gretel-synthetics VS RobustVideoMatting

Hey there! Co-founder of Gretel.ai here, and I think I can provide some insights on this topic.

Firstly, the concept you're hinting at is not purely traditional ML. In traditional machine learning, we often prioritize feature extraction and engineering specific to a given problem space before training.

What you're describing and what we've been working on at Gretel.ai, is leveraging the power of models like Large Language Models (LLMs) to understand and extrapolate from vast amounts of diverse data without the need for time-consuming feature engineering. Here's a link to our open-source library https://github.com/gretelai/gretel-synthetics for synthetic data generation (currently supporting GAN and RNN-based language models), and also our recent announcement around a Tabular LLM we're training to help people build with data https://gretel.ai/tabular-llm

A few areas where we've found tabular or Large Data Models to be really useful are:

you can try QuantGAN: https://github.com/PakAndrey/QuantGANforRisk also try DoppelGANger https://github.com/gretelai/gretel-synthetics/tree/master/src/gretel_synthetics/timeseries_dgan

Robust Video Matting/Background Remover (Remove Background from images and videos, useful for compositing) https://github.com/PeterL1n/RobustVideoMatting (RVM - Remove backgrounds from videos) https://github.com/nadermx/backgroundremover (BackgroundRemover - works well on single images) -------VOICE GENERATION--------

Looks like runway is packaging a bunch of AI tools like stable diffusion and other opensource tools into a paid package. The matting tools it is using looks like this tool https://github.com/PeterL1n/RobustVideoMatting which can be run off your computer for free if you can figure out the geeky side of installing this stuff. I've tried it out and it sometimes works well but most of the time the results aren't as good as the examples on their github. Still a good tool to have in the toolbox though.

OR this separate application looks promising, if you can work out Google Collab (I couldn't unfortunately): https://github.com/PeterL1n/BackgroundMattingV2 https://github.com/PeterL1n/RobustVideoMatting

I have done a bit of coding and I will use pretrained models only. Looking at things like depth and segmentation. Like this as an example. I am using it on a collab now but its so cumbersome. https://github.com/PeterL1n/RobustVideoMatting

I do not know much about Machine learning, and I am not sure if I can ask question here. But if yes, I need help with either choosing best libraries to do Video Editing like Background Removal and similar. Some of the ones that I found is RVM: https://github.com/PeterL1n/RobustVideoMatting (which currently seems like the best choice)

First of all I would ask if somebody retrained Robust Video Matting model on own data? I am trying to, but with all the models I end up getting bad quality result as the ones attached to the post. So my data is some objects rotating on 360 and with white backgrounds, The task seems to be pretty simple as the model just has to remove white bgr and keep colorized object. I have masks on every 10th frame of my videos. The masks are 0 - bgr, 255 - fgr. I have tried Robust Video Matting model, MODNet, PaddleSeg and several segmentation models and every of them failed to show consistent results on that data. What should I do in the case?

I have found a github with a project like this but it is tedious to use: https://github.com/PeterL1n/RobustVideoMatting