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3D human pose estimation in video with temporal convolutions and semi-supervised training
Dario Pavllo, Christoph Feichtenhofer, David Grangier +1
In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints. We also…
Scaling Neural Machine Translation
Myle Ott, Sergey Edunov, David Grangier +1
Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that redu…
QuaterNet: A Quaternion-based Recurrent Model for Human Motion
Dario Pavllo, David Grangier, Michael Auli
Deep learning for predicting or generating 3D human pose sequences is an active research area. Previous work regresses either joint rotations or joint positions. The former strateg…
Analyzing Uncertainty in Neural Machine Translation
Myle Ott, Michael Auli, David Grangier +1
Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these model…