text: Input. Unless stated otherwise, during during pretraining, we always randomly sample a clip of 16 frames from 
  each input video with a temporal stride of 4 between sampled frames. An input video clip therefore covers 64 frames 
  in total, or roughly 2 seconds of a given video running at 30 frames per second. We then resize the video’s spatial 
  dimensions to 224 
  3 for the entire clip. Since ViT networks 
  process a 1D sequence of tokens, we must convert an input video clip into a 1D token sequence. To do so, we apply a 
  3D convolution comprising d filters of size 2 
  16 with a temporal stride of 2 and a spatial stride of 16, resulting 
  d. Next we add absolute 3D sin-cos positional embeddings to the spatio-temporal 
  in a tensor of shape 8 
  feature map and flatten it, resulting in a 1D token sequence of shape 1568 
  d. This process is demonstrated in 
  Figure 7. 
  
  224, resulting in an overall shape of 16  224  224 
  ×  ×  ×  × 
  
  16 
  ×  × 
  14  14 
  ×  ×  × 
  × 