vibe_win_install
VIBE
vibe_win_install | VIBE | |
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1 | 5 | |
48 | 2,802 | |
- | - | |
0.0 | 0.0 | |
about 3 years ago | about 1 year ago | |
Python | Python | |
- | GNU General Public License v3.0 or later |
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vibe_win_install
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My experience using VIBE ( free motion extraction tool for animation )
So I spent the past few days trying to install this piece of software. There are some pretty detailed instructions that helped a great deal ( https://github.com/carlosedubarreto/vibe_win_install ), but there were some key steps that the author glossed over and had me wasting a great deal of time figuring out.
VIBE
- 3D reconstruction models for human body
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Any ghetto Mo'Cap solutions?
there are projects like this and similar, that are able to create motion capture from video: https://github.com/KevinLTT/video2bvh or https://github.com/mkocabas/VIBE
- My experience using VIBE ( free motion extraction tool for animation )
- Free deep learning tool for extracting animation from videos (link in post)
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Markerless Motion Capture - Turning Videos into 3D Animations
Another Edit: Here's another one that supports FBX export out of the box: https://github.com/mkocabas/VIBE
What are some alternatives?
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
video2bvh - Extracts human motion in video and save it as bvh mocap file.
b3d_mocap_import - addon for blender to import mocap data from tools like easymocap, frankmocap and Vibe
frankmocap - A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
smplx - SMPL-X
SMPL - NumPy, TensorFlow and PyTorch implementation of human body SMPL model and infant body SMIL model.
kapao - KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.
manydepth - [CVPR 2021] Self-supervised depth estimation from short sequences