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Multiview Regenerative Morphing with Dual Flows

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

This paper aims to address a new task of image morphing under a multiview setting, which takes two sets of multiview images as the input and generates intermediate renderings that not only exhibit smooth transitions between the two input sets but also ensure visual consistency across different views at any transition state. To achieve this goal, we propose a novel approach called Multiview Regenerative Morphing that formulates the morphing process as an optimization to solve for rigid transformation and optimal-transport interpolation. Given the multiview input images of the source and target scenes, we first learn a volumetric representation that models the geometry and appearance for each scene to enable the rendering of novel views. Then, the morphing between the two scenes is obtained by solving optimal transport between the two volumetric representations in Wasserstein metrics. Our approach does not rely on user-specified correspondences or 2D/3D input meshes, and we do not assume any predefined categories of the source and target scenes. The proposed view-consistent interpolation scheme directly works on multiview images to yield a novel and visually plausible effect of multiview free-form morphing. Code: https://github.com/jimtsai23/MorphFlow

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Acknowledgements

This work was supported in part by the MOST grants 110-2634-F-007-027 and 111-2221-E-001-011-MY2 of Taiwan. We are grateful to National Center for High-performance Computing for providing computational resources and facilities.

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Correspondence to Hwann-Tzong Chen .

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Tsai, CJ., Sun, C., Chen, HT. (2022). Multiview Regenerative Morphing with Dual Flows. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13676. Springer, Cham. https://doi.org/10.1007/978-3-031-19787-1_28

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