Toward Game Level Generation from Gameplay Videos (1602.07721v1)

Abstract: Algorithms that generate computer game content require game design knowledge. We present an approach to automatically learn game design knowledge for level design from gameplay videos. We further demonstrate how the acquired design knowledge can be used to generate sections of game levels. Our approach involves parsing video of people playing a game to detect the appearance of patterns of sprites and utilizing machine learning to build a probabilistic model of sprite placement. We show how rich game design information can be automatically parsed from gameplay videos and represented as a set of generative probabilistic models. We use Super Mario Bros. as a proof of concept. We evaluate our approach on a measure of playability and stylistic similarity to the original levels as represented in the gameplay videos.

• The paper proposes extracting level design patterns from gameplay videos using computer vision and clustering of sprite interactions.
• It employs generative probabilistic models to learn sprite placements and level structures from high-interaction gameplay moments.
• Initial evaluations show that the generated levels are playable and stylistically similar to Super Mario Bros, highlighting potential for automated game creation.

Procedural Generation of Game Levels from Gameplay Videos

The paper "Toward Game Level Generation from Gameplay Videos" by Matthew Guzdial and Mark O. Riedl addresses a novel method to acquire game design knowledge through parsing gameplay videos. The principal focus is on automating the learning of level design through the examination of sprite patterns in such videos, subsequently employing machine learning to create generative probabilistic models. This process is explored via a case paper using the well-known platformer game, Super Mario Bros.

The authors propose the extraction of valuable design information from gameplay videos—a commonly available resource—highlighting several benefits, such as format consistency and the inclusion of player interaction data. This presents an innovative methodology distinct from traditional methods relying heavily on manual coding or game files, which may involve significant biases or labor-intensive translations of design heuristics.

Methodological Approach

Central to the proposed methodology is the use of computer vision techniques to analyze gameplay videos. By leveraging OpenCV, an open-source computer vision tool, the authors process video frames to detect sprite arrangements and complex player interactions. Key areas, identified as "high interaction areas" where players linger, are emphasized because they ostensibly contain rich design elements. The system categorizes these areas through clustering techniques, resulting in datasets conducive to learning sprite placement patterns.

The model development involves constructing generative probabilistic models, inspired by Kalogerakis et al.'s (2012) approach in 3D graphics. However, the proposed method extends and customizes it for 2D sprite-based environments, absent of human-authored guidance. The developed models encode level design patterns and sprite relationships exhaustively, minimizing manual interventions.

Results and Evaluation

The authors validate their approach through initial experiments with Super Mario Bros., evaluating generated level segments on playability and stylistic resemblance metrics. They observed that their system can produce a range of playable and stylistically congruent level sections, achieving substantial output with only initial sprite datasets. However, the generated outputs demonstrated varying success rates according to different settings for style and playability parameters.

The evaluation indicated a strong correlation between certain parameters and the produced sections' playability, while the connection with stylistic measures exhibited more complexity. The discussion of results further suggests that differing player strategies might impact what is deemed stylistically similar.

Implications and Future Directions

This work implies potential advances in procedural content generation (PCG) by transforming gameplay analysis into a rich, automated design learning process. The approach could transcend beyond generating levels, ultimately leading to the construction of new games within a genre. The authors project the system's extensibility to additional game types, positing that analogous principles could be applied to varying genre designs.

Future research directions are evident, focusing on complete game generation, including automated comprehension of game mechanics via video analysis. Such advances will necessitate integration across diverse gameplay styles, incorporating player behaviors and interaction patterns.

In summary, this paper presents a meticulously crafted methodology that leverages existing gameplay videos to facilitate PCG, offering a foundation that could redefine traditional gaming creation workflows. The proposed techniques may significantly advance automated game design knowledge, galvanizing further exploration in the intersection of computer vision and game development.