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DESIRE: Distant Future Prediction in Dynamic Scenes With Interacting Agents
CVPR 2017 | We introduce a deep stochastic IOC RNN encoder-decoder framework, DESIRE, for the task of future prediction of multiple interacting agents in dynamic scenes. It produces accurate future predictions by tackling multi-modality of futures while accounting for a rich set of both static and dynamic scene contexts. It generates a diverse set of hypothetical prediction samples and then ranks and refines them through a deep IOC network.
Collaborators: Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker
