title: Voyager: Long-Range and World-Consistent Video Diffusion for Explorable
    3D Scene Generation
summary: Real-world applications like video gaming and virtual reality often demand
  the ability to model 3D scenes that users can explore along custom camera
  trajectories. While significant progress has been made in generating 3D objects
  from text or images, creating long-range, 3D-consistent, explorable 3D scenes
  remains a complex and challenging problem. In this work, we present Voyager, a
  novel video diffusion framework that generates world-consistent 3D point-cloud
  sequences from a single image with user-defined camera path. Unlike existing
  approaches, Voyager achieves end-to-end scene generation and reconstruction
  with inherent consistency across frames, eliminating the need for 3D
  reconstruction pipelines (e.g., structure-from-motion or multi-view stereo).
  Our method integrates three key components: 1) World-Consistent Video
  Diffusion: A unified architecture that jointly generates aligned RGB and depth
  video sequences, conditioned on existing world observation to ensure global
  coherence 2) Long-Range World Exploration: An efficient world cache with point
  culling and an auto-regressive inference with smooth video sampling for
  iterative scene extension with context-aware consistency, and 3) Scalable Data
  Engine: A video reconstruction pipeline that automates camera pose estimation
  and metric depth prediction for arbitrary videos, enabling large-scale, diverse
  training data curation without manual 3D annotations. Collectively, these
  designs result in a clear improvement over existing methods in visual quality
  and geometric accuracy, with versatile applications.
paper_authors._id: 68413366adeec0116d071af2
paper_authors.name: Tianyu Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:26:41.198Z
paper_authors._id: 63425d394c9a81858b36aeb5
paper_authors.avatarUrl: /avatars/511ad6a75bd1c10fc510ef527e7f8e5b.svg
paper_authors.fullname: Tianyu Huang
paper_authors.type: user
paper_authors.user: tyhuang
paper_authors._id: 68413366adeec0116d071af3
paper_authors.name: Wangguandong Zheng
paper_authors._id: 68413366adeec0116d071af4
paper_authors.name: Tengfei Wang
paper_authors._id: 68413366adeec0116d071af5
paper_authors.name: Yuhao Liu
paper_authors._id: 68413366adeec0116d071af6
paper_authors.name: Zhenwei Wang
paper_authors._id: 68413366adeec0116d071af7
paper_authors.name: Junta Wu
paper_authors._id: 68413366adeec0116d071af8
paper_authors.name: Jie Jiang
paper_authors._id: 68413366adeec0116d071af9
paper_authors.name: Hui Li
paper_authors._id: 68413366adeec0116d071afa
paper_authors.name: Rynson W. H. Lau
paper_authors._id: 68413366adeec0116d071afb
paper_authors.name: Wangmeng Zuo
paper_authors._id: 68413366adeec0116d071afc
paper_authors.name: Chunchao Guo
paper_title: Voyager: Long-Range and World-Consistent Video Diffusion for Explorable
    3D Scene Generation
paper_summary: Real-world applications like video gaming and virtual reality often demand
  the ability to model 3D scenes that users can explore along custom camera
  trajectories. While significant progress has been made in generating 3D objects
  from text or images, creating long-range, 3D-consistent, explorable 3D scenes
  remains a complex and challenging problem. In this work, we present Voyager, a
  novel video diffusion framework that generates world-consistent 3D point-cloud
  sequences from a single image with user-defined camera path. Unlike existing
  approaches, Voyager achieves end-to-end scene generation and reconstruction
  with inherent consistency across frames, eliminating the need for 3D
  reconstruction pipelines (e.g., structure-from-motion or multi-view stereo).
  Our method integrates three key components: 1) World-Consistent Video
  Diffusion: A unified architecture that jointly generates aligned RGB and depth
  video sequences, conditioned on existing world observation to ensure global
  coherence 2) Long-Range World Exploration: An efficient world cache with point
  culling and an auto-regressive inference with smooth video sampling for
  iterative scene extension with context-aware consistency, and 3) Scalable Data
  Engine: A video reconstruction pipeline that automates camera pose estimation
  and metric depth prediction for arbitrary videos, enabling large-scale, diverse
  training data curation without manual 3D annotations. Collectively, these
  designs result in a clear improvement over existing methods in visual quality
  and geometric accuracy, with versatile applications.
paper_ai_summary: Voyager is a video diffusion framework that generates world-consistent 3D point-cloud sequences from a single image, enabling long-range, consistent 3D scene exploration with user-defined camera paths.
paper_ai_keywords: video diffusion world-consistent video diffusion 3D point-cloud sequences camera path end-to-end scene generation consistent frames unified architecture RGB and depth video sequences world observation global coherence long-range world exploration world cache point culling auto-regressive inference smooth video sampling scene extension context-aware consistency scalable data engine camera pose estimation metric depth prediction large-scale diverse training data