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The objective of this work is human pose estimation in videos, where multipleframes are available. We investigate a ConvNet architecture that is able tobenefit from temporal context by combining information across the multipleframes using optical flow.  To this end we propose a network architecture with the following novelties:(i) a deeper network than previously investigated for regressing heatmaps; (ii)spatial fusion layers that learn an implicit spatial model; (iii) optical flowis used to align heatmap predictions from neighbouring frames; and (iv) a finalparametric pooling layer which learns to combine the aligned heatmaps into apooled confidence map.  We show that this architecture outperforms a number of others, including onethat uses optical flow solely at the input layers, one that regresses jointcoordinates directly, and one that predicts heatmaps without spatial fusion.  The new architecture outperforms the state of the art by a large margin onthree video pose estimation datasets, including the very challenging Poses inthe Wild dataset, and outperforms other deep methods that don't use a graphicalmodel on the single-image FLIC benchmark (and also Chen & Yuille and Tompson etal. in the high precision region).

Flowing ConvNets for Human Pose Estimation in Videos