This code implements a demo of the Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources paper by Adrian Bulat and Georgios Tzimiropoulos. Download the model available bellow and place it in the models folder.
https://www.adrianbulat.com/binary-human-pose-estimationTags | binary network-binarization human-pose-estimation deep-learning torch7 computer-vision |
Implementation | Lua |
License | Public |
Platform |
'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.
deep-learning openpose tensorflow mobilenet pose-estimation convolutional-neural-networks neural-network image-processing human-pose-estimation embedded realtime cnn mobile ros robotics catkinBy Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. Code repo for winning 2016 MSCOCO Keypoints Challenge, 2016 ECCV Best Demo Award, and 2017 CVPR Oral paper.
human-pose-estimation realtime caffe human-behavior-understanding deep-learning computer-vision matlab cpp11 cvpr-2017Update 04/06/2017 Article "Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods" have been accepted for publication in Pattern Recogntion (Elsevier). The Deepgaze CNN head pose estimator module is based on this work. Update 22/03/2017 Fixed a bug in mask_analysis.py and almost completed a more robust version of the CNN head pose estimator.
convolutional-neural-networks motion-tracking color-detection face-detection skin-detection motion-detection head-pose-estimation human-computer-interaction histogram-comparison histogram-intersection cnn particle-filter saliency-mapOpenPose represents the first real-time multi-person system to jointly detect human body, hand, and facial keypoints (in total 135 keypoints) on single images. For further details, check all released features and release notes.
openpose computer-vision machine-learning multi-threading cpp cpp11 caffe opencv human-pose-estimation real-time deep-learning human-behavior-understanding cvpr-2017Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little. A simple yet effective baseline for 3d human pose estimation. In ICCV, 2017. https://arxiv.org/pdf/1705.03098.pdf. The code in this repository was mostly written by Julieta Martinez, Rayat Hossain and Javier Romero.
tensorflow computer-vision 3d-vision baseline iccv-2017 iccv-17Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance. For details about the method and quantitative results please check the paper.
head-pose-estimation head-pose face-pose gaze-estimation gaze head deep-learning deep-neural-networksAlpha Pose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset. To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset. Note: Please read PoseFlow/README.md for details.
pose-estimation deep-learning iccv2017 posetracking torch computer-vision machine-learning tracking state-of-the-art gpu pytorch faster-rcnnHere we present our wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge. In this work, we describe our winning solution for MICCAI 2017 Endoscopic Vision Sub-Challenge: Robotic Instrument Segmentation and demonstrate further improvement over that result. Our approach is originally based on U-Net network architecture that we improved using state-of-the-art semantic segmentation neural networks known as LinkNet and TernausNet. Our results shows superior performance for a binary as well as for multi-class robotic instrument segmentation. We believe that our methods can lay a good foundation for the tracking and pose estimation in the vicinity of surgical scenes.
medical-imaging robot-assisted-surgery computer-vision image-segmentation deep-learning pytorchWelcome to the DeepLabCut repository, a toolbox for markerless tracking of body parts of animals in lab settings performing various tasks, like trail tracking, reaching in mice and various Drosophila behaviors during egg-laying (see Mathis et al. for details). There is, however, nothing specific that makes the toolbox only applicable to these tasks and/or species. The toolbox has also already been successfully applied to rats, humans, various fish species, bacteria, leeches, various robots, and race horses. Please check out www.mousemotorlab.org/deeplabcut for video demonstrations of automated tracking. This work utilizes the feature detectors (ResNet + readout layers) of one of the state-of-the-art algorithms for human pose estimation by Insafutdinov et al., called DeeperCut, which inspired the name for our toolbox (see references below).
behavior-analysis deep-learning pose-estimationOver the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace – a tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace is the first toolkit capable of facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation with available source code for both running and training the models. The computer vision algorithms which represent the core of OpenFace demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware. OpenFace is an implementation of a number of research papers from the Multicomp group, Language Technologies Institute at the Carnegie Mellon University and Rainbow Group, Computer Laboratory, University of Cambridge. The founder of the project and main developer is Tadas Baltrušaitis.
R. Girdhar, G. Gkioxari, L. Torresani, M. Paluri and D. Tran. Detect-and-Track: Efficient Pose Estimation in Videos. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018. This code was developed and tested on NVIDIA P100 (16GB), M40 (12GB) and 1080Ti (11GB) GPUs. Training requires at least 4 GPUs for most configurations, and some were trained with 8 GPUs. It might be possible to train on a single GPU by scaling down the learning rate and scaling up the iteration schedule, but we have not tested all possible setups. Testing can be done on a single GPU. Unfortunately it is currently not possible to run this on a CPU as some ops do not have CPU implementations.
Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. DensePose-RCNN is implemented in the Detectron framework and is powered by Caffe2. In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model.
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.
neural-network machine-learning tensorflow keras deeplearningPython 3 is required to run this code. First of all, you should install TensorFlow as described in the official documentation. We recommended to use virtualenv. When running training or prediction scripts, please make sure to set the environment variable TF_CUDNN_USE_AUTOTUNE to 0 (see this ticket for explanation).
NOTE: This is not official implementation. Original paper is DeepPose: Human Pose Estimation via Deep Neural Networks. I strongly recommend to use Anaconda environment. This repo may be able to be used in Python 2.7 environment, but I haven't tested.
chainerThe Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.
machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpegWe run this script under TensorFlow 1.4 and the TensorLayer 1.8.0+. 🚀 This repo will be moved to here (please star) for life-cycle management soon. More cool Computer Vision applications such as pose estimation and style transfer can be found in this organization.
tensorlayer tensorflow super-resolution gan cnn srgan vgg16 vgg19 vggPocketFlow is an open-source framework for compressing and accelerating deep learning models with minimal human effort. Deep learning is widely used in various areas, such as computer vision, speech recognition, and natural language translation. However, deep learning models are often computational expensive, which limits further applications on mobile devices with limited computational resources. PocketFlow aims at providing an easy-to-use toolkit for developers to improve the inference efficiency with little or no performance degradation. Developers only needs to specify the desired compression and/or acceleration ratios and then PocketFlow will automatically choose proper hyper-parameters to generate a highly efficient compressed model for deployment.
deep-learning model-compression mobile-app automl computer-visionThis repository implements a demo of the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper. Please visit our webpage or read bellow for instructions on how to run the code and access the dataset. Note: If you are interested in a binarized version, capable of running on devices with limited resources please also check https://github.com/1adrianb/binary-face-alignment for a demo.
face-alignment torch7 3d-face-alignment deeplearning computer-visionAs reported by Cisco, 90% of net traffic will be visual, and indeed, most of the visual data are cat photos and videos. Thus, understanding, modeling and synthesizing our feline friends becomes a more and more important research problem these days, especially for our cat lovers. Cat Paper Collection is an academic paper collection that includes computer graphics, computer vision, machine learning and human-computer interaction papers that produce experimental results related to cats. If you want to add/remove a paper, please send an email to Jun-Yan Zhu (junyanz at berkeley dot edu).
machine-learning computer-vision papers cat computer-graphics cats deep-learning papers-collection awesome-list
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