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Eulerian Motion Reconstruction for Water Scenery
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Chuhan Chen, Yen-Chi Cheng, Ayush Saraf, Rajvi Shah, Tuotuo Li, Johannes Kopf, Chen Gao, Hung-Yu Tseng, Deva Ramanan, Matthew O'Toole, Changil Kim

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ResearcharXiv cs.CV

Eulerian Motion Reconstruction for Water Scenery

arXiv:2609.38622v1 Announce Type: new Abstract: Reconstructing and animating water scenery from nature produces compelling and immersive visual experiences. Previous work examined this task from the perspective of 2D video textures, with the goal of creating a looping video. In our work, we tackle the problem from a 3D perspective, creating a looping 4D dynamic reconstruction which can be interactively rendered from novel viewpoints from a single non-looping 2D source video. We represent motion as a 3D static \textit{Eulerian} motion field that advects canonical Gaussian splats that are cyclically reborn at fixed time periods, supervised using rendering losses. To model non-periodic and stochastic dynamics present in real-world scenes, we add a non-periodic, time-varying residual term to capture deviations from the static Eulerian motion field. We show quantitatively and qualitatively that our framework enables photorealistic animation of water scenes better than prior art.

Original source

This story was published by arXiv cs.CV and written by Chuhan Chen, Yen-Chi Cheng, Ayush Saraf, Rajvi Shah, Tuotuo Li, Johannes Kopf, Chen Gao, Hung-Yu Tseng, Deva Ramanan, Matthew O'Toole, Changil Kim. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on arxiv.org

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