# Player Experience Extraction from Gameplay Video

arXiv: https://arxiv.org/abs/1809.06201 · HTML: https://mlsanity.com/paper/1809.06201/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Contrastive Learning of Generalized Game Representations](https://mlsanity.com/paper/2106.10060/) (2021)
- [Label-Free Subjective Player Experience Modelling via Let's Play Videos](https://mlsanity.com/paper/2410.02967/) (2024)
- [Improving Deep Localized Level Analysis: How Game Logs Can Help](https://mlsanity.com/paper/2212.03376/) (2022)

## Abstract (arXiv)

The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. In this paper we present two approaches to derive game logs from game video via convolutional neural networks and transfer learning. We evaluate the approaches in a Super Mario Bros. clone, Mega Man and Skyrim. Our results demonstrate our approach outperforms random forest and other transfer baselines.