Abstract: Simulating human behavior and interactions within various environments is crucial for numerous applications, including generating training data for machine learning algorithms, creating autonomous agents for interactive applications like augmented and virtual reality (AR/VR) or computer games, guiding architectural design decisions, and more. In this talk, I will discuss our previous and ongoing research efforts dedicated to modeling and synthesizing digital humans, with the ultimate goal of enabling them to exhibit spontaneous behavior and move autonomously in a digital environment. A key aspect of our work, which I will highlight during the talk, is the development of the Guided Motion Diffusion model. This approach generates high-quality and diverse human motion based on textual prompts and spatial constraints, such as motion trajectories and obstacles. Through a detailed exploration of our research, I will illustrate how these techniques can be applied to various scenarios, ultimately enriching and enhancing the realism of digital human behaviors across multiple domains.