# Visibility-Guided Structured Measure Flow for Class-Conditioned 3D Gaussian Generation

arXiv: https://arxiv.org/abs/2609.27778 · HTML: https://mlsanity.com/paper/2609.27778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 40 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 30 of 32)

- [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)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (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)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [ABO: Dataset and Benchmarks for Real-World 3D Object Understanding](https://mlsanity.com/paper/2110.06199/) (2021)
- [Advances in Neural Rendering](https://mlsanity.com/paper/2111.05849/) (2021)
- [OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation](https://mlsanity.com/paper/2301.07525/) (2023)
- [3D-FUTURE: 3D Furniture shape with TextURE](https://mlsanity.com/paper/2009.09633/) (2020)
- [Text-to-3D using Gaussian Splatting](https://mlsanity.com/paper/2309.16585/) (2023)
- [Mixture of Volumetric Primitives for Efficient Neural Rendering](https://mlsanity.com/paper/2103.01954/) (2021)
- [GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models](https://mlsanity.com/paper/2310.08529/) (2023)
- [MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes](https://mlsanity.com/paper/2302.12249/) (2023)
- [DiffGS: Functional Gaussian Splatting Diffusion](https://mlsanity.com/paper/2410.19657/) (2024)
- [GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling](https://mlsanity.com/paper/2403.19655/) (2024)
- [L3DG: Latent 3D Gaussian Diffusion](https://mlsanity.com/paper/2410.13530/) (2024)
- [SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis](https://mlsanity.com/paper/2411.16443/) (2024)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [Atlas Gaussians Diffusion for 3D Generation](https://mlsanity.com/paper/2408.13055/) (2024)
- [GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation](https://mlsanity.com/paper/2411.08033/) (2024)
- [Generative 3D Gaussians with Learned Density Control](https://mlsanity.com/paper/2605.16355/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has made real-time, high-fidelity 3D rendering practical, yet turning this explicit representation into a native generative space remains an open challenge. Directly generating 3DGS objects is difficult because Gaussian primitives are unordered, variable-sized, locally dense, and highly sensitive to rendering behavior. We present VISTA-GS, a visibility-guided structured measure flow framework for class-conditioned 3D Gaussian generation. Instead of treating a 3DGS object as a flat primitive sequence or a generic latent token grid, we formulate it as a structured Gaussian measure weighted by opacity, anisotropic covariance, and multi-view visibility. Based on this formulation, we introduce a visibility-aware measure VAE that learns permutation-invariant, variable-size-compatible, and rendering-aware latent representations of 3DGS objects. We further develop a renderer-consistent measure flow that transports class-conditioned priors toward the learned 3DGS measure distribution while aligning the decoded objects with their multi-view rendering distributions. To preserve object layout and local details, VISTA-GS incorporates structure-preserving patch transport that couples global class semantics, local Gaussian measure patches, and spatial anchors during flow prediction. On VISTA-Obj30, VISTA-GS improves over the strongest baseline by roughly 60--72% across geometry, appearance, view-consistency error, and generation speed. This design enables efficient generation of coherent, detailed, and view-consistent 3D Gaussian objects without relying on per-instance optimization, multi-view image synthesis, or reconstruction-based lifting pipelines. Project code and model checkpoints will be released.