2 research outputs found
Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision
We tackle the problem of Human Locomotion Forecasting, a task for jointly
predicting the spatial positions of several keypoints on the human body in the
near future under an egocentric setting. In contrast to the previous work that
aims to solve either the task of pose prediction or trajectory forecasting in
isolation, we propose a framework to unify the two problems and address the
practically useful task of pedestrian locomotion prediction in the wild. Among
the major challenges in solving this task is the scarcity of annotated
egocentric video datasets with dense annotations for pose, depth, or egomotion.
To surmount this difficulty, we use state-of-the-art models to generate (noisy)
annotations and propose robust forecasting models that can learn from this
noisy supervision. We present a method to disentangle the overall pedestrian
motion into easier to learn subparts by utilizing a pose completion and a
decomposition module. The completion module fills in the missing key-point
annotations and the decomposition module breaks the cleaned locomotion down to
global (trajectory) and local (pose keypoint movements). Further, with Quasi
RNN as our backbone, we propose a novel hierarchical trajectory forecasting
network that utilizes low-level vision domain specific signals like egomotion
and depth to predict the global trajectory. Our method leads to
state-of-the-art results for the prediction of human locomotion in the
egocentric view. Project pade: https://karttikeya.github.io/publication/plf/Comment: Accepted to WACV 2020 (Oral