The JRDB-GMP dataset, while a valuable resource for long-term multi-agent human pose forecasting, may have some limitations that could be addressed for further improvement:
Limited Diversity: The dataset may lack diversity in terms of scenarios, environments, or types of interactions. Increasing the variety of scenes, agent behaviors, and environmental conditions can enhance the dataset's robustness and generalizability.
Annotation Quality: Ensuring the accuracy and consistency of 3D pose annotations extracted from images is crucial. Implementing rigorous quality control measures and validation processes can help improve the reliability of the dataset.
Scalability: Expanding the dataset to include a larger number of agents, longer sequences, and more complex interactions can provide a more comprehensive understanding of multi-agent dynamics. This scalability can help capture a wider range of real-world scenarios.
Contextual Information: Incorporating contextual information such as scene semantics, agent intentions, or environmental factors can enrich the dataset and enable models to better interpret and predict human motion in diverse settings.