peterzhousz Goto Github PK

followers: 79.0 following: 1.2K repos: 3.9K gists: 0.0

Name: PeterZhouSZ

Type: User

PeterZhouSZ's Projects

egopose

Official PyTorch Implementation of "Ego-Pose Estimation and Forecasting as Real-Time PD Control". ICCV 2019.

ei

Equivariant Imaging (EI), ICCV'2021 Oral

eig-faces

Efficiently inverting a probabilistic graphics program of face generation with an inference network. Includes computational models and neural and behavioral data analysis.

eigengan-tensorflow

EigenGAN: Layer-Wise Eigen-Learning for GANs (ICCV 2021)

elastic-curves

Matlab + Gurobi Implementation of the paper "The Design Space of Plane Elastic Curves" (Siggraph 2021)

elasticreconstruction

3D reconstruction system to creating detailed scene geometry from range video.

eld

A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising (CVPR 2020 Oral & TPAMI 2021)

elementsyn

code and data repository for "Dynamic Element Textures"

elpips

E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles

emanet

The code for Expectation-Maximization Attention Networks for Semantic Segmentation (ICCV'2019 Oral)

embeddeddeformation

A C++ implementation of Embedded Deformation of Shape Manipulation

embeddeddeformationgraphvr

Embedded Deformation Graph editting meets HTC Vive...

Official repository accompanying a CVPR 2022 paper EMOCA: Emotion Driven Monocular Face Capture And Animation. EMOCA takes a single image of a face as input and produces a 3D reconstruction. EMOCA sets the new standard on reconstructing highly emotional images in-the-wild

emopy

A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)

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.

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