AI-generated-characters for Learning and Wellbeing

Last update: Jan 01, 2023

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Overview

AI-generated-characters for Learning and Wellbeing

Click here for the full project page.

This repository contains the source code for the paper AI-generated characters for supporting personalized learning and well-being by Pat Pataranutaporn, Valdemar Danry, Joanne Leong, Parinya Punpongsanon, Dan Novy, Pattie Maes & Misha Sra. This repository is a combination previous work on AI generated characters that include Siarohin et al., Prajwal et al., and Corentin.

Colab Demo

The code is available on google-colab. See: AI_Generated_Characters.ipynb. To run press Open In Colab button.

Examples of Outputs

With the pipeline, one can easily create a video of AI-generated characters from Video, Audio, and Text input (text is coming soon).

Owner

MIT Media Lab

GitHub Repository

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AI-generated-characters for Learning and Wellbeing

Related tags

Overview

AI-generated-characters for Learning and Wellbeing

Colab Demo

Examples of Outputs

Owner

MIT Media Lab

Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

A full-fledged version of Pix2Seq

A GUI to automatically create a TOPAS-readable MLC simulation file

2D Human Pose estimation using transformers. Implementation in Pytorch

An Artificial Intelligence trying to drive a car by itself on a user created map

This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.

BOVText: A Large-Scale, Multidimensional Multilingual Dataset for Video Text Spotting

Neurolab is a simple and powerful Neural Network Library for Python

Official source code of Fast Point Transformer, CVPR 2022

Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Deep Learning for Human Part Discovery in Images - Chainer implementation

The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion" (NeurIPS 2021)

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