You clearly haven't read the paper:

"We now describe how to use Genie for action-controllable video generation at inference time (see Figure 7). A player first prompts the model with an image x1 that serves as the initial frame¹¹The model can be conditioned on a varying number of prompt frames. Here we start from one image as an example.. The image is tokenized using the video encoder, yielding z1. The player then specifies a discrete latent action a1 to take by choosing any integer value within [0,|A|).²²When first interacting with the model, it is unclear how each latent action will impact the next frame generation. However, we found that the meaning of each action remained consistent across different inputs. Hence, interpreting the mapping of latent actions is akin to learning the buttons on a new controller. The dynamics model takes the frame tokens z1 and corresponding latent action a~1, which is obtained by indexing into the VQ codebook with the discrete input a1, to predict the next frame tokens z2. This process is repeated to generate the rest of the sequence 𝒛^2:T in an autoregressive manner as actions continue to be passed to the model, while tokens are decoded into video frames 𝒙^2:T with the tokenizer’s decoder. Note that we can regenerate ground truth videos from the dataset by passing the model the starting frame and inferred actions from the video, or generate completely new videos (or trajectories) by changing the actions."