MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation

• URL: http://arxiv.org/abs/2404.03656v1
• Date: Thu, 4 Apr 2024 17:59:57 GMT
• Title: MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation
• Authors: Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, Shubham Tulsiani,
• Abstract summary: We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. We show that our approach can yield more accurate synthesis compared to recent state-of-the-art, including distillation-based 3D inference and prior multi-view generation methods.
• Score: 54.27399121779011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. While recent methods pursuing 3D inference advocate learning novel-view generative models, these generations are not 3D-consistent and require a distillation process to generate a 3D output. We instead cast the task of 3D inference as directly generating mutually-consistent multiple views and build on the insight that additionally inferring depth can provide a mechanism for enforcing this consistency. Specifically, we train a denoising diffusion model to generate multi-view RGB-D images given a single RGB input image and leverage the (intermediate noisy) depth estimates to obtain reprojection-based conditioning to maintain multi-view consistency. We train our model using large-scale synthetic dataset Obajverse as well as the real-world CO3D dataset comprising of generic camera viewpoints. We demonstrate that our approach can yield more accurate synthesis compared to recent state-of-the-art, including distillation-based 3D inference and prior multi-view generation methods. We also evaluate the geometry induced by our multi-view depth prediction and find that it yields a more accurate representation than other direct 3D inference approaches.

Related papers

• Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors [17.544733016978928]
  3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Recent advancements in 3D object generation have introduced techniques that reconstruct an object's 3D shape and texture. We propose bridging the gap between 2D and 3D diffusion models to address this limitation.
  arXiv  Detail & Related papers  (2024-10-12T10:14:11Z)
• 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)
• Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion [43.07285784556328]
  Existing single image-to-3D creation methods typically involve a two-stage process. We introduce a unified 3D generation framework, named Ouroboros3D, which integrates multi-view image generation and 3D reconstruction.
  arXiv  Detail & Related papers  (2024-06-05T12:15:22Z)
• 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)
• Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View Diffusion Model [65.58911408026748]
  We propose Grounded-Dreamer to generate 3D assets that can accurately follow complex, compositional text prompts. We first advocate leveraging text-guided 4-view images as the bottleneck in the text-to-3D pipeline. We then introduce an attention refocusing mechanism to encourage text-aligned 4-view image generation.
  arXiv  Detail & Related papers  (2024-04-28T04:05:10Z)
• Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior [57.986512832738704]
  We present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model.
  arXiv  Detail & Related papers  (2024-03-14T07:39:59Z)
• MVHuman: Tailoring 2D Diffusion with Multi-view Sampling For Realistic 3D Human Generation [45.88714821939144]
  We present an alternative scheme named MVHuman to generate human radiance fields from text guidance. Our core is a multi-view sampling strategy to tailor the denoising processes of the pre-trained network for generating consistent multi-view images.
  arXiv  Detail & Related papers  (2023-12-15T11:56:26Z)
• HoloFusion: Towards Photo-realistic 3D Generative Modeling [77.03830223281787]
  Diffusion-based image generators can now produce high-quality and diverse samples, but their success has yet to fully translate to 3D generation. We present HoloFusion, a method that combines the best of these approaches to produce high-fidelity, plausible, and diverse 3D samples.
  arXiv  Detail & Related papers  (2023-08-28T01:19:33Z)
• Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views [47.215089338101066]
  We present Sparse3D, a novel 3D reconstruction method tailored for sparse view inputs. Our approach distills robust priors from a multiview-consistent diffusion model to refine a neural radiance field. By tapping into 2D priors from powerful image diffusion models, our integrated model consistently delivers high-quality results.
  arXiv  Detail & Related papers  (2023-08-27T11:52:00Z)
• Novel View Synthesis with Diffusion Models [56.55571338854636]
  We present 3DiM, a diffusion model for 3D novel view synthesis. It is able to translate a single input view into consistent and sharp completions across many views. 3DiM can generate multiple views that are 3D consistent using a novel technique called conditioning.
  arXiv  Detail & Related papers  (2022-10-06T16:59:56Z)

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.