified generalist model for human motion estimation and generation, and Scenethesis, which introduces a training-free agentic framework for 3D scene generation. Overall, these developments are poised to have a significant impact on the field, enabling the creation of more realistic and engaging virtual environments.