CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning (2203.11096v2)

Abstract: Gameplay videos contain rich information about how players interact with the game and how the game responds. Sharing gameplay videos on social media platforms, such as Reddit, has become a common practice for many players. Often, players will share gameplay videos that showcase video game bugs. Such gameplay videos are software artifacts that can be utilized for game testing, as they provide insight for bug analysis. Although large repositories of gameplay videos exist, parsing and mining them in an effective and structured fashion has still remained a big challenge. In this paper, we propose a search method that accepts any English text query as input to retrieve relevant videos from large repositories of gameplay videos. Our approach does not rely on any external information (such as video metadata); it works solely based on the content of the video. By leveraging the zero-shot transfer capabilities of the Contrastive Language-Image Pre-Training (CLIP) model, our approach does not require any data labeling or training. To evaluate our approach, we present the $\texttt{GamePhysics}$ dataset consisting of 26,954 videos from 1,873 games, that were collected from the GamePhysics section on the Reddit website. Our approach shows promising results in our extensive analysis of simple queries, compound queries, and bug queries, indicating that our approach is useful for object and event detection in gameplay videos. An example application of our approach is as a gameplay video search engine to aid in reproducing video game bugs. Please visit the following link for the code and the data: https://asgaardlab.github.io/CLIPxGamePhysics/

Advanced Bug Detection in Gameplay Videos Using CLIP

The paper "CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning" presents an innovative approach for identifying video game bugs in gameplay videos using the CLIP (Contrastive Language-Image Pre-Training) model. This promising method leverages the zero-shot learning capabilities of CLIP to enable efficient search and retrieval of specific gameplay events directly from video content without the need for labeled data or retraining of models.

Methodology and Approach

The authors propose a system that utilizes the CLIP model's ability to process both text and image inputs to conduct searches within large gameplay video datasets. The methodology revolves around transforming both the frames of a video and a natural language text query into embedding vector representations. This process enables a comparison that identifies videos containing objects or events that closely match the query. The approach benefits from zero-shot learning, thereby circumventing the issues associated with traditional supervised methods, such as the need for extensive labeled datasets.

The system preprocesses videos to extract frames, which are then encoded alongside the query text using the CLIP model. Two aggregation methods are introduced to calculate a similarity score for video retrieval: using the maximum frame score and counting the number of highly similar frames per video. The paper evaluates these methods to determine the robustness and sensitivity of the gameplay video search.

Dataset and Experiments

To showcase the efficacy of their approach, the authors created the GamePhysics dataset comprising 26,954 curated gameplay videos predominantly featuring game physics bugs. Videos were sourced from the GamePhysics subreddit, and a rigorous filtering process was applied to ensure quality and relevance.

Three experiments were conducted to assess the system’s effectiveness:

1. Simple Queries: Identifying basic objects like cars or animals without additional descriptors.
2. Compound Queries: Using more complex queries that combine objects with specific characteristics or conditions.
3. Bug Queries: Searching for specific descriptions of bug-related events.

The results, measured in terms of top-$k$ accuracy and recall, demonstrated promising performance, particularly in correctly interpreting and retrieving gameplay frames that matched both simple and complex query inputs.

Results and Insights

The approach's success is partly attributed to the robust capabilities of the CLIP model, which, despite not being specifically trained on video game data, effectively identified in-game objects and events. The ability to effectively operate without further training highlights the model’s generalization capabilities across diverse visual datasets.

A common issue identified was the misclassification of similar objects, attributed to perspectives or adversarial poses, highlighting areas for further enhancement. The retrieval accuracy varied with the object or event and occasionally suffered due to confounding textures or misleading text within the game environment.

Implications and Future Directions

This work possesses significant implications for game development and software testing. It offers a novel tool for developers to quickly identify and analyze bugs by parsing extensive gameplay footage, thus streamlining bug reproduction and reducing manual testing efforts.

Future work can delve into refining the aggregation methods for better precision, enhancing the processing of adversarial poses, and extending the approach to even broader video game datasets. Moreover, integration with existing game bug detection and reproduction workflows could solidify this method’s place as a staple in automated software testing and debugging in the gaming industry.

In conclusion, the paper provides considerable insights into leveraging contrastive learning models for tasks beyond traditional benchmarks, specifically in the domain of video game testing. The adoption and expansion of such zero-shot methodologies could redefine automated testing paradigms within interactive digital media.