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 be publicly available.
在移动设备上双摄像头之间缩放时，几何内容和图像颜色在预览中会发生明显跳变，不可避免地影响用户的缩放体验。在这项工作中，我们引入了一个新任务，即双摄像头平滑缩放（DCSZ），以实现平滑的缩放预览。帧插值（FI）技术是一个潜在的解决方案，但在收集真实数据方面遇到困难。为了解决这个问题，我们建议一个数据工厂解决方案，其中连续的虚拟摄像头被组装起来，通过渲染场景的重建3D模型来生成DCSZ数据。特别地，我们提出了一个新颖的双摄像头平滑缩放高斯喷涂（ZoomGS），引入了一个特定于摄像头的编码，以构建每个虚拟摄像头的特定3D模型。借助提出的数据工厂，我们构建了一个用于DCSZ的合成数据集，并利用它来微调FI模型。此外，我们收集了没有真实数据的现实世界双重缩放图像进行评估。我们使用多种FI方法进行了广泛的实验。结果显示，微调后的FI模型在DCSZ任务上比原始模型取得了显著的性能提升。数据集、代码和预训练模型将公开可用。