MotionBERT
MotioNet
| MotionBERT | MotioNet | |
|---|---|---|
| 2 | 1 | |
| 1,148 | 565 | |
| 2.1% | 0.0% | |
| 2.5 | 0.0 | |
| 2 months ago | almost 3 years ago | |
| Python | Python | |
| Apache License 2.0 | BSD 2-clause "Simplified" License |
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MotionBERT
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How to transmit custom signal to vrchat world (AI guided 3d keypoints)
Thank you for answer. The method I am using is one of the monocular 3d pose estimation studies, and is as follows. https://github.com/Walter0807/MotionBERT
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Code and models now available for 'MotionBERT: Unified Pretraining for Human Motion Analysis'
Code: https://github.com/Walter0807/MotionBERT
MotioNet
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Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
They could be a great application for niches, though. Prototyping 3d games or movies can be helped greatly by such techniques.
If you can take a photo (effort required: seconds) and turn it into a textures low- to mid-poly model within seconds, that's a huge time saver right there.
Combined with other techniques [1], [2] this could indeed present a viable business model - again for certain niches. It's the same old routine: identify potential, present solution, profit (if only for a limited time). On its own this might not look too useful/impressive, but I'm certain there's people out there who could already benefit greatly from this work.
It's not always about "the next big thing" or "turning an entire industry on its head". I could see this work its way into various products without much fanfare and gazillion dollar valuations.
[1] https://github.com/Shimingyi/MotioNet
[2] https://deepai.org/publication/end-to-end-learning-for-3d-fa...
What are some alternatives?
UNet - Network system for VRChat UDON
awesome-virtual-try-on - A curated list of awesome research papers, projects, code, dataset, workshops etc. related to virtual try-on.
ShaderMotion
deep-motion-editing - An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]
StyleDomain - Official Implementation for "StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation" (ICCV 2023)
Meshroom - 3D Reconstruction Software