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Being able to “look into the future” is a remarkable cognitive hallmark of humans. For example, humans can naturally anticipate how people move or act in the near future, based on their historical movements, even in a complex real-world scenario in the wild, which poses a critical challenge for machines to replicate. On the contrary, the state-of-the-art human motion forecasting method often focuses on simplified scenarios, eg, predicting future motion of a single person in a deterministic way. This thesis endeavors to develop novel techniques that enable machines to anticipate human motion while considering real-world complexities. Our work is grounded in two fundamental insights: First, human motion prediction inherently involves uncertainty and multi-modality, especially in long-term forecasting. Second, such uncertainty does not suggest complete randomness in human movements; instead, they are highly dependent on the environment and its changes. To tackle these challenges, we integrate diverse generation and environment-aware prediction into various scenarios. We commence by investigating the prediction of diverse single-person motion. Our key insight is that future human motions are not completely random or independent, but rather exhibit deterministic properties consistent with physical laws and constraints. Based on this observation, we propose anchor-based representations that encode human motion in the latent space using deterministic and learnable components. These anchors have been trained to specialize and diversify for different modes of future motion, enabling us to generate more diverse and accurate …