no code implementations • 24 Feb 2023 • Veera Vimpari, Annakaisa Kultima, Perttu Hämäläinen, Christian Guckelsberger
Text-to-image generation (TTIG) models, a recent addition to creative AI, can generate images based on a text description.
no code implementations • 6 Sep 2022 • Jeppe Theiss Kristensen, Christian Guckelsberger, Paolo Burelli, Perttu Hämäläinen
The accurate and personalized estimation of task difficulty provides many opportunities for optimizing user experience.
1 code implementation • 11 Aug 2022 • Inan Evin, Perttu Hämäläinen, Christian Guckelsberger
Cutscenes form an integral part of many video games, but their creation is costly, time-consuming, and requires skills that many game developers lack.
no code implementations • 26 Jul 2021 • Shaghayegh Roohi, Christian Guckelsberger, Asko Relas, Henri Heiskanen, Jari Takatalo, Perttu Hämäläinen
This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience.
1 code implementation • 22 Sep 2020 • Amin Babadi, Michiel Van de Panne, C. Karen Liu, Perttu Hämäläinen
We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier.
no code implementations • 29 Aug 2020 • Shaghayegh Roohi, Asko Relas, Jari Takatalo, Henri Heiskanen, Perttu Hämäläinen
We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game.
1 code implementation • 22 Jun 2020 • Perttu Hämäläinen, Martin Trapp, Tuure Saloheimo, Arno Solin
We propose Deep Residual Mixture Models (DRMMs), a novel deep generative model architecture.
1 code implementation • 17 Jun 2020 • Aleksi Ikkala, Perttu Hämäläinen
OpenSim is a widely used biomechanics simulator with several anatomically accurate human musculo-skeletal models.
no code implementations • 17 Sep 2019 • Perttu Hämäläinen, Juuso Toikka, Amin Babadi, C. Karen Liu
A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters.
1 code implementation • 27 Jul 2019 • Amin Babadi, Kourosh Naderi, Perttu Hämäläinen
In this paper, we propose and evaluate a novel combination of techniques for accelerating the learning of stable locomotion movements through self-imitation learning of synthetic animations.
1 code implementation • 5 Oct 2018 • Perttu Hämäläinen, Amin Babadi, Xiaoxiao Ma, Jaakko Lehtinen
Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach.
1 code implementation • 16 Nov 2017 • Joose Rajamäki, Perttu Hämäläinen
We present a simple way to learn a transformation that maps samples of one distribution to the samples of another distribution.