3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement

• URL: http://arxiv.org/abs/2412.18565v2
• Date: Tue, 29 Apr 2025 02:51:28 GMT
• Title: 3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
• Authors: Yihang Luo, Shangchen Zhou, Yushi Lan, Xingang Pan, Chen Change Loy,
• Abstract summary: We present 3DEnhancer, which employs a multi-view latent diffusion model to enhance coarse 3D inputs while preserving multi-view consistency.<n>Unlike existing video-based approaches, our model supports seamless multi-view enhancement with improved coherence across diverse viewing angles.
• Score: 66.8116563135326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model generation are restricted to low resolutions with suboptimal multi-view consistency. In this study, we present a novel 3D enhancement pipeline, dubbed 3DEnhancer, which employs a multi-view latent diffusion model to enhance coarse 3D inputs while preserving multi-view consistency. Our method includes a pose-aware encoder and a diffusion-based denoiser to refine low-quality multi-view images, along with data augmentation and a multi-view attention module with epipolar aggregation to maintain consistent, high-quality 3D outputs across views. Unlike existing video-based approaches, our model supports seamless multi-view enhancement with improved coherence across diverse viewing angles. Extensive evaluations show that 3DEnhancer significantly outperforms existing methods, boosting both multi-view enhancement and per-instance 3D optimization tasks.

Related papers

• CDI3D: Cross-guided Dense-view Interpolation for 3D Reconstruction [25.468907201804093]
  Large Reconstruction Models (LRMs) have shown great promise in leveraging multi-view images generated by 2D diffusion models to extract 3D content. However, 2D diffusion models often struggle to produce dense images with strong multi-view consistency. We present CDI3D, a feed-forward framework designed for efficient, high-quality image-to-3D generation with view.
  arXiv  Detail & Related papers  (2025-03-11T03:08:43Z)
• 3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow [69.94527569577295]
  3D vision and spatial reasoning have long been recognized as preferable for accurately perceiving our three-dimensional world. Due to the difficulties in collecting high-quality 3D data, research in this area has only recently gained momentum. We propose converting existing densely activated LLMs into mixture-of-experts (MoE) models, which have proven effective for multi-modal data processing.
  arXiv  Detail & Related papers  (2025-01-28T04:31:19Z)
• Flex3D: Feed-Forward 3D Generation With Flexible Reconstruction Model And Input View Curation [61.040832373015014]
  We propose Flex3D, a novel framework for generating high-quality 3D content from text, single images, or sparse view images. We employ a fine-tuned multi-view image diffusion model and a video diffusion model to generate a pool of candidate views, enabling a rich representation of the target 3D object. In the second stage, the curated views are fed into a Flexible Reconstruction Model (FlexRM), built upon a transformer architecture that can effectively process an arbitrary number of inputs.
  arXiv  Detail & Related papers  (2024-10-01T17:29:43Z)
• PlacidDreamer: Advancing Harmony in Text-to-3D Generation [20.022078051436846]
  PlacidDreamer is a text-to-3D framework that harmonizes multi-view generation and text-conditioned generation. It employs a novel score distillation algorithm to achieve balanced saturation.
  arXiv  Detail & Related papers  (2024-07-19T02:00:04Z)
• Vivid-ZOO: Multi-View Video Generation with Diffusion Model [76.96449336578286]
  New challenges lie in the lack of massive captioned multi-view videos and the complexity of modeling such multi-dimensional distribution. We propose a novel diffusion-based pipeline that generates high-quality multi-view videos centered around a dynamic 3D object from text.
  arXiv  Detail & Related papers  (2024-06-12T21:44:04Z)
• Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data [80.92268916571712]
  A critical bottleneck is the scarcity of high-quality 3D objects with detailed captions. We propose Bootstrap3D, a novel framework that automatically generates an arbitrary quantity of multi-view images. We have generated 1 million high-quality synthetic multi-view images with dense descriptive captions.
  arXiv  Detail & Related papers  (2024-05-31T17:59:56Z)
• MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation [54.27399121779011]
  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.
  arXiv  Detail & Related papers  (2024-04-04T17:59:57Z)
• Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models [6.738732514502613]
  Diffusion$2$ is a novel framework for dynamic 3D content creation. It reconciles the knowledge about geometric consistency and temporal smoothness from 3D models to directly sample dense multi-view images. Experiments demonstrate the efficacy of our proposed framework in generating highly seamless and consistent 4D assets.
  arXiv  Detail & Related papers  (2024-04-02T17:58:03Z)
• Envision3D: One Image to 3D with Anchor Views Interpolation [18.31796952040799]
  We present Envision3D, a novel method for efficiently generating high-quality 3D content from a single image. It is capable of generating high-quality 3D content in terms of texture and geometry, surpassing previous image-to-3D baseline methods.
  arXiv  Detail & Related papers  (2024-03-13T18:46:33Z)
• MVDream: Multi-view Diffusion for 3D Generation [14.106283556521962]
  We introduce MVDream, a diffusion model that is able to generate consistent multi-view images from a given text prompt. Learning from both 2D and 3D data, a multi-view diffusion model can achieve the generalizability of 2D diffusion models and the consistency of 3D renderings.
  arXiv  Detail & Related papers  (2023-08-31T07:49:06Z)

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.