Gameplay Video
Gameplay video analysis is a rapidly evolving field focused on extracting meaningful information and insights from video recordings of gameplay. Current research emphasizes developing AI models, often employing transformer architectures, convolutional neural networks, and recurrent neural networks, to perform tasks such as action recognition, pose estimation, emotion detection, and event segmentation within video sequences. These advancements are driving progress in areas like player experience modeling, video editing and generation, and even applications in healthcare (e.g., pain recognition and ADHD diagnosis) by leveraging the rich spatiotemporal data inherent in gameplay videos. The resulting techniques have significant implications for improving game design, enhancing user experience, and creating new possibilities for human-computer interaction.
Papers
How Suboptimal is Training rPPG Models with Videos and Targets from Different Body Sites?
Björn Braun, Daniel McDuff, Christian Holz
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Yueqian Wang, Xiaojun Meng, Jianxin Liang, Yuxuan Wang, Qun Liu, Dongyan Zhao
RID-TWIN: An end-to-end pipeline for automatic face de-identification in videos
Anirban Mukherjee, Monjoy Narayan Choudhury, Dinesh Babu Jayagopi