A non-recursive encoder-dynamics-decoder network, fed with 3D joint coordinates rearranged into small pseudo-images, predicts future poses and outperforms two baselines on G3D and a filtered NTU dataset.
Frequency Domain Transformer Networks for Video Prediction
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abstract
The task of video prediction is forecasting the next frames given some previous frames. Despite much recent progress, this task is still challenging mainly due to high nonlinearity in the spatial domain. To address this issue, we propose a novel architecture, Frequency Domain Transformer Network (FDTN), which is an end-to-end learnable model that estimates and uses the transformations of the signal in the frequency domain. Experimental evaluations show that this approach can outperform some widely used video prediction methods like Video Ladder Network (VLN) and Predictive Gated Pyramids (PGP).
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PISEP^2: Pseudo Image Sequence Evolution based 3D Pose Prediction
A non-recursive encoder-dynamics-decoder network, fed with 3D joint coordinates rearranged into small pseudo-images, predicts future poses and outperforms two baselines on G3D and a filtered NTU dataset.