HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels
- URL: http://arxiv.org/abs/2507.21809v2
- Date: Wed, 13 Aug 2025 09:21:11 GMT
- Title: HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels
- Authors: 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,
- Abstract summary: HunyuanWorld 1.0 is a novel framework that combines the best of both worlds for generating immersive, explorable, and interactive 3D scenes from text and image conditions.<n>Our approach features three key advantages: 1) 360deg 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.
- Score: 30.986527559921335
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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{\deg} 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{\deg} 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.
Related papers
- Beyond Pixel Histories: World Models with Persistent 3D State [50.4601060508243]
PERSIST is a new paradigm of world model which simulates the evolution of a latent 3D scene.<n>We show substantial improvements in spatial memory, 3D consistency, and long-horizon stability over existing methods.
arXiv Detail & Related papers (2026-03-03T19:58:31Z) - WorldGen: From Text to Traversable and Interactive 3D Worlds [87.95088818329403]
We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts.<n>Our approach transforms natural language descriptions into fully textured environments that can be immediately explored or edited within standard game engines.<n>This work represents a step towards accessible, generative world-building at scale, advancing the frontier of 3D generative AI for applications in gaming, simulation, and immersive social environments.
arXiv Detail & Related papers (2025-11-20T22:13:18Z) - OmniX: From Unified Panoramic Generation and Perception to Graphics-Ready 3D Scenes [57.790894531046796]
Panorama-based 2D lifting has emerged as a promising technique to produce immersive, realistic, and diverse 3D environments.<n>In this work, we advance this technique to generate graphics-ready 3D scenes suitable for physically based rendering (PBR), relighting, and simulation.<n>Our key insight is to repurpose 2D generative models for panoramic perception of geometry, textures, and PBR materials.<n>Based on a lightweight and efficient cross-modal adapter structure, OmniX reuses 2D generative priors for a broad range of panoramic vision tasks.
arXiv Detail & Related papers (2025-10-30T17:59:51Z) - WorldGrow: Generating Infinite 3D World [75.81531067447203]
We tackle the challenge of generating the infinitely extendable 3D world -- large, continuous environments with coherent geometry and realistic appearance.<n>We propose WorldGrow, a hierarchical framework for unbounded 3D scene synthesis.<n>Our method features three core components: (1) a data curation pipeline that extracts high-quality scene blocks for training, making the 3D structured latent representations suitable for scene generation; (2) a 3D block inpainting mechanism that enables context-aware scene extension; and (3) a coarse-to-fine generation strategy that ensures both global layout plausibility and local geometric/textural fidelity.
arXiv Detail & Related papers (2025-10-24T17:39:52Z) - Terra: Explorable Native 3D World Model with Point Latents [74.90179419859415]
We present Terra, a native 3D world model that represents and generates explorable environments in an intrinsic 3D latent space.<n>Specifically, we propose a novel point-to-Gaussian variational autoencoder (P2G-VAE) that encodes 3D inputs into a latent point representation.<n>We then introduce a sparse point flow matching network (SPFlow) for generating the latent point representation, which simultaneously denoises the positions and features of the point latents.
arXiv Detail & Related papers (2025-10-16T17:59:56Z) - NeoWorld: Neural Simulation of Explorable Virtual Worlds via Progressive 3D Unfolding [46.79724166827757]
We introduce NeoWorld, a framework for generating interactive 3D virtual worlds from a single input image.<n>Inspired by the on-demand worldbuilding concept in the science fiction novel Simulacron-3 (1964), our system constructs expansive environments.
arXiv Detail & Related papers (2025-09-29T08:24:28Z) - Matrix-3D: Omnidirectional Explorable 3D World Generation [20.568791715708134]
We propose Matrix-3D, a framework that utilize panoramic representation for wide-coverage omnidirectional 3D world generation.<n>We first train a trajectory-guided panoramic video diffusion model that employs scene mesh renders as condition.<n>To lift the panorama scene video to 3D world, we propose two separate methods: (1) a feed-forward large panorama reconstruction model for rapid 3D scene reconstruction and (2) an optimization-based pipeline for accurate and detailed 3D scene reconstruction.
arXiv Detail & Related papers (2025-08-11T15:29:57Z) - WonderFree: Enhancing Novel View Quality and Cross-View Consistency for 3D Scene Exploration [28.97217489759405]
Key challenge in current 3D generation methods is the limited explorability.<n>We propose WonderFree, the first model that enables users to interactively generate 3D worlds with the freedom to explore from arbitrary angles and directions.
arXiv Detail & Related papers (2025-06-25T16:28:40Z) - Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation [66.95956271144982]
We present Voyager, a novel video diffusion framework that generates world-consistent 3D point-cloud sequences from a single image.<n>Unlike existing approaches, Voyager achieves end-to-end scene generation and reconstruction with inherent consistency across frames.
arXiv Detail & Related papers (2025-06-04T17:59:04Z) - WorldExplorer: Towards Generating Fully Navigable 3D Scenes [49.21733308718443]
WorldExplorer builds fully navigable 3D scenes with consistent visual quality across a wide range of viewpoints.<n>We generate multiple videos along short, pre-defined trajectories, that explore the scene in depth.<n>Our novel scene memory conditions each video on the most relevant prior views, while a collision-detection mechanism prevents degenerate results.
arXiv Detail & Related papers (2025-06-02T15:41:31Z) - Constructing a 3D Town from a Single Image [23.231661811526955]
3DTown is a training-free framework designed to synthesize realistic and coherent 3D scenes from a single top-down view.<n>We decompose the input image into overlapping regions and generate each using a pretrained 3D object generator.<n>Our results demonstrate that high-quality 3D town generation is achievable from a single image using a principled, training-free approach.
arXiv Detail & Related papers (2025-05-21T17:10:47Z) - SynCity: Training-Free Generation of 3D Worlds [107.69875149880679]
We propose SynCity, a training- and optimization-free approach to generating 3D worlds from textual descriptions.<n>We show how 3D and 2D generators can be combined to generate ever-expanding scenes.
arXiv Detail & Related papers (2025-03-20T17:59:40Z) - GenEx: Generating an Explorable World [59.0666303068111]
We introduce GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination.<n>GenEx generates an entire 3D-consistent imaginative environment from as little as a single RGB image.<n> GPT-assisted agents are equipped to perform complex embodied tasks, including both goal-agnostic exploration and goal-driven navigation.
arXiv Detail & Related papers (2024-12-12T18:59:57Z) - WonderWorld: Interactive 3D Scene Generation from a Single Image [38.83667648993784]
We present WonderWorld, a novel framework for interactive 3D scene generation.<n>WonderWorld generates connected and diverse 3D scenes in less than 10 seconds on a single A6000 GPU.
arXiv Detail & Related papers (2024-06-13T17:59:10Z) - 3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation [51.64796781728106]
We propose a generative refinement network to synthesize new contents with higher quality by exploiting the natural image prior to 2D diffusion model and the global 3D information of the current scene.
Our approach supports wide variety of scene generation and arbitrary camera trajectories with improved visual quality and 3D consistency.
arXiv Detail & Related papers (2024-03-14T14:31:22Z) - Unrolling Virtual Worlds for Immersive Experiences [13.615681132633561]
This research pioneers a method for generating immersive worlds, drawing inspiration from elements of vintage adventure games like Myst.
We explore the intricate conversion of 2D panoramas into 3D scenes using equirectangular projections.
arXiv Detail & Related papers (2023-11-14T13:16:34Z) - Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training [51.632418297156605]
We introduce MixCon3D, a method aiming to sculpt holistic 3D representation in contrastive language-image-3D pre-training.
We develop the 3D object-level representation from complementary perspectives, e.g., multi-view rendered images with the point cloud.
Then, MixCon3D performs language-3D contrastive learning, comprehensively depicting real-world 3D objects and bolstering text alignment.
arXiv Detail & Related papers (2023-11-03T06:05:36Z) - Fantasia3D: Disentangling Geometry and Appearance for High-quality
Text-to-3D Content Creation [45.69270771487455]
We propose a new method of Fantasia3D for high-quality text-to-3D content creation.
Key to Fantasia3D is the disentangled modeling and learning of geometry and appearance.
Our framework is more compatible with popular graphics engines, supporting relighting, editing, and physical simulation of the generated 3D assets.
arXiv Detail & Related papers (2023-03-24T09:30:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.