HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions

• URL: http://arxiv.org/abs/2407.15187v1
• Date: Sun, 21 Jul 2024 14:52:51 GMT
• Title: HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions
• Authors: Haiyang Zhou, Xinhua Cheng, Wangbo Yu, Yonghong Tian, Li Yuan,
• Abstract summary: 3D scene generation is in high demand across various domains, including virtual reality, gaming, and the film industry. We introduce HoloDreamer, a framework that first generates high-definition panorama as a holistic initialization of the full 3D scene. We 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.
• Score: 31.342899807980654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models [112.2625368640425]
  High-resolution Image-to-3D model (Hi3D) is a new video diffusion based paradigm that redefines a single image to multi-view images as 3D-aware sequential image generation. Hi3D first empowers the pre-trained video diffusion model with 3D-aware prior, yielding multi-view images with low-resolution texture details.
  arXiv  Detail & Related papers  (2024-09-11T17:58:57Z)
• ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model [16.14713604672497]
  ReconX is a novel 3D scene reconstruction paradigm that reframes the ambiguous reconstruction challenge as a temporal generation task. The proposed ReconX first constructs a global point cloud and encodes it into a contextual space as the 3D structure condition. Guided by the condition, the video diffusion model then synthesizes video frames that are both detail-preserved and exhibit a high degree of 3D consistency.
  arXiv  Detail & Related papers  (2024-08-29T17:59:40Z)
• SceneDreamer360: Text-Driven 3D-Consistent Scene Generation with Panoramic Gaussian Splatting [53.32467009064287]
  We propose a text-driven 3D-consistent scene generation model: SceneDreamer360. Our proposed method leverages a text-driven panoramic image generation model as a prior for 3D scene generation. Our experiments demonstrate that SceneDreamer360 with its panoramic image generation and 3DGS can produce higher quality, spatially consistent, and visually appealing 3D scenes from any text prompt.
  arXiv  Detail & Related papers  (2024-08-25T02:56:26Z)
• 3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation [51.64796781728106]
  We propose a generative refinement network to synthesize new contents with higher quality by exploiting the natural image prior to 2D diffusion model and the global 3D information of the current scene. Our approach supports wide variety of scene generation and arbitrary camera trajectories with improved visual quality and 3D consistency.
  arXiv  Detail & Related papers  (2024-03-14T14:31:22Z)
• Denoising Diffusion via Image-Based Rendering [54.20828696348574]
  We introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images.
  arXiv  Detail & Related papers  (2024-02-05T19:00:45Z)
• SceneWiz3D: Towards Text-guided 3D Scene Composition [134.71933134180782]
  Existing approaches either leverage large text-to-image models to optimize a 3D representation or train 3D generators on object-centric datasets. We introduce SceneWiz3D, a novel approach to synthesize high-fidelity 3D scenes from text.
  arXiv  Detail & Related papers  (2023-12-13T18:59:30Z)
• Guide3D: Create 3D Avatars from Text and Image Guidance [55.71306021041785]
  Guide3D is a text-and-image-guided generative model for 3D avatar generation based on diffusion models. Our framework produces topologically and structurally correct geometry and high-resolution textures.
  arXiv  Detail & Related papers  (2023-08-18T17:55:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.