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| With the rapid development of the augmented reality field, virtual shadow generation technology has garnered widespread attention. However, traditional methods involve complex computational requirements for environmental modeling and display, and the effectiveness of using fewer 3D parameter estimation methods is suboptimal. To address these issues, this paper proposes an end-to-end virtual shadow generation method based on shadow detection. Firstly, we introduce a shadow detector designed to identify real shadows and their corresponding occlusions in the background context, while also learning the mapping relationship between occlusions and shadows in the current scene. Secondly, we devise a virtual shadow generator. Utilizing the mapping relationship obtained from the detector as guidance information, it is encoded into the generator's input. Through feature extraction, encoding, and decoding of the input information, we ultimately obtain the virtual shadow image. Experimental results demonstrate the exceptional performance of the proposed virtual shadow generation method. In comparison to existing methods for direct virtual shadow generation, our approach significantly enhances the harmony and realism of the synthesized images. |
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| Keywords:shadow detection; virtual shadow generation; deep learning; augmented reality |
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