# UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement

arXiv: https://arxiv.org/abs/2512.21185 · HTML: https://mlsanity.com/paper/2512.21185/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 29 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 27 of 27)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models](https://mlsanity.com/paper/2301.11445/) (2023)
- [CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets](https://mlsanity.com/paper/2406.13897/) (2024)
- [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](https://mlsanity.com/paper/2404.07191/) (2024)
- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models](https://mlsanity.com/paper/2502.06608/) (2025)
- [Native and Compact Structured Latents for 3D Generation](https://mlsanity.com/paper/2512.14692/) (2025)
- [Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material](https://mlsanity.com/paper/2506.15442/) (2025)
- [Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention](https://mlsanity.com/paper/2505.17412/) (2025)
- [Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging](https://mlsanity.com/paper/2503.22236/) (2025)
- [Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders](https://mlsanity.com/paper/2412.17808/) (2024)
- [Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling](https://mlsanity.com/paper/2505.14521/) (2025)
- [Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets](https://mlsanity.com/paper/2505.07747/) (2025)
- [SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling](https://mlsanity.com/paper/2503.21732/) (2025)
- [Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention](https://mlsanity.com/paper/2507.17745/) (2025)
- [LATTICE: Democratize High-Fidelity 3D Generation at Scale](https://mlsanity.com/paper/2512.03052/) (2025)
- [ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soups](https://mlsanity.com/paper/2005.11621/) (2020)
- [Unleashing Vecset Diffusion Model for Fast Shape Generation](https://mlsanity.com/paper/2503.16302/) (2025)

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

- [Feedforward 3D Editing Learns from Semantic-Part Transformation](https://mlsanity.com/paper/2605.27351/) (2026)
- [Procedura: Agentic 3D Modeling with Procedural Control](https://mlsanity.com/paper/2608.26238/) (2026)
- [EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers](https://mlsanity.com/paper/2605.16745/) (2026)
- [Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models](https://mlsanity.com/paper/2603.22782/) (2026)
- [DVD: Discrete Voxel Diffusion for 3D Generation and Editing](https://mlsanity.com/paper/2605.07971/) (2026)
- [DreamCharacter-1: From 3D Generative Foundation Models to Product-Ready Character Generation](https://mlsanity.com/paper/2607.07817/) (2026)
- [SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation](https://mlsanity.com/paper/2603.14152/) (2026)
- [AnySurf: Any Surface Generation with Directed Edge](https://mlsanity.com/paper/2605.26149/) (2026)
- [SuperVoxelGPT: Adaptive and Ordered 3D Tokenization for Autoregressive Shape Generation](https://mlsanity.com/paper/2605.29655/) (2026)
- [Point-MF: Stabilizing One-Step Mean Flows for Single-Image Point Cloud Reconstruction](https://mlsanity.com/paper/2604.24586/) (2026)
- [TOPOS: High-Fidelity and Efficient Industry-Grade 3D Head Generation](https://mlsanity.com/paper/2605.14594/) (2026)
- [InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360deg Image](https://mlsanity.com/paper/2607.03990/) (2026)
- [Beyond Global Latents: Chunk-Based Sparse Grid VAE for Scalable 3D Modeling](https://mlsanity.com/paper/2608.02016/) (2026)
- [PLSR: Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion](https://mlsanity.com/paper/2609.06436/) (2026)
- [Seg3DParts: Segmentation-Grounded Controllable Part-Level 3D Generation](https://mlsanity.com/paper/2609.36918/) (2026)
- [HI3D 3.0 (Twinkle3D): Object-specific 3D Asset Generation with High Resolution](https://mlsanity.com/paper/2610.11685/) (2026)

## Abstract (arXiv)

In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipeline: a coarse global structure is first synthesized and then refined to produce detailed, high-quality geometry. To support reliable 3D generation, we develop a comprehensive data processing pipeline that includes a novel watertight processing method and high-quality data filtering. This pipeline improves the geometric quality of publicly available 3D datasets by removing low-quality samples, filling holes, and thickening thin structures, while preserving fine-grained geometric details. To enable fine-grained geometry refinement, we decouple spatial localization from geometric detail synthesis in the diffusion process. We achieve this by performing voxel-based refinement at fixed spatial locations, where voxel queries derived from coarse geometry provide explicit positional anchors encoded via RoPE, allowing the diffusion model to focus on synthesizing local geometric details within a reduced, structured solution space. Our model is trained exclusively on publicly available 3D datasets, achieving strong geometric quality despite limited training resources. Extensive evaluations demonstrate that UltraShape 1.0 performs competitively with existing open-source methods in both data processing quality and geometry generation. All code and trained models will be released to support future research.