Evaluation of Neural-Network and Large-Language Model Approaches for Generating Instructions for Animations
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2025
Författare
BARLETTARO, ELISABETTA
ERIKSSON, EMMA
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Conversational agents are used more and more in customer service, health care, for
educational purposes. The fundamental problems of conversational agents are many,
including limitations in interpretation of complex queries and lack of emotional intelligence.
Despite this, there are distinct advantages of conversational agents, such as
efficient data analysis, reduction of operational costs and aid in interactive learning
for personalized teaching. The most significant challenge this project aims to undertake
is to generate realistic and complex animations in the context of interactive
learning with a real-time constraint. The investigation includes how to select machine
learning tools and models to aid in the advancement of animation generation,
by using both Large-Language Models and purposely constructed Neural Networks.
While Large-Language Models are convenient when used in straightforward conditions,
Neural Networks are more dependable in an operative application thanks to
their consistent format, adaptability and specifically developed purpose.
Beskrivning
Ämne/nyckelord
animations, armature, avatar, Blender, blendshapes, educational, largelanguage models, neural networks, Python, real-time, training simulator, Unity