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HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions

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arxiv 2407.15187 v1 pith:PFHN75NY submitted 2024-07-21 cs.CV cs.GR

Haiyang Zhou , Xinhua Cheng , Wangbo Yu , Yonghong Tian , Li Yuan This is my paper · ORCID
classification cs.CVcs.GR
keywords generationscenediffusionmodelspanoramascenestextcreation
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3D scene generation is in high demand across various domains, including virtual reality, gaming, and the film industry. Owing to the powerful generative capabilities of text-to-image diffusion models that provide reliable priors, the creation of 3D scenes using only text prompts has become viable, thereby significantly advancing researches in text-driven 3D scene generation. In order to obtain multiple-view supervision from 2D diffusion models, prevailing methods typically employ the diffusion model to generate an initial local image, followed by iteratively outpainting the local image using diffusion models to gradually generate scenes. Nevertheless, these outpainting-based approaches prone to produce global inconsistent scene generation results without high degree of completeness, restricting their broader applications. To tackle these problems, we introduce HoloDreamer, a framework that first generates high-definition panorama as a holistic initialization of the full 3D scene, then leverage 3D Gaussian Splatting (3D-GS) to quickly reconstruct the 3D scene, thereby facilitating the creation of view-consistent and fully enclosed 3D scenes. Specifically, we propose Stylized Equirectangular Panorama Generation, a pipeline that combines multiple diffusion models to enable stylized and detailed equirectangular panorama generation from complex text prompts. Subsequently, Enhanced Two-Stage Panorama Reconstruction is introduced, conducting a two-stage optimization of 3D-GS to inpaint the missing region and enhance the integrity of the scene. Comprehensive experiments demonstrated that our method outperforms prior works in terms of overall visual consistency and harmony as well as reconstruction quality and rendering robustness when generating fully enclosed scenes.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. What if? Emulative Simulation with World Models for Situated Reasoning

    cs.CV 2026-03 conditional novelty 6.5 of 10

    WanderDream supplies 15.8K panoramic mental-exploration videos and 158K QA pairs showing that world-model imagination measurably improves situated spatial reasoning without active exploration.

  2. UniWorld-View: Large-Baseline View Synthesis via Video Diffusion Models

    cs.CV 2026-08 conditional novelty 6.0 of 10

    UniWorld-View couples an occlusion-aware point cloud renderer with a dual-stream video diffusion model to synthesize large-baseline novel views from monocular video.

  3. CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-Centric 3D Scene Generation

    cs.GR 2026-07 conditional novelty 6.0 of 10

    CGGS generates viewpoint-consistent, text-aligned ego-centric 3D scenes via consistency-augmented multi-view diffusion, flow-guided layout initialization, and mutual-information depth-refined Gaussian optimization.

  4. ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment

    cs.CV 2025-07 conditional novelty 6.0 of 10

    ScenePainter introduces a SceneConceptGraph that encodes multi-level scene concepts and relations, and aligns an outpainting model with them to reduce semantic drift in perpetual 3D scene generation.

  5. HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation

    cs.CV 2025-04 conditional novelty 6.0 of 10

    A framework that generates panoramic videos from one image and reconstructs them into 4D Gaussian scenes, with a new panoramic video dataset and improved depth alignment.

  6. RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A two-stage, zero-shot diffusion pipeline that textures room-scale meshes with global style consistency and per-instance repainting.

  7. WorldClaw: Agentic 3D Open-World Generation at Scale

    cs.AI 2026-08 conditional novelty 4.0 of 10

    WorldClaw generates globally coherent, locally detailed, editable 3D worlds from open-ended text using a coarse-to-fine agentic pipeline.

  8. 3D Scene Generation: A Survey

    cs.CV 2025-05 conditional

    The paper surveys 3D scene generation and organizes methods into four paradigms, with datasets, evaluation metrics, applications, and future directions.

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