tencent-hunyuan/hunyuan3d-2.1

From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

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Summary Information

Updated 2 hours ago
Added to GitGenius on September 17th, 2026
Created on June 13th, 2025
Open Issues & Pull Requests: 153 (+0)
GitHub issues: Enabled
Number of forks: 606
Total Stargazers: 4,040 (+1)
Total Subscribers: 37 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.6 days
Mean response time: 21.2 days
90th percentile: 74.0 days
Tracked items: 115

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Issue Activity (beta)

Open issues: 132
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 384 days
Stale 30+ days: 132
Stale 90+ days: 127

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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  • 2025犀牛鸟开源人才培养活动 (10)

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Detailed Description

Hunyuan3D 2.1 is an image-to-3D generation tool that creates high-fidelity 3D assets with production-ready physically-based rendering materials from single images or text prompts.

The tool addresses the challenge of converting 2D visual input into complete 3D models suitable for professional use. It generates both geometry and PBR materials, which means the resulting assets include properly defined surface properties like albedo, normal maps, and roughness that allow them to render correctly under different lighting conditions. This approach bridges the gap between rapid 3D asset creation and production-quality output that would traditionally require manual refinement by 3D artists.

Developers and studios working on game development, virtual production, or 3D content pipelines should consider this tool if they need to accelerate asset creation from reference images or text descriptions. The inclusion of production-ready PBR materials distinguishes it from simpler 3D generation approaches that produce only geometry without proper material definitions, reducing the post-processing work required before assets can be integrated into rendering engines or game engines.

The project shows active development with regular updates to its codebase and ongoing refinement of its generation capabilities. The repository maintains comprehensive documentation and examples demonstrating the tool's functionality. Development activity indicates sustained effort in improving both the quality of generated assets and the robustness of the generation pipeline.