Motion is fundamental to how humans and dynamic objects interact with the world. Modeling motion is therefore essential for understanding dynamic phenomena, from reconstructing motion in real-world observations to generating plausible new motions. However, motion does not occur in isolation: it depends on an entity's physical properties, how it is observed, and how it interacts with others. Capturing these different forms of context remains a key challenge in motion modeling.

In this dissertation, we study motion modeling across humans and general dynamic objects. First, we introduce a text-driven human motion synthesis framework that jointly predicts body shape and motion, enabling shape-aware motion generation across different body morphologies. Second, we develop a template-free framework for reconstructing arbitrary dynamic objects from casual monocular videos, combining generative priors, object-centric camera estimation, and long-term point tracking. Finally, we extend motion modeling to multi-person interactions by constructing a large-scale dataset from sitcom videos and developing a text-conditioned motion generation framework that models spatial relationships and temporal structure to generate coordinated multi-person motions. Together, these works address complementary challenges in motion modeling, spanning body shape, dynamic 3D reconstruction, and human interactions.