TomKlootwijk's Stars
NVlabs/eg3d
mttr2021/MTTR
openai/DALL-E
PyTorch package for the discrete VAE used for DALL·E.
dji-sdk/Mobile-UXSDK-Beta-Android
Mobile Android UXSDK Beta
Yonet/Microsoft-Rocketbox
Microsoft Rocketbox is now available for research and academic use! The library of 115 rigged avatars offers flexibility, enabling the easy use of facial animations across characters and the mixing and matching of texture elements.
dukelec/mbrush
PrinCube / MBrush APP Source Code
touchyfeelytech/sex-tech-hackathon
Rainbows End Sex Tech Hackathon: 23 + 24 January 2021
typedb-osi/typedb-loader
TypeDB Loader - Data Migration Tool for TypeDB
PavelDoGreat/WebGL-Fluid-Simulation
Play with fluids in your browser (works even on mobile)
aritter/twitter_nlp
Twitter NLP Tools
google/patents-public-data
Patent analysis using the Google Patents Public Datasets on BigQuery
microsoft/tensorflow-directml
Fork of TensorFlow accelerated by DirectML
microsoft/DirectML
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm.
pallada-92/dna-3d-engine
3d engine implementation in DNA code!
dahburj/Barracuda-PoseNet
PoseNet Using Unity MLAgents Barracuda Engine
infocom-tpo/PoseNet-Unity
PoseNet in Unity
Kiarash-Parvizi/GameLog
High-Performance Portable Log System for unity
Lightstreamer/mqtt-chat-example
The Chat Demo is a very simple chat application based on MQTT.Cool
Cysharp/UniTask
Provides an efficient allocation free async/await integration for Unity.
Tyrrrz/DiscordChatExporter
Exports Discord chat logs to a file
harperreed/node-ifttt-mqtt-bridge
Firebase Webhook (IFTTT/API.AI) -> MQTT bridge
valenting/node_tradfri_ifttt
Backend to control Ikea TRADFRI from IFTTT
justadudewhohacks/face-api.js
JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
tensorflow/tfjs-models
Pretrained models for TensorFlow.js
alexandrosstergiou/Inception_v3_TV_Human_Interactions
Applying Transfer Learning on Inception V3 model (weights trained on Imagenet) for the Oxford TV Human Interactions dataset. The network gets as inputs images extracted every 5 frames from videos.
charlierix/PhotogrammetryImagePrep
Putting a GUI around extracting images from video to be fed into a photogrammetry app
nxcd/face-dataset-creator
It's a face extractor from videos and images to create a dataset about face images
dhvanikotak/Emotion-Detection-in-Videos
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
TejasBob/Panorama
Image Stitching on key-frames extracted from video
OlafenwaMoses/ImageAI
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities