Human motion trajectory prediction: A survey
With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …
of such systems to perceive, understand, and anticipate human behavior becomes …
An algorithmic perspective on imitation learning
As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …
complex, unstructured settings, manually programming their behavior has become …
Ego4d: Around the world in 3,000 hours of egocentric video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …
Socratic models: Composing zero-shot multimodal reasoning with language
Large pretrained (eg," foundation") models exhibit distinct capabilities depending on the
domain of data they are trained on. While these domains are generic, they may only barely …
domain of data they are trained on. While these domains are generic, they may only barely …
Social gan: Socially acceptable trajectories with generative adversarial networks
Understanding human motion behavior is critical for autonomous moving platforms (like self-
driving cars and social robots) if they are to navigate human-centric environments. This is …
driving cars and social robots) if they are to navigate human-centric environments. This is …
Observation-centric sort: Rethinking sort for robust multi-object tracking
Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that
objects move linearly. While this assumption is acceptable for very short periods of …
objects move linearly. While this assumption is acceptable for very short periods of …
Social lstm: Human trajectory prediction in crowded spaces
Humans navigate complex crowded environments based on social conventions: they
respect personal space, yielding right-of-way and avoid collisions. In our work, we propose a …
respect personal space, yielding right-of-way and avoid collisions. In our work, we propose a …
Learning spatiotemporal features with 3d convolutional networks
We propose a simple, yet effective approach for spatiotemporal feature learning using deep
3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised …
3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised …
Merlot: Multimodal neural script knowledge models
As humans, we understand events in the visual world contextually, performing multimodal
reasoning across time to make inferences about the past, present, and future. We introduce …
reasoning across time to make inferences about the past, present, and future. We introduce …
Tnt: Target-driven trajectory prediction
Predicting the future behavior of moving agents is essential for real world applications. It is
challenging as the intent of the agent and the corresponding behavior is unknown and …
challenging as the intent of the agent and the corresponding behavior is unknown and …