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HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels
HunyuanWorld Team, Zhenwei Wang, Yuhao Liu, Junta Wu, Zixiao Gu, Haoyuan Wang, Xuhui Zuo, Tianyu Huang, Wenhuan Li, Sheng Zhang, Yihang Lian, Yulin Tsai, Lifu Wang, Sicong Liu, Puhua Jiang, Xianghui Yang, Dongyuan Guo, Yixuan Tang, Xinyue Mao, Jiaao Yu, Junlin Yu, Jihong Zhang, Meng Chen, Liang Dong, Yiwen Jia, Chao Zhang, Yonghao Tan, Hao Zhang, Zheng Ye, Peng He, Runzhou Wu, Minghui Chen, Zhan Li, Wangchen Qin, Lei Wang, Yifu Sun, Lin Niu, Xiang Yuan, Xiaofeng Yang, Yingping He, Jie Xiao, Yangyu Tao, Jianchen Zhu, Jinbao Xue, Kai Liu, Chongqing Zhao, Xinming Wu, Tian Liu, Peng Chen, Di Wang, Yuhong Liu, Linus, Jie Jiang, Tengfei Wang, Chunchao Guo
TL;DR
Generating immersive, playable 3D worlds from text or images is difficult because video methods lack 3D consistency and 3D methods face data and representation constraints. HunyuanWorld 1.0 combines panoramic proxies with semantically layered mesh reconstruction and disentangled objects. The authors report state-of-the-art performance for text- and image-based 3D world generation, with applications in VR, physical simulation, and game development.
Problem
Existing video-based methods lack 3D consistency and rendering efficiency, while 3D-based methods face scarce scene data, inefficient representations, and limited object disentanglement.
Method
HunyuanWorld 1.0 uses panoramic images as world proxies, then performs semantic layering and mesh-based 3D reconstruction with disentangled object representations.
Results
The authors report state-of-the-art performance for immersive, explorable, and interactive 3D worlds across text- and image-based generation.
Takeaways & Limitations
The framework supports 360° experiences, mesh export, and applications in virtual reality, physical simulation, and game development.
Abstract
from arXiv · showhide
Creating immersive and playable 3D worlds from texts or images remains a fundamental challenge in computer vision and graphics. Existing world generation approaches typically fall into two categories: video-based methods that offer rich diversity but lack 3D consistency and rendering efficiency, and 3D-based methods that provide geometric consistency but struggle with limited training data and memory-inefficient representations. To address these limitations, we present HunyuanWorld 1.0, a novel framework that combines the best of both worlds for generating immersive, explorable, and interactive 3D scenes from text and image conditions. Our approach features three key advantages: 1) 360° immersive experiences via panoramic world proxies; 2) mesh export capabilities for seamless compatibility with existing computer graphics pipelines; 3) disentangled object representations for augmented interactivity. The core of our framework is a semantically layered 3D mesh representation that leverages panoramic images as 360° world proxies for semantic-aware world decomposition and reconstruction, enabling the generation of diverse 3D worlds. Extensive experiments demonstrate that our method achieves state-of-the-art performance in generating coherent, explorable, and interactive 3D worlds while enabling versatile applications in virtual reality, physical simulation, game development, and interactive content creation.
1 Introduction
HunyuanWorld 1.0 addresses limitations of video- and 3D-based world generation with a semantically layered mesh framework that supports immersive, explorable, and interactive worlds from text or images.
- Motivation: Video-based methods offer rich diversity but lack true 3D consistency, incur sequential rendering costs, and remain incompatible with interactive graphics pipelines.Their 2D frame-based representation can cause long-range content drift and incoherence.
- Motivation: 3D-based methods provide geometric consistency and pipeline compatibility, but world-level generation is limited by scarce 3D scene data, inefficient representations, and monolithic scenes.Monolithic scenes do not separate individual objects for interactive manipulation.
- Framework: HunyuanWorld 1.0 combines 2D and 3D generation through a semantically layered 3D mesh representation with instance-level object modeling.The framework uses panoramic world proxies, mesh export, and disentangled object representations.
- Framework: The staged framework generates panoramas, decomposes scenes into semantic layers, reconstructs aligned 3D worlds, and supports long-range exploration with world-consistent video diffusion and caching.These stages extend navigation beyond the original viewpoints while preserving world consistency.
- Reported outcome: The authors report state-of-the-art performance for immersive, explorable, and interactive 3D worlds across diverse artistic styles and scene types.They also describe applications in virtual reality, game development, physical simulation, and object interaction.
2 Technical Details
HunyuanWorld 1.0 uses panoramas as world proxies, then layers and reconstructs them into 3D meshes for text- and image-conditioned world generation. Its pipeline adds scene-aware conditioning, panoramic data curation, object decomposition, and layer completion.
- Pipeline overview: The staged pipeline generates a panorama from text or images, then performs world layering and reconstruction to produce a 3D world.Panoramas provide a proxy representation before semantic decomposition and 3D reconstruction.
- Panorama generation: Text-to-panorama generation enhances user prompts, while image-to-panorama generation projects a pinhole image into ERP space and fills missing content.Image conditioning preserves the input image while completing a coherent 360° panorama.
- Panorama generation: Panorama-DiT conditions diffusion on enhanced text prompts or on a VAE encoding of the projected image concatenated with noisy latent features.The image-conditioned path provides additional control through the projected input image.
- Panorama generation: Panoramic generation addresses spherical distortion and stitching discontinuities through elevation-aware augmentation and circular denoising strategies.The stated goal is improved robustness to viewpoint variation and boundary artifacts.
- Data curation: Training panoramas undergo automatic quality assessment and expert inspection, while captioning uses recaptioning, LLM distillation, and professional verification.The curation process filters visual artifacts and image-text misalignment.
- Scene-aware conditioning: Scene-aware prompts identify salient input objects as negative prompts and describe a complete 360° scene hierarchically to avoid duplicated objects.This strategy addresses redundant reproduction of prominent objects in image-conditioned generation.
- World layering: The semantic representation decomposes scenes into layers because panoramas lack occluded-region information and support rotation rather than free translation.The design follows a sky, terrain/background, and object-asset organization inspired by practical 3D modeling.
- World layering: Layer decomposition handles panorama-boundary discontinuities with circular padding before object detection, followed by coordinate remapping and pixel-wise segmentation.This treats objects crossing ERP boundaries as contiguous entities.
3 Model Evaluation
HunyuanWorld 1.0 is evaluated for panorama and 3D-world generation from image and text conditions, using quantitative and qualitative comparisons. It consistently outperforms baselines while supporting immersive environments, mesh export, and object-level interaction.
- Evaluation Scope: The evaluation covers image-to-panorama, text-to-panorama, image-to-world, and text-to-world generation.Benchmarks include real and AI-generated images plus crowd-sourced prompts spanning scene types, styles, and lengths.
- Panorama Generation: HunyuanWorld 1.0 consistently outperforms both baseline methods across image-to-panorama evaluation metrics.Six 90°-FOV views at 960 × 960 resolution provide complete 360° coverage for comparison.
- Panorama Generation: HunyuanWorld 1.0 achieves superior text-to-panorama performance across all evaluation metrics and diverse artistic styles.Qualitative results show fidelity to textual descriptions while maintaining high visual quality.
- 3D World Generation: HunyuanWorld 1.0 outperforms baselines in image-to-world visual quality and semantic alignment while producing geometrically consistent novel views.Qualitative comparisons report superior visual quality and geometric consistency relative to baseline approaches.
- 3D World Generation: HunyuanWorld 1.0 outperforms baselines across text-to-world metrics, with high visual fidelity and strong alignment to input descriptions.The comparison notes that Director3D struggles with long-range camera trajectories in many test cases.
- Applications: The framework supports VR browsing, physics-based simulation, game-engine integration, and precise manipulation of separate objects.Its three stated advantages are 360° experiences, mesh export, and disentangled object modeling.
4 Related Work
Related work spans immersive panorama generation, video-based world generation, and 3D world generation. HunyuanWorld builds on layered representations and targets layered 3D world reconstruction from panoramic proxies.
- Immersive Scene Image Generation: Immersive scene image generation adapts latent diffusion models to panoramic image synthesis using methods such as MVDiffusion, PanoDiff, and DiffPano.Later approaches incorporate spatial priors for panorama generation.
- Video-Based World Generation: Video-based world generation exploits video diffusion models’ world knowledge and increasingly incorporates 3D constraints such as camera trajectories and 3D points.Examples include encoding camera poses as Plücker coordinates.
- 3D World Generation: 3D world generation includes procedural and learning-based methods, with 3D assets offering stronger consistency and computer-graphics compatibility than videos.Existing methods include progressive novel-view generation and scene reconstruction through panorama inpainting and 3D Gaussian Splatting.
5 Conclusion
HunyuanWorld 1.0 generates immersive, explorable, and interactive 3D worlds from text and images using layered meshes and panoramic proxies. Experiments report state-of-the-art performance and support applications across VR, simulation, and games.
- Conclusion: The framework combines panoramic world proxies with semantically layered 3D meshes and disentangled objects for text- and image-based world generation.The representation targets diverse, 3D-consistent worlds with enhanced interactivity.
- Conclusion: HunyuanWorld 1.0 achieves state-of-the-art performance for both text-based and image-based 3D world generation.The conclusion identifies 360° immersion, mesh export, and disentangled object representations as key features.
- Conclusion: The framework supports applications in virtual reality, physical simulation, and game development and is positioned as a baseline for future research.These applications follow from its immersive experiences, exportable meshes, and object representations.