mediapipe
openpose
Our great sponsors
mediapipe | openpose | |
---|---|---|
49 | 36 | |
25,140 | 29,627 | |
2.5% | 1.4% | |
9.9 | 5.2 | |
5 days ago | 12 days ago | |
C++ | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mediapipe
-
MEDIAPIPE on-device diffusion plugins for conditioned text-to-image generation
Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.
-
Running a TensorFlow object detector model and drawing boxes around objects at 60 FPS - all in React Native / JavaScript!
You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md
-
Open source Background Remover: Remove Background from images and video using AI
I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].
For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM
[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...
[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."
[3] - https://developers.google.com/mediapipe/solutions/vision/ima...
-
Getting face feature pose statistics
I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.
-
New ControlNet Face Model
We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.
-
Trained an ML model using TensorFlow.js to classify American Sign Language (ASL) alphabets on browser. We are creating an open-source platform and would love to receive your feedback on our project.
Medipaipe library link: https://mediapipe.dev/
-
mediapipe VS daisykit - a user suggested alternative
2 projects | 24 Mar 2023
- Google Summer of code 2023 is coming
-
10$ Full Body Tracking! I'm proud to release ToucanTrack (in Beta!). Get decent FBT with the power of 2 PS3 Eye Cameras and AI!
If you're looking for the differences in terms of how inference is done, I recommend you take a look at MediaPipe's source code. MediaPipe doesn't use raw code, but uses a "graph" instead (eg. pose_landmark_cpu.pbtxt), which can be visualised using MediaPipe Viz. I also used axinc-ai/ailia-models as the base (preprocessing, inference, postprocessing, etc...) which I further built upon (keypoint refinement, roi from keypoints, filtering / smoothing, etc...)
-
Started working on this motion tracking prototype demo game in python and Unity!
I thought of doing that but unfortunately medipipe requires a RGB input and performs better with it more on that here
openpose
-
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;
-
Analyze defects and errors in the created images
OpenPose
-
[D] Which open source models can replicate wonder dynamics's drag'n'drop cg characters?
Perhaps something like OpenPose for pose estimation?
-
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.
-
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?
-
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
-
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
What are some alternatives?
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
ue4-mediapipe-plugin - UE4 MediaPipe plugin
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.
lightweight-human-pose-estimation.pytorch - Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
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
jeelizFaceFilter - Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D...).
freemocap - Free Motion Capture for Everyone 💀✨