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
Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics
Shishira R Maiya, Anubhav Gupta, Matthew Gwilliam, Max Ehrlich, Abhinav Shrivastava
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos
Shreyank N Gowda, Boyan Gao, David A. Clifton
Joint-Motion Mutual Learning for Pose Estimation in Videos
Sifan Wu, Haipeng Chen, Yifang Yin, Sihao Hu, Runyang Feng, Yingying Jiao, Ziqi Yang, Zhenguang Liu
SAM 2: Segment Anything in Images and Videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, Christoph Feichtenhofer
How Effective are Self-Supervised Models for Contact Identification in Videos
Malitha Gunawardhana, Limalka Sadith, Liel David, Daniel Harari, Muhammad Haris Khan