# LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field arXiv: https://arxiv.org/abs/2404.08966 ยท HTML: https://mlsanity.com/paper/2404.08966/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 53 references that are in the map. - First posted on arXiv 13 Apr 2024. ## Builds on (most cited first, 27 of 27) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023) - [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018) - [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016) - [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022) - [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021) - [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020) - [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020) - [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020) - [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021) - [Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction](https://mlsanity.com/paper/2309.13101/) (2023) - [HexPlane: A Fast Representation for Dynamic Scenes](https://mlsanity.com/paper/2301.09632/) (2023) - [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021) - [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023) - [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023) - [Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle](https://mlsanity.com/paper/2312.03431/) (2023) - [Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models](https://mlsanity.com/paper/2312.13763/) (2023) - [RobustNeRF: Ignoring Distractors with Robust Losses](https://mlsanity.com/paper/2302.00833/) (2023) - [Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs](https://mlsanity.com/paper/2304.10532/) (2023) - [Animating Pictures with Eulerian Motion Fields](https://mlsanity.com/paper/2011.15128/) (2020) - [Controllable Animation of Fluid Elements in Still Images](https://mlsanity.com/paper/2112.03051/) (2021) - [3D Cinemagraphy from a Single Image](https://mlsanity.com/paper/2303.05724/) (2023) - [Text-Guided Synthesis of Eulerian Cinemagraphs](https://mlsanity.com/paper/2307.03190/) (2023) - [Endless Loops: Detecting and Animating Periodic Patterns in Still Images](https://mlsanity.com/paper/2105.09374/) (2021) - [3D Video Loops from Asynchronous Input](https://mlsanity.com/paper/2303.05312/) (2023) - [StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN](https://mlsanity.com/paper/2403.14186/) (2024) - [Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning](https://mlsanity.com/paper/1708.02970/) (2017) ## Built on by (most cited first, 9 of 9) - [VividDream: Generating 3D Scene with Ambient Dynamics](https://mlsanity.com/paper/2405.20334/) (2024) - [Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects](https://mlsanity.com/paper/2412.06257/) (2024) - [DiffWind: Physics-Informed Differentiable Modeling of Wind-Driven Object Dynamics](https://mlsanity.com/paper/2603.09668/) (2026) - [SplitGaussian: Reconstructing Dynamic Scenes via Visual Geometry Decomposition](https://mlsanity.com/paper/2508.04224/) (2025) - [HiSC: Hierarchical Spatial Clustering Token Compression for Efficient 3D Scene Understanding](https://mlsanity.com/paper/2608.04610/) (2026) - [Dehallu3D: Hallucination-Mitigated 3D Generation from Single Image via Cyclic View Consistency Refinement](https://mlsanity.com/paper/2603.01601/) (2026) - [CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting](https://mlsanity.com/paper/2605.09279/) (2026) - [Knowledge as Orbit: Finite Collections as Phases of an Exactly Periodic Latent Generator](https://mlsanity.com/paper/2609.17417/) (2026) - [Make It Flow: Animating Water in 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2610.13860/) (2026) ## Abstract (arXiv) Cinemagraph is a unique form of visual media that combines elements of still photography and subtle motion to create a captivating experience. However, the majority of videos generated by recent works lack depth information and are confined to the constraints of 2D image space. In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling. To achieve this, we first employ the 3D-GS method to reconstruct 3D Gaussian point clouds from multi-view images of static scenes,incorporating shape regularization terms to prevent blurring or artifacts caused by object deformation. We then adopt an autoencoder tailored for 3D Gaussian to project it into feature space. To maintain the local continuity of the scene, we devise SuperGaussian for clustering based on the acquired features. By calculating the similarity between clusters and employing a two-stage estimation method, we derive an Eulerian motion field to describe velocities across the entire scene. The 3D Gaussian points then move within the estimated Eulerian motion field. Through bidirectional animation techniques, we ultimately generate a 3D Cinemagraph that exhibits natural and seamlessly loopable dynamics. Experiment results validate the effectiveness of our approach, demonstrating high-quality and visually appealing scene generation. The project is available at https://pokerlishao.github.io/LoopGaussian/.