openpose
lightweight-human-pose-estimation.pytorch
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| openpose | lightweight-human-pose-estimation.pytorch | |
|---|---|---|
| 36 | 2 | |
| 29,729 | 2,017 | |
| 1.1% | - | |
| 5.2 | 0.0 | |
| about 1 month ago | 28 days ago | |
| C++ | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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openpose
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AI "Artists" Are Lazy, and the Ultimate Goal of AI Image Generation (hint: its sloth)
Open Pose, a multi-person keypoint detection library for body, face, hands, and foot estimation [10], is used for posing generated characters;
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Analyze defects and errors in the created images
OpenPose
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[D] Which open source models can replicate wonder dynamics's drag'n'drop cg characters?
Perhaps something like OpenPose for pose estimation?
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Do we have Locally Run AI mocap yet?
OpenPose looks like what you're looking for, it seems to have plugins for Unity. I can't say I've used it though.
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Accelerate Machine Learning Local Development and Test Workflows with Nvidia Docker
FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04 # https://hub.docker.com/r/nvidia/cuda ENV DEBIAN_FRONTEND=noninteractive # install the dependencies for building OpenPose RUN apt-get update && # The rest is ignored for brevity. RUN pip3 install --no-cache-dir # The rest is ignored for brevity. # install cmake, clone OpenPose and download models RUN wget https://cmake.org/files/v3.20/cmake-3.20.2-linux-x86_64.tar.gz && \ # The rest is ignored for brevity. WORKDIR /openpose/build RUN alias python=python3 && cmake -DBUILD_PYTHON=OFF -DWITH_GTK=OFF -DUSE_CUDNN=ON .. # Build OpenPose. Cudnn 8 causes memory issues this is why we are using base with CUDA 10 and Cudnn 7 # Fix for CUDA 10.0 and Cudnn 7 based on the post below. # https://github.com/CMU-Perceptual-Computing-Lab/openpose/issues/1753#issuecomment-792431838 RUN sed -ie 's/set(AMPERE "80 86")/#&/g' ../cmake/Cuda.cmake && \ sed -ie 's/set(AMPERE "80 86")/#&/g' ../3rdparty/caffe/cmake/Cuda.cmake && \ make -j`nproc` && \ make install WORKDIR /openpose
- nub needs some directions
- How to get rotation (yaw/pitch/roll) from face detection keypoints?
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Help finding an appropriate model for human pose estimation
Openpose: This is supposedly realtime (I assume on a gpu, 24fps?) and they provide training code
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We built a free, open-source markerless motion capture system during the pandemic. This animation was created with 4x $20US webcams and a gaming PC, details in the comments [OC]
The pose tracking OP’s using relies on openpose - if you look at the GIFs in their readme, they appear to track individual fingers with fairly high resolution, so I’d imagine it would be fairly straightforward to map that to fret positions.
while I congratulate you for putting together a pipeline, I think it has to be said that the basis of this - the very powerful openpose - has a license that is not completely FOSS. It might be open source but is definitely not free as free beer. So if you consider using this for a fancy project (i.e. marketing an app), you have to get in touch with university of california first https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE
lightweight-human-pose-estimation.pytorch
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Help finding an appropriate model for human pose estimation
Lightweight OpenPose: Runs in realtime >20fps confirmed, training code is provided
What are some alternatives?
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
MocapNET - We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance
freemocap - Free Motion Capture for Everyone 💀✨
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
VIBE - Official implementation of CVPR2020 paper "VIBE: Video Inference for Human Body Pose and Shape Estimation"
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans