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 Running Projects
- L.A.M.A. AI using Reinforcement Learning
- Intrusion Detection system
- Competitive coding
- Why The Hype Around GANs
- 3D reconstruction using 2D images
- Computer Vision Workbench
- 3D Object Classification using Mesh Neural Network
- Lossless high entropy compression algorithm
- ML GYM
- Tools for Web Development
- Strategy Wars [Online]
- Food Recommendation through Machine Learning
- Conversational Chatbot
- Virtual Keyboard
- Super Shenron
- Gestures for 3D space
- Road Network 3D Rendering using OpenGL
- Face Recognition using Statistics
- Introduction to Kaggle and Machine Learning
- Krittika Website
- Rubik's cube solver
- Planet/Atmosphere Renderer using OpenGL
- Digital Depth Perception
- KontaKt App
- Tinkerers’ Laboratory Website
- Graphic Intensive MUSIC APP
- Pool It!
- Insti Buddy
- Intelligent agents
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|