OmniZoom: AUniversal Plug-and-Play Paradigm for Cross-Device Smooth Zoom Interpolation
–Neural Information Processing Systems
Dual-camera smartphones suffer from geometric and photometric inconsistencies during zoom transitions, primarily due to disparities in intrinsic/extrinsic parameters and divergent image processing pipelines between the two cameras. Existing interpolation methods struggle to effectively address this issue, constrained by the lack of ground-truth datasets and motion ambiguity in dynamic scenarios. To overcome these challenges, we propose OmniZoom, a universal plug-and-play paradigm for cross-device smooth zoom interpolation. Specifically, we present a novel cross-device virtual data generation method utilizing 3DGaussian Splatting. This method tackles data scarcity by decoupling geometric features via spatial transition modeling and correcting photometric variations with dynamic color adaptation. It is further enhanced by cross-domain consistency learning for device-agnostic semantic alignment.
Neural Information Processing Systems
Jun-23-2026, 01:09:26 GMT
- Country:
- Europe (0.28)
- North America > United States (0.28)
- Genre:
- Research Report > Experimental Study (1.00)
- Technology:
- Information Technology
- Sensing and Signal Processing > Image Processing (1.00)
- Communications > Mobile (0.89)
- Artificial Intelligence
- Vision (1.00)
- Natural Language (0.93)
- Representation & Reasoning (0.92)
- Machine Learning
- Neural Networks > Deep Learning (0.67)
- Statistical Learning (0.67)
- Information Technology