MVGamba: Unify 3D Content Generation as State Space Sequence Modeling

• URL: http://arxiv.org/abs/2406.06367v2
• Date: Thu, 20 Jun 2024 07:33:42 GMT
• Title: MVGamba: Unify 3D Content Generation as State Space Sequence Modeling
• Authors: Xuanyu Yi, Zike Wu, Qiuhong Shen, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, Shuicheng Yan, Xinchao Wang, Hanwang Zhang,
• Abstract summary: We introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor. With off-the-detail multi-view diffusion models integrated, MVGamba unifies 3D generation tasks from a single image, sparse images, or text prompts. Experiments demonstrate that MVGamba outperforms state-of-the-art baselines in all 3D content generation scenarios with approximately only $0.1times$ of the model size.
• Score: 150.80564081817786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent 3D large reconstruction models (LRMs) can generate high-quality 3D content in sub-seconds by integrating multi-view diffusion models with scalable multi-view reconstructors. Current works further leverage 3D Gaussian Splatting as 3D representation for improved visual quality and rendering efficiency. However, we observe that existing Gaussian reconstruction models often suffer from multi-view inconsistency and blurred textures. We attribute this to the compromise of multi-view information propagation in favor of adopting powerful yet computationally intensive architectures (e.g., Transformers). To address this issue, we introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor based on the RNN-like State Space Model (SSM). Our Gaussian reconstructor propagates causal context containing multi-view information for cross-view self-refinement while generating a long sequence of Gaussians for fine-detail modeling with linear complexity. With off-the-shelf multi-view diffusion models integrated, MVGamba unifies 3D generation tasks from a single image, sparse images, or text prompts. Extensive experiments demonstrate that MVGamba outperforms state-of-the-art baselines in all 3D content generation scenarios with approximately only $0.1\times$ of the model size.

Related papers

• NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model [57.92709692193132]
  NovelGS is a diffusion model for Gaussian Splatting given sparse-view images. We leverage the novel view denoising through a transformer-based network to generate 3D Gaussians.
  arXiv  Detail & Related papers  (2024-11-25T07:57:17Z)
• UniG: Modelling Unitary 3D Gaussians for View-consistent 3D Reconstruction [20.089890859122168]
  We present UniG, a view-consistent 3D reconstruction and novel view synthesis model. UniG generates a high-fidelity representation of 3D Gaussians from sparse images.
  arXiv  Detail & Related papers  (2024-10-17T03:48:02Z)
• Self-augmented Gaussian Splatting with Structure-aware Masks for Sparse-view 3D Reconstruction [9.953394373473621]
  Sparse-view 3D reconstruction is a formidable challenge in computer vision. We propose a self-augmented coarse-to-fine Gaussian splatting paradigm, enhanced with a structure-aware mask. Our method achieves state-of-the-art performances for sparse input views in both perceptual quality and efficiency.
  arXiv  Detail & Related papers  (2024-08-09T03:09:22Z)
• MVDiff: Scalable and Flexible Multi-View Diffusion for 3D Object Reconstruction from Single-View [0.0]
  This paper proposes a general framework to generate consistent multi-view images from single image or leveraging scene representation transformer and view-conditioned diffusion model. Our model is able to generate 3D meshes surpassing baselines methods in evaluation metrics, including PSNR, SSIM and LPIPS.
  arXiv  Detail & Related papers  (2024-05-06T22:55:53Z)
• InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models [66.83681825842135]
  InstantMesh is a feed-forward framework for instant 3D mesh generation from a single image. It features state-of-the-art generation quality and significant training scalability. We release all the code, weights, and demo of InstantMesh with the intention that it can make substantial contributions to the community of 3D generative AI.
  arXiv  Detail & Related papers  (2024-04-10T17:48:37Z)
• GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation [85.15374487533643]
  We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0.1s. GRM is a feed-forward transformer-based model that efficiently incorporates multi-view information. We also showcase the potential of GRM in generative tasks, i.e., text-to-3D and image-to-3D, by integrating it with existing multi-view diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:59:34Z)
• LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation [51.19871052619077]
  We introduce Large Multi-View Gaussian Model (LGM), a novel framework designed to generate high-resolution 3D models from text prompts or single-view images. We maintain the fast speed to generate 3D objects within 5 seconds while boosting the training resolution to 512, thereby achieving high-resolution 3D content generation.
  arXiv  Detail & Related papers  (2024-02-07T17:57:03Z)
• DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model [86.37536249046943]
  textbfDMV3D is a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model incorporates a triplane NeRF representation and can denoise noisy multi-view images via NeRF reconstruction and rendering.
  arXiv  Detail & Related papers  (2023-11-15T18:58:41Z)

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.