If we did a good job then the paper should be written in a way that is digestible. When you don't understand things, follow the references to learn more (and there's probably videos covering most of the components we use).
In the Appendix we have a case study that should be possible to re-implement and run with a single GPU/TPU. We are hoping the community can build from that and innovate. If you take these steps and get stuck, feel free to get in touch!
The bigger issue is lack of generating novel content rather than a total "unravel". We focus on OOD images because our motivation is generating diverse environments, but these are much harder to play for longer vs images closer to the training videos. It is interesting because one of the things you gain when going from 1B->10B is the OOD images working at all. Note it is not even trivial to detect the character given our model does not train with any labels or have any inductive biases to do so.
Point of clarification -- we don't expect bigger models to be the only way to improve this and are working on innovations on the modeling side, however we don't want to overlook the significance of scaling either :)
Hey author here :) First, tough crowd, love it, always great to get feedback because we are actively working on improving the model. We are very happy to admit it is not perfect, but given not many people thought this was possible a year ago, I am quite excited to see the next step of improvement. This is like the GPT1 of foundation world models, and we have a fair few ideas in the works to speed up progress.
The resolution is 90p but we use an upsampler to make it 360p for examples on the website.
Author here :) Re: 1) typically no, but of course it can hallucinate just like LLMs. 2) Agreed but the key point missing is Dreamer is trained from an RL environment with action labels. Genie is trained exclusively from videos and learns an action space. This is the first version of something that is now possible and will only improve with scale.