Dual-Camera Smooth Zoom on Mobile Phones
<|reference_start|>Dual-Camera Smooth Zoom on Mobile Phones: When zooming between dual cameras on a mobile, noticeable jumps in geometric content and image color occur in the preview, inevitably affecting the user's zoom experience. In this work, we introduce a new task, ie, dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview. The frame interpolation (FI) technique is a potential solution but struggles with ground-truth collection. To address the issue, we suggest a data factory solution where continuous virtual cameras are assembled to generate DCSZ data by rendering reconstructed 3D models of the scene. In particular, we propose a novel dual-camera smooth zoom Gaussian Splatting (ZoomGS), where a camera-specific encoding is introduced to construct a specific 3D model for each virtual camera. With the proposed data factory, we construct a synthetic dataset for DCSZ, and we utilize it to fine-tune FI models. In addition, we collect real-world dual-zoom images without ground-truth for evaluation. Extensive experiments are conducted with multiple FI methods. The results show that the fine-tuned FI models achieve a significant performance improvement over the original ones on DCSZ task. The datasets, codes, and pre-trained models will are available at https://github.com/ZcsrenlongZ/ZoomGS.<|reference_end|>
@article{wu2024dual-camera,
    title={Dual-Camera Smooth Zoom on Mobile Phones},
    author={Renlong Wu, Zhilu Zhang, Yu Yang, and Wangmeng Zuo},
    journal={arXiv preprint arXiv:2404.04908},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04908},
    primaryClass={cs.CV}
}