contrastive-unpaired-translation
PeopleSansPeople
contrastive-unpaired-translation | PeopleSansPeople | |
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6 | 5 | |
2,102 | 294 | |
- | 2.4% | |
2.1 | 3.0 | |
8 months ago | 2 months ago | |
Python | C# | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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contrastive-unpaired-translation
- Help me with my GANS project
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[D] What is current SOTA in Image to Image Translation?
For unpaired image-to-image translation, the SOTA is probably Contrastive Unpaired Translation, which is developed by the same group that developed CycleGAN and is kind of the successor algorithm.
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[P] Can't finish my master's thesis. What to do?
Here is link number 1 - Previous text "CUT"
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[D] What are techniques for few shot domain adaptation?
Code for https://arxiv.org/abs/2007.15651 found: https://github.com/taesungp/contrastive-unpaired-translation
- [D] What's the successor to pix2pixHD
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?
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
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.
CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
com.unity.perception - Perception toolkit for sim2real training and validation in Unity
pytorch-AdaIN - Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]
VirtualHumanBatchProcessing
vrn - :man: Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"
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.
mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
tdk-demo - This is a collection of TDK demo projects that use different databases and options
PyTorch-GAN - PyTorch implementations of Generative Adversarial Networks.
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch