DG AI Research Lab

Can we learn game representations from images that extract only the game’s content while ignoring its graphic styling? Spoiler: Yes!

Overview of the paper “Monster Mash: A Single-View Approach to Casual 3D Modeling and Animation” by M Dvorožňák et al.

Overview of the paper “Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess” by N Tomašev et al.

Overview of the paper “Next-Best View Policy for 3D Reconstruction” by D Peralta et al.

Overview of the paper “End-to-End Object Detection with Transformers” by N Carion et al.

Overview of the paper “CenterHMR: a Bottom-up Single-shot Method for Multi-person 3D Mesh Recovery from a Single Image” by Y Sun et al.

Overview of the paper ”A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild” by K R Prajwal et al.

Overview of the paper “Data-driven Authoring of Large-scale Ecosystems” by K Kapp et al.

A.I. bots in gaming are usually built by hand-coding a bunch of rules that impart game-intelligence. For the most part, this approach does…

Game Studios spend millions of dollars and thousands of development hours designing game graphics in trying to make them look as close to…

In this article, I’ll explain how I used tensorflow’s object detection model to play the classic FPS game Counter Strike.

This project is a continuation of my previous article. In it, I explained how we can use CycleGANs for image style transfer, and apply it…

A code tutorial in Tensorflow that uses Reinforcement Learning to take free kicks.

Using Pose Estimation and Conditional Adversarial Networks to create and visualize new Fortnite dances.