  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.
