I’m thrilled to announce the availability of the 256-MetaverseRecords Dataset, a dataset for experiments with machine learning technology for metaverse recordings. This dataset represents a significant step forward in the exploration of the integration of virtual worlds in Multimedia Information Retrieval.
The dataset was created to explore the use of meatverse virtual worlds and evlauate performance of feature exraction methods on Metaverse Recordings. The dataset contains 256 video records of user sessions in virtual worlds, mostly based on screen recordings.
The dataset is part of my work to achieve a doctor’s degree. The recordings were made during a student workshop with 13 participants. Each student was tasked to create 20 videos with a duration of 2-5 minutes, resulting in a rich collection of data. This variety ensures a robust dataset that encapsulates a wide range of virtual interactions, making it an invaluable tool for researchers.
Each student was required to annotate their videos based on a provided schema. These annotations include information about the actions occurring within each recording. This structured approach to data collection ensures that the dataset is not only vast but also well-organized, allowing for efficient data retrieval and analysis.
Together with the recordings, several annotations are available. These annotations serve as a starting point for self-developed processing and experiments. Researchers and developers can use these annotations to train machine learning models, test new algorithms, or even develop new annotation techniques tailored to the unique characteristics of the metaverse.
The 256-MetaverseRecords Dataset is not just a collection of videos; it’s a gateway to understanding the complex dynamics of virtual worlds. The potential applications of this dataset are vast. For instance, it could be used to improve user experience in virtual reality (VR) games by enhancing the realism of the environment or to develop new educational tools that leverage the immersive nature of VR.
Furthermore, this dataset could play a crucial role in advancing the field of computer vision, especially in understanding and interpreting the rich, dynamic content of virtual environments. It offers a unique challenge to traditional feature extraction methods, pushing the boundaries of what’s possible in machine learning and AI.
In conclusion, the 256-MetaverseRecords Dataset is an invaluable resource for anyone interested in the intersection of virtual reality and machine learning. It opens up new avenues for research and development, promising to contribute significantly to our understanding and utilization of virtual worlds.
The dataset has its dedicated website and is available at: https://patricksteinert.de/256-metaverse-records-dataset/