arXiv Computer Vision By Peiyu Liu, Dingxi Zhang, Federico Tombari, Marc Pollefeys, Christina Tsalicoglou, Daniel Barath

SplashSplat: Reconstructing Splashing Liquids from Real-World Multi-View Videos

Read the original on arXiv Computer Vision →

SplashSplat introduces a new benchmark of 20 real-world splashing liquid scenes captured with seven synchronized 4K cameras at 60 fps, providing per-view liquid and container masks and fixed evaluation splits. The method reconstructs per‑frame liquid signed distance fields (SDFs) from these masks, fuses them into a coarse velocity field, and uses Lagrangian carriers to generate differentiable local Gaussian representations for rendering. SplashSplat outperforms existing dynamic Gaussian splatting techniques on both real and synthetic data, offering more physically plausible motion, lower training cost, and enabling temporal interpolation and style transfer without re‑optimization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI
Jul 29

Physics-Grounded Fluid Video Generation with a Simulation Dataset and Dual-Stream Optical-Flow Supervision

arXiv:2607. 25321v1 Announce Type: new Abstract: Video diffusion models generate visually compelling content but routinely violate elementary physics when the subject involves fluids: liquid columns break apart in mid-air, container water levels fail to rise as liquid is poured in, and splashes disperse without regard to momentum or gravity.

By Ruijie Su, Yuanzhi Liang, Xiaohua Xie, Jianhuang Lai
arXiv Computer Vision
Oct 12

Make It Flow: Animating Water in 3D Gaussian Splatting Scenes

The paper introduces a technique for animating water within static 3D Gaussian Splatting (3DGS) scenes, allowing realistic, looping water motion without dynamic capture or physical simulation. It estimates 2D optical flow from images or renderings, lifts it into a 3D Eulerian motion field, and deforms Gaussian primitives accordingly, producing temporally seamless animations. The method maintains real‑time rendering performance and improves visual quality and efficiency compared to prior dynamic Gaussian splatting and 3D cinemagraph approaches.

By Kjell Keune, Florian Hahlbohm, Jannis M\"oller, Susana Castillo, Martin Eisemann, Marcus Magnor
Hugging Face Trending Papers
Oct 9

Make It Flow: Animating Water in 3D Gaussian Splatting Scenes

The paper introduces a technique for animating water within static 3D Gaussian Splatting (3DGS) scenes, allowing realistic motion without dynamic capture or physical simulation. It estimates 2D optical flow from images or renderings, lifts it into a 3D Eulerian motion field, and applies this to deform Gaussian primitives, creating temporally seamless looping animations. The method maintains real‑time rendering performance, improves perceptual realism, and supports free‑viewpoint navigation, outperforming prior dynamic Gaussian splatting and 3D cinemagraph approaches on a new waterfall dataset.