# SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis

arXiv: https://arxiv.org/abs/2411.16443 · HTML: https://mlsanity.com/paper/2411.16443/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 113 of its 144 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 113)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)

## Built on by (most cited first, 30 of 34)

- [FlashWorld: High-quality 3D Scene Generation within Seconds](https://mlsanity.com/paper/2510.13678/) (2025)
- [Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction](https://mlsanity.com/paper/2601.04090/) (2026)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [GeoVideo: Introducing Geometric Regularization into Video Generation Model](https://mlsanity.com/paper/2512.03453/) (2025)
- [SteerX: Creating Any Camera-Free 3D and 4D Scenes with Geometric Steering](https://mlsanity.com/paper/2503.12024/) (2025)
- [VideoRFSplat: Direct Scene-Level Text-to-3D Gaussian Splatting Generation with Flexible Pose and Multi-View Joint Modeling](https://mlsanity.com/paper/2503.15855/) (2025)
- [WorldSplat: Gaussian-Centric Feed-Forward 4D Scene Generation for Autonomous Driving](https://mlsanity.com/paper/2509.23402/) (2025)
- [Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator](https://mlsanity.com/paper/2510.13454/) (2025)
- [ReDirector: Creating Any-Length Video Retakes with Rotary Camera Encoding](https://mlsanity.com/paper/2511.19827/) (2025)
- [Flow Straight and Fast in Hilbert Space: Functional Rectified Flow](https://mlsanity.com/paper/2509.10384/) (2025)
- [Omni-3DEdit: Generalized Versatile 3D Editing in One-Pass](https://mlsanity.com/paper/2603.17841/) (2026)
- [ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies](https://mlsanity.com/paper/2506.14315/) (2025)
- [Understanding, Accelerating, and Improving MeanFlow Training](https://mlsanity.com/paper/2511.19065/) (2025)
- [Elucidating Rectified Flow with Deterministic Sampler: Polynomial Discretization Complexity for Multi and One-step Models](https://mlsanity.com/paper/2508.08735/) (2025)
- [Any 3D Scene is Worth 1K Tokens: 3D-Grounded Representation for Scene Generation at Scale](https://mlsanity.com/paper/2604.11331/) (2026)
- [Geo-Align: Video Generation Alignment via Metric Geometry Reward](https://mlsanity.com/paper/2605.23903/) (2026)
- [Free Lunch for Stabilizing Rectified Flow Inversion](https://mlsanity.com/paper/2602.11850/) (2026)
- [OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder](https://mlsanity.com/paper/2603.16099/) (2026)
- [Stitched Value Model for Diffusion Alignment](https://mlsanity.com/paper/2605.19804/) (2026)
- [UMI3D: Robust 3D Generation on Unconstrained Multi-Image Inputs via Simultaneous Focus Cross-Attention Routing](https://mlsanity.com/paper/2607.24298/) (2026)
- [LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures](https://mlsanity.com/paper/2507.06109/) (2025)
- [ReCoGS: Real-time ReColoring for Gaussian Splatting scenes](https://mlsanity.com/paper/2511.18441/) (2025)
- [CustomX: Unified Character, Action, and Scene Customization in Video World Models](https://mlsanity.com/paper/2512.17796/) (2025)
- [Unified Sensor Simulation for Autonomous Driving](https://mlsanity.com/paper/2602.05617/) (2026)
- [ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation](https://mlsanity.com/paper/2603.09819/) (2026)
- [SARe: Structure-Aware Generative 3D Fragment Reassembly](https://mlsanity.com/paper/2603.21611/) (2026)
- [ATATA: One Algorithm to Align Them All](https://mlsanity.com/paper/2601.11194/) (2026)
- [Logi-PAR: Logic-Infused Patient Activity Recognition via Differentiable Rule](https://mlsanity.com/paper/2603.05184/) (2026)
- [Monocular Depth Estimation from a Single Image: Progress and Opportunities](https://mlsanity.com/paper/2609.01172/) (2026)

## Abstract (arXiv)

Text-based generation and editing of 3D scenes hold significant potential for streamlining content creation through intuitive user interactions. While recent advances leverage 3D Gaussian Splatting (3DGS) for high-fidelity and real-time rendering, existing methods are often specialized and task-focused, lacking a unified framework for both generation and editing. In this paper, we introduce SplatFlow, a comprehensive framework that addresses this gap by enabling direct 3DGS generation and editing. SplatFlow comprises two main components: a multi-view rectified flow (RF) model and a Gaussian Splatting Decoder (GSDecoder). The multi-view RF model operates in latent space, generating multi-view images, depths, and camera poses simultaneously, conditioned on text prompts, thus addressing challenges like diverse scene scales and complex camera trajectories in real-world settings. Then, the GSDecoder efficiently translates these latent outputs into 3DGS representations through a feed-forward 3DGS method. Leveraging training-free inversion and inpainting techniques, SplatFlow enables seamless 3DGS editing and supports a broad range of 3D tasks-including object editing, novel view synthesis, and camera pose estimation-within a unified framework without requiring additional complex pipelines. We validate SplatFlow's capabilities on the MVImgNet and DL3DV-7K datasets, demonstrating its versatility and effectiveness in various 3D generation, editing, and inpainting-based tasks.