HY-World 2.0 is a multi-modal world model framework for generating and reconstructing 3D worlds from various input modalities. It produces editable 3D assets that can be imported into game engines, offering capabilities for both world generation and reconstruction.
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Links and model details
Process and understand human language for various applications
Example
Chatbots, sentiment analysis, content classification, entity extraction
Automate language-based tasks, improve user interactions, extract insights from text
Generate human-like text for various purposes
Example
Auto-complete suggestions, content drafting, template filling
Accelerate writing tasks, maintain consistency, scale content production
Translate between languages and adapt content for different audiences
Example
Multi-language support, tone adaptation, simplification
HY-World 2.0 is an open-source state-of-the-art world model. We will release all model weights, code, and technical details to facilitate reproducibility and advance research in this field. It accepts diverse input modalities — text, single-view images, multi-view images, and videos — and produces 3D world representations (meshes / Gaussian Splattings). The model offers two core capabilities: World Generation and World Reconstruction.
HY-World 2.0 is in the explainx.ai LLM directory. HY-World 2.0 is a multi-modal world model framework for generating and reconstructing 3D worlds from various input modalities. It produces editable 3D assets that can be imported into game engines, offering capabilities for both world generation and reconstruction.. It is labeled open-weights / public artifacts, with publisher field Tencent-Hunyuan and license Proprietary. Structured FAQs below clarify source, weights, and benchmark data. Canonical URL: /llms/hy-world-2-0.
Listing on explainx.ai. Information may change; verify with the publisher.
Reach global audiences, improve accessibility, tailor messaging
Prerequisites
Time Estimate
1-4 hours for basic integration
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when you need to process or generate natural language text, when prompting can solve the problem, and when occasional errors are acceptable with validation.
✗ Avoid when
Avoid when perfect accuracy is required, when real-time information is needed, for mission-critical decisions without human oversight, or when costs would exceed value delivered.
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