WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Projects
- KontaKt App
- Sudoku Spoiler
- GLSL Raytracing
- Decentralized Land Registration on Ethereum
- Galactic Collision Simulator
- Competetive coding
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Generating a human pose dataset using PC games
- Monte Carlo Path Tracing Renderer
- Tabbing App
- Geo-location Augmented Reality
- AR chess app
- Prevention of Sophisticated DoS attack / Network Security
- Winning a Deep Learning challenge
- Automated Fiducial Localisation from MRI/CT Images
- Panorama in Cam Scanner
- 3D Object Reconstruction from Single Image
- Statistical Modelling of Star Maps
- Face Recognition Systems
- Competitive Coding
- Can Machines Identify Genres?
- Joint Modelling of Source Code and Natural Language
- Front end development for FOSSEE websites
- Institute Delivery System
- Capture The Swag
- Panorama in Cam-Scanner
- Poisson Solver with Image Editing
- Blind Source Separation
- FAQ Bot for Freshmen
- Capturing semantic structures in Neural Machine Translation
This project will focus on getting human pose estimates in games to generate a dataset using no manual annotations or labelling.
Deep networks are very data hungry in this age. Annotating lots of data is very tedious, expensive, and inefficient.
However, a lot of ground truth data can be easily generated by using the rendering of video games to extract specific information like semantic segmentation, depth maps, etc. The project will focus on getting human pose estimates in games to generate a “in-the-wild” dataset using no manual annotations or labelling.
This will be done by injecting specialized code into the DirectX rendering API. We’ll further test the effectiveness of the dataset on real images to see if such a dataset can provide benefits in training.
|Week1||Understand the main paper, and what pose estimation is|
|Week2||Download a free game and start exploring the DirectX API|
|Week 3, 4||Extract the pose information from pre-renders|
|Week 5, 6||Cleaning up the dataset, and testing a small DNN to predict pose|
|Week 7, 8||Try on one more game and start testing on real datasets|