Not great, but getting better.
Some of it I look at and think, “with some smoothing, or normalization, this isn’t terrible”.
And then it just utterly fails on some sections of the video.
I’d say that’s because I don’t have enough similar data (blow job frames) in the training dataset, which is only 1,600 labelled frames across 129 videos.
All the labelling was done by me, and I’ve steered away from using funscript data as training material because they don’t necessarily match the video frame and because it is bad form to train ML on other people’s work, even if the result is freely available.
I’ve seen mention that the point isn’t to perfectly follow the motion and depth when scripting, but I’ve got no chance of training a soul into a ML model so I tried to keep the labels as accurate as possible with the thinking that, at best, it might help take some of the tedium out of scripting so that the people with the talent can answer some of the deeper questions in life like; “If it’s a handjob scene, and then hand rests at the base, what the hell should the script do if the hand stays on the base but she starts licking the tip???” 
I ran prediction for that video on cpu on a second PC. Python doesn’t thread good, so I just ran lots of them and join the result.
Prediction against that video took about an hour.
gif of the prediction smashing cpu https://www.redgifs.com/watch/navyagedvaquita.
This is a different model again from before, it’s 9 frames for inference but it’s the 5th frame that’s labelled. I didn’t use any zooming preprocessing model for training/prediction on this one, but might add that back in because a frame of video resized to 224x224 is almost impossible for a human to script well, let alone ML.
As before, the model and script is in the mega, this time it’s nested in the nineframe folder. The model is not something most people can pick up and do anything with without a python and ML background, and the funscripts in that folder are bad and will likely injure anyone that looks at them suggestively.