DI-star
RobustVideoMatting
DI-star | RobustVideoMatting | |
---|---|---|
9 | 16 | |
1,162 | 8,176 | |
1.2% | - | |
3.3 | 0.0 | |
10 months ago | 30 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
DI-star
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Better AI ?
There is an AI/bot scene for SC2, I don't have many links but you can start by looking here: https://github.com/opendilab/DI-star https://www.youtube.com/watch?v=fvQF-24IpXs (Harstem and uThermal both have more videos vs different bots).
- [ENG] 2022 GSL S3 Code S RO.20 Group B
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Any idea about DI-star ? It's an AI model could beat top human players in StarCraft II!
Looks like a simplified AlphaStar using LSTM RNN instead of Pointer Transformer, much heavier supervised imitation learning, Zerg vs Zerg only (with simplified build order module), and a much smaller AlphaStar League: https://github.com/opendilab/DI-star/blob/main/docs/guidance_to_small_scale_training.md
For more information,plz visit out GitHub page:https://github.com/opendilab/DI-star
- Any idea about DI-star?An AI model could beat top human players in StarCraftII
- A large-scale game AI distributed training platform developed for StarCraftII
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Why can't we make a perfect AI for Starcraft through evolution
First of all, let's discuss what the level of AI is now. If the "level" refers to the capability of competing, the current AI has been very closed to the top human player in some types of games, like chess, Texas Poker, and Mahjong of CARDS, DOTA2 of MOBA, as well as StarCraft2 of RTS. As for other games, if we have enough human resources and computing performance, we also can get similar results. If the "level" has other meanings, like AI agents having human behavior, intelligent NPC can be designed specifically for different people so that they can have different gaming experience. These are all at the stage of issue-defining and exploring new technology solutions. Although traditional game AI is mostly based on hard code, it still has much prior knowledge. In recent years, some hot ML-related techs have performed well in competitiveness while in other fields, they haven't found the perfect entry point. If we expand the conclusions above in detail, the design of game AI can be divided into two parts: issue defining and issue solving. For those competitive issues which have already got complete definitions, their core issue is to explore the optimal strategy based on the evaluation standard, like ladder points. Traditional solutions can deal with less complex scenarios, like chess and Gobang. While machine learning related techs, including deep learning and reinforcement learning, they can perform very well in much more complex games, like StarCraft II. {For this you can try it in DI-star: this project is a reimplementation (with a few improvements) of Alphastar (Only Zerg vs Zerg) based on OpenDILab.}
- Show HN: Come and fight professional AI in StarCraftII
- DI-Star (Starcraft 2 AI, Continuation of AlphaStar)
RobustVideoMatting
- lineart_coarse + openpose, batch img2img
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Tools For AI Animation and Filmmaking , Community Rules, ect. (**FAQ**)
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--------
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Adobe After Effects VS Runway AI 👀
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.
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Rotoscoping a video by comparing images
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
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CatFileCreator in Nuke
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
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[Q] Video Editing using AI
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)
- Is this FOSS ML software safe?
- [D] Is this ML project safe?
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Trying to train videomatting model
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?
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Remove Background NO GREENSCREEN?
I have found a github with a project like this but it is tedious to use: https://github.com/PeterL1n/RobustVideoMatting
What are some alternatives?
pytorch-lightning - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. [Moved to: https://github.com/PyTorchLightning/pytorch-lightning]
MODNet - A Trimap-Free Portrait Matting Solution in Real Time [AAAI 2022]
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
BackgroundMattingV2 - Real-Time High-Resolution Background Matting
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
Kornia - Geometric Computer Vision Library for Spatial AI
pytorch-deep-image-matting - Pytorch implementation of deep image matting
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
coremltools - Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
keras-onnx - Convert tf.keras/Keras models to ONNX