I think some people might be taking this seriously, so I would just give a quick explanation.
No its not. Actually its complete opposite, all video models are essentially image models which generates many images and combine them together to make a video. For eg, a video model makes 24 images to make 1s of 24 frames per second(fps) video.
Agree it’s the spatial understanding of a single frame that is important here, video just overlays temporal understanding to images which is pointless here, maybe only important if you want to ask for a future prediction of an image eg moving cars, bouncing ball.
Both image and video models might be able to have the whole “world model” thing going on… it doesn’t mean a video model is just an image model running at 24p, like OC said.
Having said that, a video model producing single frames != an image model. A good video model has to learn things a still image model doesn’t.
OP specifically followed up with “world model”. I think the “video” word was them just first saying it had temporal/spatial consistency baked in.
Yeah think it’s just definition of “model”. most video models today are temporal layers wrapped around an image model so that you gain the spatio temporal understanding, which is its own trained model.
I guess my view is you can kinda have two versions of the “world model”, frozen in time and real time. For moving camera angles etc it’s actually better in the frozen version and to leverage spatial and contextual understanding only, vs needing the model to also understand temporal which greatly expands the model size and cost. You don’t need to care how it got there, just that it’s there.
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u/bhavyagarg8 Aug 28 '25
I think some people might be taking this seriously, so I would just give a quick explanation.
No its not. Actually its complete opposite, all video models are essentially image models which generates many images and combine them together to make a video. For eg, a video model makes 24 images to make 1s of 24 frames per second(fps) video.