mlsanityData to 7 Oct 2026

Water Simulation and Rendering from a Still Photograph

arXiv 2210.02553 ↗First posted 5 Oct 2022cs.GR cs.CVcited by 5

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Abstract (arXiv)

We propose an approach to simulate and render realistic water animation from a single still input photograph. We first segment the water surface, estimate rendering parameters, and compute water reflection textures with a combination of neural networks and traditional optimization techniques. Then we propose an image-based screen space local reflection model to render the water surface overlaid on the input image and generate real-time water animation. Our approach creates realistic results with no user intervention for a wide variety of natural scenes containing large bodies of water with different lighting and water surface conditions. Since our method provides a 3D representation of the water surface, it naturally enables direct editing of water parameters and also supports interactive applications like adding synthetic objects to the scene.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 47 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

20 of 47 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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8 more
  1. 2020A Physics-based Noise Formation Model for Extreme Low-light Raw Denoisingcited by 138
  2. 2019Single Image Reflection Removal through Cascaded Refinementcited by 58
  3. 2020Animating Pictures with Eulerian Motion Fieldscited by 54
  4. 2019Document Rectification and Illumination Correction using a Patch-based CNNcited by 37
  5. 2019Blind Geometric Distortion Correction on Images Through Deep Learningcited by 28
  6. 2017Two-Stream Convolutional Networks for Dynamic Texture Synthesiscited by 15
  7. 2019CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentationcited by 15
  8. 2015Fast Guided Filtercited by 12

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Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.