The model uses a hybrid dataset called OBER (OBject-Effect Removal) which is designed to support research in object removal with effects. The dataset combines both camera-captured and simulated data, making it a comprehensive resource for training and testing object removal models. ObjectClear can be easily integrated into various applications, and its performance can be further improved by fine-tuning the model on specific datasets.
ObjectClear provides a simple and efficient way to remove objects from images and videos. The model can be used for various applications such as image editing, video editing, and computer vision tasks. ObjectClear is also a useful tool for researchers and developers who want to explore the capabilities of object removal models. The model is available for use, reproduction, and distribution strictly for non-commercial purposes, and the code, models, and datasets are licensed under NTU S-Lab License 1.0.
