Action-Reaction: Forecasting the Dynamics of Human Interaction

By De-An Huang, Kris M. Kitani. 2014

130 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review (2018, background): “, 2015), and forecasting of human behavior (Huang and Kitani, 2014; Huang et al., 2015).”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Huang and Kitani [168] Cost Topology Stat Lowlv Hand Devanne et al.”
Unsupervised Learning for Physical Interaction through Video Prediction (2016, background): “Learning physics: Several works have explicitly addressed prediction of physical interactions, including predicting ball motion [5], block falling [2], the effects of forces [19, 18], future human interactions [9], and future car trajectories [26].”
An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders (2016, methodology): “Finally, supervised learning has also been used to model and forecast labeled human-human [2,16], and human-object [17,18] interactions.”
Dense Optical Flow Prediction from a Static Image (2015, background): “But recent approaches have argued for much richer form of pre- dictions even in terms of pixels [24] or the features of the next frame [7, 19].”
Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video (2020, background): “Huang and Kitani [13] explored the task of action anticipation in the context of dual-agent interactions, where the actions of an agent are used to predict the response of the other agent.”
Anticipating the future by watching unlabeled video (2015, background): “Pioneering work has forecasted high level semantics before [17, 21, 13], but they have required supervision, which makes it difficult to scale.”
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction (2020, unknown): “The related techniques can be widely applied to many computer vision and robotics scenarios, such as human-computer interaction [24, 23, 17, 13], autonomous driving [6], and pedestrian tracking [1, 15, 3].”
Simultaneous Feature and Body-Part Learning for real-time robot awareness of human behaviors (2017, methodology): “Several methods were also proposed to select more descriptive human body joints [2], [10], [11], [20], [21], [22], [23].”
Anticipating Visual Representations from Unlabeled Video (2015, background): “Methods that anticipate concepts have typically required supervision [16, 21, 12], which is expensive to scale.”
Forecasting Interactive Dynamics of Pedestrians with Fictitious Play (2016, background): “There has been growing interest in developing computational models of human activities that can extrapolate unseen information and predict future unobserved activities [34, 38, 22, 33, 18, 42, 40, 37, 15, 14, 16, 17, 35, 9, 49, 43, 36, 32].”
Grounded Human-Object Interaction Hotspots From Video (2018, methodology): “Future prediction has been applied to action anticipation [26, 55, 32, 44], active-object forecasting [16], and to guide demonstration learning in robotics [13, 14, 11].”
Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions (2015, methodology): “For the third baseline, we quantize the continuous state space into K discrete state by k-means clustering and apply the discrete Markov decision process (DMDP) inference used in (Kitani et al. 2012).”
Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos (2017, methodology): “There have been a few works which predict the label of the future unobserved activity such as approaches using semantic scene labeling [19], Probabilistic Suffix Tree (PST) [23], augmentedHidden Conditional Random Field (a-HCRF) [44], Markov Random Field (MRF) [3], kernel-based reinforcement learnin”
Learning Temporal Dynamics from Cycles in Narrated Video (2021, background): “Rather than predicting pixels, another line of work uses supervision to predict future action labels [19, 24, 28, 36, 45].”
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions (2019, background): “Beyond body pose, there is a growing interest in modeling human-human interactions [57, 23, 40] to predict pedestrian trajectories [1, 43, 2], analyze social behavior and group activities [43, 59, 6, 15, 23], and understand human-object interactions [61, 19, 12].”