Search Results for author: Perttu Hämäläinen

Found 12 papers, 7 papers with code

Personalized Game Difficulty Prediction Using Factorization Machines

no code implementations6 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.

Cine-AI: Generating Video Game Cutscenes in the Style of Human Directors

1 code implementation11 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.

Unity

Predicting Game Engagement and Difficulty Using AI Players

no code implementations26 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.

Learning Task-Agnostic Action Spaces for Movement Optimization

1 code implementation22 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.

Predicting Game Difficulty and Churn Without Players

no code implementations29 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.

Deep Residual Mixture Models

1 code implementation22 Jun 2020 Perttu Hämäläinen, Martin Trapp, Tuure Saloheimo, Arno Solin

We propose Deep Residual Mixture Models (DRMMs), a novel deep generative model architecture.

BIG-bench Machine Learning

Converting Biomechanical Models from OpenSim to MuJoCo

1 code implementation17 Jun 2020 Aleksi Ikkala, Perttu Hämäläinen

OpenSim is a widely used biomechanics simulator with several anatomically accurate human musculo-skeletal models.

reinforcement-learning Reinforcement Learning (RL)

Visualizing Movement Control Optimization Landscapes

no code implementations17 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.

Self-Imitation Learning of Locomotion Movements through Termination Curriculum

1 code implementation27 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.

Imitation Learning

An Iterative Closest Points Approach to Neural Generative Models

1 code implementation16 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.

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