# Optimizing 4D Gaussians for Dynamic Scene Video from Single Landscape Images arXiv: https://arxiv.org/abs/2504.05458 ยท HTML: https://mlsanity.com/paper/2504.05458/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 43 references that are in the map. - First posted on arXiv 4 Apr 2025. ## Builds on (most cited first, 30 of 33) - [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) - [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021) - [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023) - [Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction](https://mlsanity.com/paper/2309.13101/) (2023) - [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023) - [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023) - [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023) - [ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis](https://mlsanity.com/paper/2409.02048/) (2024) - [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019) - [SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes](https://mlsanity.com/paper/2312.14937/) (2023) - [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023) - [Single-View View Synthesis with Multiplane Images](https://mlsanity.com/paper/2004.11364/) (2020) - [LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2311.13384/) (2023) - [Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling](https://mlsanity.com/paper/2401.15977/) (2024) - [3D Photography using Context-aware Layered Depth Inpainting](https://mlsanity.com/paper/2004.04727/) (2020) - [DreamGaussian4D: Generative 4D Gaussian Splatting](https://mlsanity.com/paper/2312.17142/) (2023) - [Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models](https://mlsanity.com/paper/2312.13763/) (2023) - [3D Ken Burns Effect from a Single Image](https://mlsanity.com/paper/1909.05483/) (2019) - [4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency](https://mlsanity.com/paper/2312.17225/) (2023) - [GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis](https://mlsanity.com/paper/2312.11458/) (2023) - [Layer-structured 3D Scene Inference via View Synthesis](https://mlsanity.com/paper/1807.10264/) (2018) - [Animating Pictures with Eulerian Motion Fields](https://mlsanity.com/paper/2011.15128/) (2020) - [One Shot 3D Photography](https://mlsanity.com/paper/2008.12298/) (2020) - [Controllable Animation of Fluid Elements in Still Images](https://mlsanity.com/paper/2112.03051/) (2021) - [Text2Immersion: Generative Immersive Scene with 3D Gaussians](https://mlsanity.com/paper/2312.09242/) (2023) - [Text-Guided Synthesis of Eulerian Cinemagraphs](https://mlsanity.com/paper/2307.03190/) (2023) - [VividDream: Generating 3D Scene with Ambient Dynamics](https://mlsanity.com/paper/2405.20334/) (2024) - [Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image](https://mlsanity.com/paper/2308.10257/) (2023) - [DeepLandscape: Adversarial Modeling of Landscape Video](https://mlsanity.com/paper/2008.09655/) (2020) ## Built on by (most cited first, 4 of 4) - [Geometry-Aware Single-Image 4D Synthesis via Dense Trajectory Generation](https://mlsanity.com/paper/2512.05044/) (2025) - [AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation](https://mlsanity.com/paper/2607.18539/) (2026) - [LivingWorld: Interactive 4D World Generation with Environmental Dynamics](https://mlsanity.com/paper/2604.01641/) (2026) - [Eulerian Motion Reconstruction for Water Scenery](https://mlsanity.com/paper/2609.38622/) (2026) ## Abstract (arXiv) To achieve realistic immersion in landscape images, fluids such as water and clouds need to move within the image while revealing new scenes from various camera perspectives. Recently, a field called dynamic scene video has emerged, which combines single image animation with 3D photography. These methods use pseudo 3D space, implicitly represented with Layered Depth Images (LDIs). LDIs separate a single image into depth-based layers, which enables elements like water and clouds to move within the image while revealing new scenes from different camera perspectives. However, as landscapes typically consist of continuous elements, including fluids, the representation of a 3D space separates a landscape image into discrete layers, and it can lead to diminished depth perception and potential distortions depending on camera movement. Furthermore, due to its implicit modeling of 3D space, the output may be limited to videos in the 2D domain, potentially reducing their versatility. In this paper, we propose representing a complete 3D space for dynamic scene video by modeling explicit representations, specifically 4D Gaussians, from a single image. The framework is focused on optimizing 3D Gaussians by generating multi-view images from a single image and creating 3D motion to optimize 4D Gaussians. The most important part of proposed framework is consistent 3D motion estimation, which estimates common motion among multi-view images to bring the motion in 3D space closer to actual motions. As far as we know, this is the first attempt that considers animation while representing a complete 3D space from a single landscape image. Our model demonstrates the ability to provide realistic immersion in various landscape images through diverse experiments and metrics. Extensive experimental results are https://cvsp-lab.github.io/ICLR2025_3D-MOM/.