#Video Prediction

Explore a state-of-the-art model for video prediction presented at CVPR2023. The dynamic multi-scale voxel flow network achieves high performance on various datasets, advancing video forecasting techniques. It includes detailed installation, training, and testing guides, supporting a range of video prediction uses. Access demonstrations and utilize pre-trained models for swift application. Improve your projects with this effective video prediction tool.

OpenSTL offers a robust framework for spatio-temporal predictive learning with a variety of methods and use cases. Its modular design facilitates integration and customization for fields like video prediction, weather forecasting, and traffic analysis. The project provides implementations in PyTorch Lightning and naive PyTorch, addressing various technical preferences. Key features include adaptable code design, established benchmarks, a wide array of datasets, and necessary dependencies. The platform's comprehensive documentation, model zoo, and visualization tools enable researchers to effectively engage with and enhance the OpenSTL framework.

Enhanced image prediction accuracy through early vision-language fusion for text-prompted segmentation tasks. This model excels in Referring Expression Segmentation by enabling fast computations on T4 GPUs. Utilizing simple image training on RES datasets, it extends its capabilities to zero-shot video prediction with innovations from SAM-2. It supports multitasking for semantic and body part segmentation, serving as a versatile tool for academic and practical use. Experience its capabilities through available demos, code, and weights.