HI3D 3.0 (Twinkle3D): Object-specific 3D Asset Generation with High Resolution

arXiv:2610.11685 · cs.CV, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Project page: https://mathmagic-official.github.io/twinkle3d

Terminology

• Fidelity — 27× in this paper · explained in 2 episodes
• Alignment — 22× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 20× in this paper
• In International Conference on Learning Representations — 9× in this paper
• Transformer — 9× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• International Conference on Computer Vision — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Flow Matching — 3× in this paper · explained in 8 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• ACM Transactions on Graphics — 2× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Speculative Decoding — 1× in this paper · explained in 2 episodes

Sources

• PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models
• Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
• PP-OCR: A Practical Ultra Lightweight OCR System
• Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets
• Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation
• Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale
• MeshCraft: Exploring Efficient and Controllable Mesh Generation with Flow-based DiTs
• UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
• Shap-E: Generating Conditional 3D Implicit Functions
• Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
• Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets
• TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
• Point-E: A System for Generating 3D Point Clouds from Complex Prompts
• SAM 3D: 3Dfy Anything in Images
• Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodings
• Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
• TripoSR: Fast 3D Object Reconstruction from a Single Image
• AssetGen: Deployable 3D Asset Generation at Interactive Speed
• iFlame: Interleaving Full and Linear Attention for Efficient Mesh Generation
• LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models