com.unity.perception
PeopleSansPeople
com.unity.perception | PeopleSansPeople | |
---|---|---|
8 | 5 | |
872 | 294 | |
1.1% | 2.4% | |
0.0 | 3.0 | |
11 months ago | 2 months ago | |
C# | C# | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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com.unity.perception
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Synthetic image Generation
Unity
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dataset collection for transfer learning
If you are interested, there are open source solutions on top of Unity, Blender, Unreal. You can generate yourself the data you described easier than it looks (the amount of options and settings can be intimidating with these tools).
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Help with Yolov4 Training and Synthetic Data
I see that you are already selected Unity for your AR app development, so you can go ahead and generate your data in Unity as well. They have an open source package to do that. Repo has tutorials to get you started and perception team is responsive to issues and questions.
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Unity provides People Generator & Home Interior Generator for Computer Vision tasks!
Check the Unity Perception Package here.
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How to generate images and labels using unity (or other game engine) to train YOLO5 object detection model? (Synthetic Data generation using unity for neural network learning). Are there any existing solutions? Or something similar
Hey, you can use Unity’s official Perception package. They have tutorials in the repo, but if you need more there are couple of tutorials on YT. If you don’t mind, what kind of images are you going to generate? I am developing a synthetic data generation tool for Unity, may I ask you couple of questions?
- Are there any tools to generate images and labels from 3d models/games?
- [D] Use of (machine learning + Game engines) for automatic 2D/3D content creation
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How to get started with synthetic data generation?
Using Unity: https://github.com/Unity-Technologies/com.unity.perception
PeopleSansPeople
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PI wants me to make a synthetic dataset.
Also, check this Unity repo out
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Generating human motion synthetic data ?
I was trying to train a model which goes on top of one of the pose estimation models(posenet, movenet, mediapipe) which detects the action performed(waving, swipe right, etc), and I was planning on generating synthetic data for it. I saw that there's a project for unity PeopleSansPeople, but it's not right to train a model for action recognition. I would like something that either simulates a human doing a simple action, to which I would be able to add randomness to it. I was thinking to either use Unity or maybe write something that would model the human keypoints(the output of pose estimation) and simulate them.. I am wondering if there already exists something that you guys might know about??
- [P] Can't finish my master's thesis. What to do?
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[R] PeopleSansPeople: Unity's Human-Centric Synthetic Data Generator. GitHub link in comments.
Source code: https://github.com/Unity-Technologies/PeopleSansPeople
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[R] PeopleSansPeople: Unity's Human-Centric Synthetic Data Generator
Webpage: https://unity-technologies.github.io/PeopleSansPeople/ Paper: https://arxiv.org/abs/2112.09290 Source code: https://github.com/Unity-Technologies/PeopleSansPeople Papers with code: https://paperswithcode.com/paper/peoplesanspeople-a-synthetic-data-generator https://paperswithcode.com/dataset/peoplesanspeople Demo video: https://youtu.be/mQ_DUdB70dc Summary: PeopleSansPeople is a human-centric data generator provided by Unity Technologies that contains highly-parametric and simulation-ready 3D human assets, parameterized lighting and camera system, parameterized environment generators, and fully-manipulable and extensible domain randomizers. PeopleSansPeople can generate RGB images with sub-pixel-perfect 2D/3D bounding box, COCO-compliant human keypoints, and semantic/instance segmentation masks in JSON annotation files. All packaged in macOS and Linux executable binaries capable of generating 1M+ datasets. In addition we release a template Unity environment for lowering the barrier of entry and getting you started with creating your own highly-parameterized human-centric synth data generator. We affectionately named our synthetic data generator PeopleSansPeople, as it is a data generator aimed at human-centric computer vision without using human data which bears serious privacy, safety, ethical, bias, and legal concerns. Benchmarks: The domain randomization we used for our benchmarks are naïve, brute-forced sweeps through the pre-chosen range of parameters; as such we end up generating psychedelic-looking scenes, which turned out to train more performant models for human-centric computer vision.Using PeopleSansPeople we benchmarked a Detectron2 Keypoint R-CNN variant. Results indicate synthetic pre-training with our data outperforms results of training on real data alone or pre-training with ImageNet, both in limited and abundant data regimes.We envisage that this freely-available data generator should enable a wide range of research into the emerging field of simulation to real transfer learning in the critical area of human-centric computer vision.
What are some alternatives?
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment - This project is part of the CS course 'Systems Engineering Meets Life Sciences I' at Goethe University Frankfurt. In this Computer Vision project, we present our first attempt at tackling the problem of traffic sign recognition using a systems engineering approach.
VirtualHumanBatchProcessing
kubric - A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
EasySynth - Unreal Engine plugin for easy creation of synthetic image datasets
tdk-demo - This is a collection of TDK demo projects that use different databases and options
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
unrealcv - UnrealCV: Connecting Computer Vision to Unreal Engine