Voyager: Long-Range and World-Consistent Video Diffusion for Explorable
  3D Scene Generation
Authors: name: Tianyu Huang, name: Wangguandong Zheng, name: Tengfei Wang, name: Yuhao Liu, name: Zhenwei Wang, name: Junta Wu, name: Jie Jiang, name: Hui Li, name: Rynson W. H. Lau, name: Wangmeng Zuo, name: Chunchao Guo

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.