talking-head-anime-3-demo
Activeloop Hub
talking-head-anime-3-demo | Activeloop Hub | |
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4 | 31 | |
879 | 4,807 | |
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0.0 | 9.9 | |
9 months ago | over 1 year ago | |
Python | Python | |
MIT License | Mozilla Public License 2.0 |
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talking-head-anime-3-demo
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What would I use to create a chatbot but with an animated face of a man i created with Stable Diffusion - and also do it from my local pc, not any other websites etc. Anyone have any ideas on this?
Talking Head. This is the visual of converting the image into an animated character. This is honestly the hard part. There's vmagicmirror for 3d model vrm/vroid characters. And for anime characters there's talking head anime 3 if you modify it a bit. But other than that there's basically nothing. Some online services can do it for you but not local/offline.
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100% AI including verse, photo, outpainting, animation, voice, and lip syncing
I'm currently using a modified version of this repo which does face/head posing for anime characters. Works okayish and I can run it around 18fps or so which is decent. Seems to be the best thing available atm. not really accurate lipsync (you have to manually enter mouth position) but I mapped "a" viseme to volume and it works well enough lol.
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Is there a way to do facial rigs on AI images?
EasyVTuber is based off of Talking Head which has the ability to animate the mouth, eyes, and even the eyebrows with slight head turning and breathing with TrueDepth facial tracking (iPhone): https://github.com/pkhungurn/talking-head-anime-3-demo
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Can't find root directory, help?
https://github.com/pkhungurn/talking-head-anime-3-demo I keep trying to get the above program to work, but I can't find the the repository's root directory so I can unzip the model files into the data/models folder. How do I find the repository's root directory?
Activeloop Hub
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[Q] where to host 50GB dataset (for free?)
Hey u/platoTheSloth, as u/gopietz mentioned (thanks a lot for the shout-out!!!), you can share them with the general public through uploading to Activeloop Platform (for researchers, we offer special terms, but even as a general public member you get up to 300GBs of free storage!). Thanks to our open source dataset format for AI, Hub, anyone can load the dataset in under 3seconds with one line of code, and stream it while training in PyTorch/TensorFlow.
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[D] NLP has HuggingFace, what does Computer Vision have?
u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :)
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[N] [P] Access 100+ image, video & audio datasets in seconds with one line of code & stream them while training ML models with Activeloop Hub (more at docs.activeloop.ai, description & links in the comments below)
u/gopietz good question. htype="class_label" will work, but querying doesn't support multi-dimensional labels yet. Would you mind opening an issue requesting that feature?
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Easy way to load, create, version, query and visualize computer vision datasets
Hi HN,
In machine learning, we are faced with tensor-based computations (that's the language that ML models think in). I've recently discovered a project that helps you make it much easier to set up and conduct machine learning projects, and enables you to create and store datasets in deep learning-native format.
Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as TensorFlow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai). The real benefit for me is that, I can stream my datasets without the need to store them on my machine (my datasets can be up to 10GB+ big, but it works just as well with 100GB+ datasets like ImageNet (https://docs.activeloop.ai/datasets/imagenet-dataset), for instance).
Hub allows us to store images, audio, video data in a way that can be accessed at lightning speed. The data can be stored on GCS/S3 buckets, local storage, or on Activeloop cloud. The data can directly be used in the training TensorFlow/ PyTorch models so that you don't need to set up data pipelines. The package also comes with data version control, dataset search queries, and distributed workloads.
For me, personally the simplicity of the API stands out, for instance:
Loading datasets in seconds
import hub ds = hub.load("hub://activeloop/cifar10-train")
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Easy way to load, create, version, query & visualize machine learning datasets
Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as Tensorflow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai/3)
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Datasets and model creation flow
Consider this
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[P] Database for AI: Visualize, version-control & explore image, video and audio datasets
Please take a look at our open-source dataset format https://github.com/activeloopai/hub and a tutorial on htypes https://docs.activeloop.ai/how-hub-works/visualization-and-htype
I'm Davit from Activeloop (activeloop.ai).
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The hand-picked selection of the best Python libraries released in 2021
Hub.
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What are good alternatives to zip files when working with large online image datasets?
What solution have you used that you like as a data scientist when working with large datasets? Any standard python API to access the data? Other solution? If anyone has used https://github.com/activeloopai/Hub or other similar API I'd be interested to hear your experience working with it!