Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation (2506.04225v1)

Abstract: 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.

• The paper presents a novel video diffusion framework, Voyager, that generates long-range, explorable 3D scenes from a single image.
• It achieves world-consistent outputs by aligning RGB and depth sequences with auto-regressive inference, point culling, and efficient world caching.
• Empirical results show significant improvements over prior methods in PSNR, SSIM, and LPIPS, enhancing both visual quality and geometric accuracy.

Analyzing "Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation"

The paper entitled "Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation" presents an advanced framework for generating long-range, explorable 3D scenes using video diffusion models. It addresses a significant challenge in the domain of virtual environment creation, particularly in scenarios requiring seamless navigation across 3D spaces such as gaming and virtual reality.

The authors propose a novel method, "Voyager," which significantly departs from traditional 3D reconstruction pipelines like structure-from-motion. Instead, it leverages a video diffusion framework that uses a single image to generate world-consistent 3D point-cloud sequences as users define custom camera paths. This approach emphasizes inherent frame consistency and bypasses the need for post-hoc 3D reconstructions.

Key Components of the Voyager Framework

1. World-Consistent Video Diffusion: This component introduces a unified architecture capable of generating aligned RGB and depth video sequences. It ensures global coherence by conditioning the generation process on existing world observations.
2. Long-Range World Exploration: Voyager includes an efficient world cache accompanied by point culling and auto-regressive inference. This design facilitates the iterative extension of scenes with context-aware consistency, optimizing coherence over larger trajectories.
3. Scalable Data Engine: By automating camera pose estimation and metric depth prediction for arbitrary videos, this engine curates large-scale and diverse training data essential for robust model training.

The combination of these elements demonstrates a marked improvement in visual quality and geometric accuracy over prior methods. Importantly, this approach supports directly generating 3D-consistent videos and scenes without the common pitfalls of long-range spatial inconsistency and visual hallucination.

Implications and Applications

The research provides substantial implications for practical applications in fields involving video gaming, film production, and robotic simulations. The capability to generate explorable 3D worlds from minimal inputs could streamline content creation pipelines, reducing manual labor and increasing the scalability of virtual world development.

Theoretical implications include the potential shifts in how 3D environments are conceived and deployed, particularly regarding the integration of video diffusion models and novel view synthesis approaches. By effectively handling long-range spatial dynamics and ensuring temporal coherence, techniques like those proposed in Voyager could become foundational in the generation of immersive virtual experiences.

Comparison with Existing Methods

Voyager's efficiency of maintaining point clouds from different perspectives contributes to its robustness in scene generation. It is contrasted with recent literature on novel view synthesis (NVS) and video generation, highlighting challenges such as long-range spatial inconsistency and the visual artifacts from traditional methods reliant on partial view guidance.

The model's efficacy is illustrated through quantitative evaluations and qualitative assessments, surpassing existing baselines such as SEVA, ViewCrafter, See3D, and FlexWorld regarding metrics like PSNR, SSIM, and LPIPS scores.

Future Directions

Looking forward, further refinement of the world-consistent video diffusion models could focus on enhancing the scalability of these systems for broader real-world applications. Future research might explore integrations with real-time rendering systems or the development of more generalized models that can handle diverse environmental contexts without dedicated retraining.

Moreover, speculating on advancements in AI, integration with reinforcement learning could enable intelligent scene adaptation, utilizing the model's capabilities to autonomously refine scenes based on specific objectives like aesthetic value or efficiency in navigable space.

In conclusion, the Voyage framework represents a significant stride toward seamless, AI-driven 3D scene generation, optimizing both practical usability and theoretical coherence within the complex domain of virtual environment modeling.