How to run a saved TensorFlow Model? (Video Prediction Model)
The TensorFlow saver is used to save the weights of a specific model at some given point. When you want to use a trained model, you must first define the model's architecture (which should be similar to the one used for saving the weights), then you can use the same "saver" class to restore the weights:

with tf.Session() as sess:
    # Restore variables from disk.
    saver.restore(sess, "../my_saved_model.ckpt")


Regarding your initial question. I think that if you are just starting with deep learning and TensorFlow, this is the wrong place to start and you should first understand how TensorFlow works in general by applying it at easier tasks like image classification (start with MNIST).

From what I understand, you need to use the "construct_model" function and pass it your initial image sequence (video) and some action tensor, and it should output a predicted frames:

def construct_model(images,
                actions=None,
                states=None,
                iter_num=-1.0,
                k=-1,
                use_state=True,
                num_masks=10,
                stp=False,
                cdna=True,
                dna=False,
                context_frames=2):
"""Build convolutional lstm video predictor using STP, CDNA, or DNA.
  Args:
    images: tensor of ground truth image sequences
    actions: tensor of action sequences
    states: tensor of ground truth state sequences
    iter_num: tensor of the current training iteration (for sched. sampling)
    k: constant used for scheduled sampling. -1 to feed in own prediction.
    use_state: True to include state and action in prediction
    num_masks: the number of different pixel motion predictions (and
           the number of masks for each of those predictions)
    stp: True to use Spatial Transformer Predictor (STP)
    cdna: True to use Convoluational Dynamic Neural Advection (CDNA)
    dna: True to use Dynamic Neural Advection (DNA)
    context_frames: number of ground truth frames to pass in before
                feeding in own predictions
  Returns:
    gen_images: predicted future image frames
    gen_states: predicted future states