ZoomDiff is a high‑fidelity diffusion model designed to improve dual‑camera smooth zooming by producing photo‑realistic transitions. It strengthens dual‑image conditional guidance during denoising, injects flow‑aligned multi‑scale features from the VAE encoder into the decoder to recover high‑frequency details, and uses flow‑guided temporal consistency supervision to ensure smoother transitions. Experiments on synthetic and real‑world datasets show that ZoomDiff outperforms state‑of‑the‑art methods both quantitatively and qualitatively.
Digital zoom transitions between dual cameras often exhibit conspicuous discontinuities in geometric structure and chromatic consistency, degrading the user experience. While recent dual-camera smooth...
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