lightweight-human-pose-estimation.pytorch
openpose
| lightweight-human-pose-estimation.pytorch | openpose | |
|---|---|---|
| 2 | 36 | |
| 2,023 | 29,902 | |
| - | 0.9% | |
| 2.5 | 5.1 | |
| 6 days ago | 21 days ago | |
| Python | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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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
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How do I properly dissect a Github repo of a ML model?
Using https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch as an example (or another repo, that was just one I found), could someone please give me a step by step process of how they read a repo for a research paper?
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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Let's take a break!
You are correct. Open Pose has two keypoints for the eyes and two more for the ears. By saying were the ears are you automatically influence the angle of the head. You can see more about it on this github page. Just scroll a tiny bit and you can see a gif of the nodes overlapped on humans
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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
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full body tracking with WiFi signals by utilizing deep learning architectures
One of the best cam only libraries (no depth sensor) I've seen is openpose, I ran it through a 360 camera and it was able to track body, face, and fingers really well even with spherical distortion from the 360 cam. example 360
- How to do body tracking for (real) camera
- How to get rotation (yaw/pitch/roll) from face detection keypoints?
What are some alternatives?
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.
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.
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