# Flatten Anything: Unsupervised Neural Surface Parameterization

arXiv: https://arxiv.org/abs/2405.14633 · HTML: https://mlsanity.com/paper/2405.14633/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 18 of 18)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation](https://mlsanity.com/paper/1802.05384/) (2018)
- [MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers](https://mlsanity.com/paper/2311.15475/) (2023)
- [Learning elementary structures for 3D shape generation and matching](https://mlsanity.com/paper/1908.04725/) (2019)
- [Shape Reconstruction by Learning Differentiable Surface Representations](https://mlsanity.com/paper/1911.11227/) (2019)
- [Neural Surface Maps](https://mlsanity.com/paper/2103.16942/) (2021)
- [Nuvo: Neural UV Mapping for Unruly 3D Representations](https://mlsanity.com/paper/2312.05283/) (2023)
- [TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations](https://mlsanity.com/paper/2006.10187/) (2020)
- [Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering](https://mlsanity.com/paper/1905.04571/) (2019)
- [Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis](https://mlsanity.com/paper/2212.08892/) (2022)
- [TEMPO: Feature-Endowed Teichm"uller Extremal Mappings of Point Clouds](https://mlsanity.com/paper/1511.06624/) (2015)
- [Spherical Conformal Parameterization of Genus-0 Point Clouds for Meshing](https://mlsanity.com/paper/1508.07569/) (2015)
- [DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization](https://mlsanity.com/paper/2212.06344/) (2022)
- [Free-boundary conformal parameterization of point clouds](https://mlsanity.com/paper/2010.15399/) (2020)

## Built on by (most cited first, 16 of 16)

- [Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation](https://mlsanity.com/paper/2509.12815/) (2025)
- [PartUV: Part-Based UV Unwrapping of 3D Meshes](https://mlsanity.com/paper/2511.16659/) (2025)
- [Auto-Regressive Surface Cutting](https://mlsanity.com/paper/2506.18017/) (2025)
- [Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation](https://mlsanity.com/paper/2503.15877/) (2025)
- [FlexPara: Flexible Neural Surface Parameterization](https://mlsanity.com/paper/2504.19210/) (2025)
- [MeshTailor: Cutting Seams via Generative Mesh Traversal](https://mlsanity.com/paper/2603.27309/) (2026)
- [Strips as Tokens: Artist Mesh Generation with Native UV Segmentation](https://mlsanity.com/paper/2604.09132/) (2026)
- [SeamCrafter: Enhancing Mesh Seam Generation for Artist UV Unwrapping via Reinforcement Learning](https://mlsanity.com/paper/2509.20725/) (2025)
- [Semantic UV mapping to improve texture inpainting for indoor scenes](https://mlsanity.com/paper/2407.09248/) (2024)
- [ArtUV: Artist-style UV Unwrapping](https://mlsanity.com/paper/2509.20710/) (2025)
- [Unsupervised Representation Learning for 3D Mesh Parameterization with Semantic and Visibility Objectives](https://mlsanity.com/paper/2509.25094/) (2025)
- [Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields](https://mlsanity.com/paper/2411.18415/) (2024)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)
- [SeamGen: Artist-Aligned UV Seam Generation via Graph Flow Matching](https://mlsanity.com/paper/2607.12379/) (2026)
- [SeamFlow: Structure-Aware Flow Matching on Edge Probabilities for Artist-Like UV Unwrapping](https://mlsanity.com/paper/2609.04751/) (2026)
- [UniBRep: Learning Unified Geometry and Topology for Image-conditioned B-Rep Generation](https://mlsanity.com/paper/2610.04092/) (2026)

## Abstract (arXiv)

Surface parameterization plays an essential role in numerous computer graphics and geometry processing applications. Traditional parameterization approaches are designed for high-quality meshes laboriously created by specialized 3D modelers, thus unable to meet the processing demand for the current explosion of ordinary 3D data. Moreover, their working mechanisms are typically restricted to certain simple topologies, thus relying on cumbersome manual efforts (e.g., surface cutting, part segmentation) for pre-processing. In this paper, we introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization via learning point-wise mappings between 3D points on the target geometric surface and adaptively-deformed UV coordinates within the 2D parameter domain. To mimic the actual physical procedures, we ingeniously construct geometrically-interpretable sub-networks with specific functionalities of surface cutting, UV deforming, unwrapping, and wrapping, which are assembled into a bi-directional cycle mapping framework. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information, thus significantly reducing the strict requirements for mesh quality and even applicable to unstructured point cloud data. More importantly, our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies, since its learning process adaptively finds reasonable cutting seams and UV boundaries. Extensive experiments demonstrate the universality, superiority, and inspiring potential of our proposed neural surface parameterization paradigm. Our code is available at https://github.com/keeganhk/FlattenAnything.