Few-Shot-Patch-Based-Training VS Deep-Image-Analogy

and here is the Code and weights: https://github.com/OndrejTexler/Few-Shot-Patch-Based-Training

PS: https://github.com/OndrejTexler/Few-Shot-Patch-Based-Training in case you havent taken a look, works like wonders.

Then why did you limit yourself in exactly the ways I described, by using the appropriate tools meant for video? Because it looked like shit until you pulled out ebsynth, right? Try this. It'll look even better and you won't have to deal with janky manual keyframe interpolation. That's the difference the right tool makes.

Interactive Video Stylization Using Few-Shot Patch-Based Training (Siggraph 2020) Project page: https://ondrejtexler.github.io/patch-based_training/index.html Git: https://github.com/OndrejTexler/Few-Shot-Patch-Based-Training

(1) i don't think there's much of a point in making formal comparisons between machine learning and human intelligence. it can be amusing to remark on machine learning applications producing human-like outputs, like artistic style transfer with image analogy. but ultimately, neural networks are just statistical models trained to provide particular outputs given a particular type of inputs. the framework certainly allows for a lot more flexibility and expressiveness than statistical models we have used in the past, but in those specific contexts, it is simply nothing compared to human intelligence and probably never will be.