HunyuanWorld-Voyager - Tencent's extended virtual world model

What is HunyuanWorld-Voyager?

HunyuanWorld-Voyager(abbreviated as Hunyuan)Voyager)It is Tencent's first ultra-long roaming world model supporting native 3D reconstruction. It is a novel video diffusion framework capable of generating 3D point cloud sequences with user-defined camera paths from a single image. It supports the generation of consistent 3D scene videos for world exploration along custom camera trajectories, generating aligned depth and RGB videos for efficient and direct 3D reconstruction. The model comprises two key components: consistent world video diffusion and long-distance world exploration, achieving iterative scene expansion through efficient point culling and autoregressive inference. A scalable data engine is proposed for generating scalable data for RGB-D video training. In the WorldScore benchmark, Voyager achieved excellent results across multiple metrics, demonstrating its powerful performance.

Main functions of HunyuanWorld-Voyager

• Generate a 3D point cloud sequence from a single image.It can generate a consistent 3D point cloud sequence from a single image based on a user-defined camera path, supporting long-distance world exploration.
• Generate consistent 3D scene videosIt can generate consistent 3D scene videos along user-defined camera paths, providing users with an immersive 3D scene roaming experience.
• Supports real-time 3D reconstructionThe generated RGB and depth videos can be directly used for efficient 3D reconstruction without the need for additional reconstruction tools, enabling rapid conversion from video to 3D models.
• Supports multiple application scenariosIt is applicable to various 3D understanding and generation tasks such as video reconstruction, image-to-3D generation, and video depth estimation, and has broad application prospects.
• Powerful performanceIn the WorldScore benchmark test released by Stanford University, HunyuanWorld-Voyager achieved excellent results on several key metrics, demonstrating its powerful capabilities in 3D scene generation and video diffusion.

The technical principles of HunyuanWorld-Voyager

• Worldwide video spreadThe model employs a unified architecture to jointly generate aligned RGB and depth video sequences, ensuring global consistency by conditionally applying existing world observations.
• Long-distance world explorationBy leveraging efficient point culling techniques and autoregressive inference, combined with smooth video sampling, iterative scene expansion is achieved while maintaining context-aware consistency.
• Scalable Data EngineThis paper proposes a video reconstruction pipeline that automates camera pose estimation and metric depth prediction, and can generate large-scale, diverse training data for any video without manual 3D annotation.
• Autoregressive Inference and World Caching MechanismThrough efficient point culling and autoregressive inference, combined with a world caching mechanism, it achieves iterative scene expansion, maintains geometric consistency, and supports arbitrary camera trajectories.
• High-efficiency 3D reconstructionThe generated RGB and depth videos can be directly used for efficient 3D reconstruction without the need for additional reconstruction tools, enabling rapid conversion from video to 3D models.

HunyuanWorld-Voyager project address

• Project official website: https://3d-models.hunyuan.tencent.com/world/
• Github repositoryhttps://github.com/Tencent-Hunyuan/HunyuanWorld-Voyager
• Hugging Face Model Libraryhttps://huggingface.co/tencent/HunyuanWorld-Voyager
• Technical Report: https://3d-models.hunyuan.tencent.com/voyager/voyager_en/assets/HYWorld_Voyager.pdf

Application Scenarios of HunyuanWorld-Voyager

• Video reconstructionIt enables efficient and direct 3D reconstruction by generating aligned RGB and depth videos, without the need for additional reconstruction tools.
• Image to 3D generationGenerates a consistent 3D point cloud sequence from a single image, supports the conversion from 2D images to 3D scenes, and can be used for the rapid construction of virtual scenes.
• Video depth estimationGenerates depth information aligned with RGB video, which can be used for video analysis and 3D understanding tasks.
• Virtual Reality (VR) and Augmented Reality (AR)The generated 3D scenes and videos can be used to create immersive VR experiences or augmented reality applications.
• Game developmentThe generated 3D scene assets can be seamlessly integrated into mainstream game engines, providing rich creative and content support for game development.
• 3D modeling and animationThe generated 3D point clouds and videos can be used as input for 3D modeling and animation production, improving creative efficiency.