3 accepted papers
Modeling the nuanced, multimodal nature of human driving remains a core challenge for autonomous systems, as existing methods often fail to capture the diversity of plausible behaviors in complex real-world scenarios. In this work, we introduce a novel benchmark and end-to-end planner for modeling r…
We introduce Motion Diversification Networks a novel framework for learning to generate realistic and diverse 3D human motion. Despite recent advances in deep generative motion modeling existing models often fail to produce samples that capture the full range of plausible and natural 3D human motion…
People who are blind perceive the world differently than those who are sighted, which can result in distinct motion characteristics. For instance, when crossing at an intersection, blind individuals may have different patterns of movement, such as veering more from a straight path or using touch-bas…