Track-Anything
openpose
Track-Anything | openpose | |
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16 | 36 | |
6,113 | 29,902 | |
- | 0.9% | |
8.1 | 5.1 | |
3 months ago | 23 days ago | |
Python | C++ | |
MIT License | 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.
Track-Anything
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Keying/masking person on a footage
I was doing rotoscoping for a silhouette of a girl dancing in front of a building, then I saw this amazing tool: https://github.com/gaomingqi/Track-Anything
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Advice for multi-animal tracking for scientific research?
My question is, how can we modernize this pipeline? We've experimented a bit with the new SAM-based track-anything tool, and it seems promising, but we actually don't want to "track anything", we only want to track fishes. What would you do in 2023, to extract tracks of one specific class of object from long video datasets? I'm hoping for any advice at all.
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[D] Which open source models can replicate wonder dynamics's drag'n'drop cg characters?
The Track-Anything tool already implements this
- Tutorial for Track-Anything, an interactive tool to segment, track, and inpaint anything in videos.
- Github for Track Anything
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Segment Anything for Video - Track Anything! 🤖
Segment Anything for Video - Track Anything! 🤖 With this tool, you can automatically isolate objects, make edits using inpainting, and track objects with precision. It's a game-changer for creative projects. Even though it does not work well with the shadows yet, we expect a rapid evolution of these technologies. Github : https://github.com/gaomingqi/Track-Anything)
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How to adapt an existing Python project to my specific use case without bringing in unnecessary dependencies or reinventing the wheel
I would like to know if there are any guidelines to follow when adapting an existing Python project for my own use-case. Specifically, I want to customize the output of the Track-Anything project by incorporating my own processing steps. However, I do not want to import the entire codebase. Rather, I only want to import the minimum amount of code necessary to produce the same output with object tracking, without having to reimplement functions that are already available.
- Track-Anything should get implemented in kdelive
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SUSTech VIP Lab Proposes Track Anything Model (TAM) That Achieves High-Performance Interactive Tracking and Segmentation in Videos
Here is the GitHub: https://github.com/gaomingqi/track-anything
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?
stable-diffusion-webui - Stable Diffusion web UI
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
sam-clip - Use Grounding DINO, Segment Anything, and CLIP to label objects in images.
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.
sd-webui-segment-anything - Segment Anything for Stable Diffusion WebUI
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