  The unsupervised Pretraining method has been widely used in aiding human
action recognition. However, existing methods focus on reconstructing the
already present frames rather than generating frames which happen in future.In
this paper, We propose an improved Variantial Autoencoder model to extract the
features with a high connection to the coming scenarios, also known as
Predictive Learning. Our framework lists as following: two steam 3D-convolution
neural networks are used to extract both spatial and temporal information as
latent variables. Then a resample method is introduced to create new normal
distribution probabilistic latent variables and finally, the deconvolution
neural network will use these latent variables generate next frames. Through
this possess, we train the model to focus more on how to generate the future
and thus it will extract the future high connected features. In the experiment
stage, A large number of experiments on UT and UCF101 datasets reveal that
future generation aids Prediction does improve the performance. Moreover, the
Future Representation Learning Network reach a higher score than other methods
when in half observation. This means that Future Representation Learning is
better than the traditional Representation Learning and other state- of-the-art
methods in solving the human action prediction problems to some extends.
