# Label-Free Subjective Player Experience Modelling via Let's Play Videos

arXiv: https://arxiv.org/abs/2410.02967 · HTML: https://mlsanity.com/paper/2410.02967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Towards General Models of Player Experience: A Study Within Genres](https://mlsanity.com/paper/2110.00978/) (2021)
- [Player Experience Extraction from Gameplay Video](https://mlsanity.com/paper/1809.06201/) (2018)
- [Making CNNs for Video Parsing Accessible](https://mlsanity.com/paper/1906.11877/) (2019)

## Built on by (most cited first, 2 of 2)

- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior](https://mlsanity.com/paper/2608.16196/) (2026)

## Abstract (arXiv)

Player Experience Modelling (PEM) is the study of AI techniques applied to modelling a player's experience within a video game. PEM development can be labour-intensive, requiring expert hand-authoring or specialized data collection. In this work, we propose a novel PEM development approach, approximating player experience from gameplay video. We evaluate this approach predicting affect in the game Angry Birds via a human subject study. We validate that our PEM can strongly correlate with self-reported and sensor measures of affect, demonstrating the potential of this approach.