# LATTICE: Democratize High-Fidelity 3D Generation at Scale

arXiv: https://arxiv.org/abs/2512.03052 · HTML: https://mlsanity.com/paper/2512.03052/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 66 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (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)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [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)
- [SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers](https://mlsanity.com/paper/2401.08740/) (2024)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis](https://mlsanity.com/paper/2111.04276/) (2021)
- [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)
- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding](https://mlsanity.com/paper/2212.05171/) (2022)
- [MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers](https://mlsanity.com/paper/2311.15475/) (2023)
- [CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation](https://mlsanity.com/paper/2110.02624/) (2021)
- [Uni3D: Exploring Unified 3D Representation at Scale](https://mlsanity.com/paper/2310.06773/) (2023)
- [Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation](https://mlsanity.com/paper/2306.17115/) (2023)
- [TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models](https://mlsanity.com/paper/2502.06608/) (2025)

## Built on by (most cited first, 30 of 44)

- [UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement](https://mlsanity.com/paper/2512.21185/) (2025)
- [LATO: 3D Mesh Flow Matching with Structured TOpology Preserving LAtents](https://mlsanity.com/paper/2603.06357/) (2026)
- [PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World](https://mlsanity.com/paper/2605.05163/) (2026)
- [Elastic Diffusion Transformer](https://mlsanity.com/paper/2602.13993/) (2026)
- [LATO.2: Factorized 3D Mesh Generation with Vertex and Topology Flow](https://mlsanity.com/paper/2607.10623/) (2026)
- [CubePart: An Open-Vocabulary Part-Controllable 3D Generator](https://mlsanity.com/paper/2605.28763/) (2026)
- [Nexus: Native Mesh Generation with Diffusion](https://mlsanity.com/paper/2607.13563/) (2026)
- [Points-to-3D: Structure-Aware 3D Generation with Point Cloud Priors](https://mlsanity.com/paper/2603.18782/) (2026)
- [Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation](https://mlsanity.com/paper/2605.13862/) (2026)
- [Generative 3D Gaussians with Learned Density Control](https://mlsanity.com/paper/2605.16355/) (2026)
- [EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers](https://mlsanity.com/paper/2605.16745/) (2026)
- [ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation](https://mlsanity.com/paper/2605.21121/) (2026)
- [SpatialCrafter: Single Image World Modeling with Generative 3D Proxies](https://mlsanity.com/paper/2608.27073/) (2026)
- [FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute](https://mlsanity.com/paper/2609.08848/) (2026)
- [Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models](https://mlsanity.com/paper/2603.22782/) (2026)
- [Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation](https://mlsanity.com/paper/2604.09231/) (2026)
- [Velocity-Space 3D Asset Editing](https://mlsanity.com/paper/2605.07385/) (2026)
- [DVD: Discrete Voxel Diffusion for 3D Generation and Editing](https://mlsanity.com/paper/2605.07971/) (2026)
- [AssetGen: Deployable 3D Asset Generation at Interactive Speed](https://mlsanity.com/paper/2605.26137/) (2026)
- [DreamCharacter-1: From 3D Generative Foundation Models to Product-Ready Character Generation](https://mlsanity.com/paper/2607.07817/) (2026)
- [TanGO: Training-Free 3D Editing via Tangent-Space Guidance and Optimization](https://mlsanity.com/paper/2607.14927/) (2026)
- [NuiWorld: Exploring a Scalable Framework for End-to-End Controllable World Generation](https://mlsanity.com/paper/2601.19048/) (2026)
- [SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation](https://mlsanity.com/paper/2603.14152/) (2026)
- [Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design](https://mlsanity.com/paper/2606.27923/) (2026)
- [PolyFlow: Continuous Topology Embedding Flow Matching for Artist-style Mesh Generation](https://mlsanity.com/paper/2606.30673/) (2026)
- [MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction](https://mlsanity.com/paper/2607.24436/) (2026)
- [ZipTok3D: High-Fidelity 3D Tokenization with Compact Token Prefixes](https://mlsanity.com/paper/2609.01740/) (2026)
- [Flow3D-OPD: Multi-Teacher On-Policy Distillation for 3D Geometry Generation with Flow-Matching Diffusion Transformer](https://mlsanity.com/paper/2609.07137/) (2026)
- [Point-MF: Stabilizing One-Step Mean Flows for Single-Image Point Cloud Reconstruction](https://mlsanity.com/paper/2604.24586/) (2026)
- [ISAP-3D: Identity-Slot Aligned Part-Aware 3D Generation](https://mlsanity.com/paper/2606.12099/) (2026)

## Abstract (arXiv)

We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis benefits from fixed spatial grids and well-established transformer architectures, 3D generation remains fundamentally more challenging due to the need to predict both spatial structure and detailed geometric surfaces from scratch. These challenges are exacerbated by the computational complexity of existing 3D representations and the lack of structured and scalable 3D asset encoding schemes. To address this, we propose VoxSet, a semi-structured representation that compresses 3D assets into a compact set of latent vectors anchored to a coarse voxel grid, enabling efficient and position-aware generation. VoxSet retains the simplicity and compression advantages of prior VecSet methods while introducing explicit structure into the latent space, allowing positional embeddings to guide generation and enabling strong token-level test-time scaling. Built upon this representation, LATTICE adopts a two-stage pipeline: first generating a sparse voxelized geometry anchor, then producing detailed geometry using a rectified flow transformer. Our method is simple at its core, but supports arbitrary resolution decoding, low-cost training, and flexible inference schemes, achieving state-of-the-art performance on various aspects, and offering a significant step toward scalable, high-quality 3D asset creation.