Gameplay Highlights Generation

TL;DR

This paper presents a multimodal video understanding approach for automatically generating gaming highlight reels by detecting interesting events, using a fine-tuned X-CLIP model that generalizes across multiple games and platforms.

Contribution

The work introduces a novel dataset and a fine-tuned multimodal model for cross-game highlight detection without game-specific engineering, improving efficiency and generalization.

Findings

Achieved over 90% accuracy in event detection in unseen gameplay footage.

Demonstrated transfer learning benefits for low-resource games.

Enabled cross-platform deployment with ONNX and quantization.

Abstract

In this work, we enable gamers to share their gaming experience on social media by automatically generating eye-catching highlight reels from their gameplay session Our automation will save time for gamers while increasing audience engagement. We approach the highlight generation problem by first identifying intervals in the video where interesting events occur and then concatenate them. We developed an in-house gameplay event detection dataset containing interesting events annotated by humans using VIA video annotator. Traditional techniques for highlight detection such as game engine integration requires expensive collaboration with game developers. OCR techniques which detect patches of specific images or texts require expensive per game engineering and may not generalize across game UI and different language. We finetuned a multimodal general purpose video understanding model such as X-CLIP using our dataset which generalizes across multiple games in a genre without per game engineering. Prompt engineering was performed to improve the classification performance of this multimodal model. Our evaluation showed that such a finetuned model can detect interesting events in first person shooting games from unseen gameplay footage with more than 90% accuracy. Moreover, our model performed significantly better on low resource games (small dataset) when trained along with high resource games, showing signs of transfer learning. To make the model production ready, we used ONNX libraries to enable cross platform inference. These libraries also provide post training quantization tools to reduce model size and inference time for deployment. ONNX runtime libraries with DirectML backend were used to perform efficient inference on Windows OS. We show that natural language supervision in the X-CLIP model leads to data efficient and highly performant video recognition models.