MVRoom: Controllable 3D Indoor Scene Generation with Multi-View Diffusion Models

• URL: http://arxiv.org/abs/2512.04248v1
• Date: Wed, 03 Dec 2025 20:33:18 GMT
• Title: MVRoom: Controllable 3D Indoor Scene Generation with Multi-View Diffusion Models
• Authors: Shaoheng Fang, Chaohui Yu, Fan Wang, Qixing Huang,
• Abstract summary: We introduce MVRoom, a controllable novel view synthesis (NVS) pipeline for 3D indoor scenes.<n>MVRoom employs a two-stage design in which the 3D layout is used throughout to enforce multi-view consistency.
• Score: 40.57348732287596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce MVRoom, a controllable novel view synthesis (NVS) pipeline for 3D indoor scenes that uses multi-view diffusion conditioned on a coarse 3D layout. MVRoom employs a two-stage design in which the 3D layout is used throughout to enforce multi-view consistency. The first stage employs novel representations to effectively bridge the 3D layout and consistent image-based condition signals for multi-view generation. The second stage performs image-conditioned multi-view generation, incorporating a layout-aware epipolar attention mechanism to enhance multi-view consistency during the diffusion process. Additionally, we introduce an iterative framework that generates 3D scenes with varying numbers of objects and scene complexities by recursively performing multi-view generation (MVRoom), supporting text-to-scene generation. Experimental results demonstrate that our approach achieves high-fidelity and controllable 3D scene generation for NVS, outperforming state-of-the-art baseline methods both quantitatively and qualitatively. Ablation studies further validate the effectiveness of key components within our generation pipeline.

Related papers

• FlashWorld: High-quality 3D Scene Generation within Seconds [44.24921660160879]
  FlashWorld is a generative model that produces 3D scenes from a single image or text prompt in seconds.<n>Our approach shifts from the conventional multi-view-oriented (MV-oriented) paradigm to a 3D-oriented approach.
  arXiv  Detail & Related papers  (2025-10-15T15:35:48Z)
• 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.<n>However, 2D diffusion models often struggle to produce dense images with strong multi-view consistency.<n>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)
• 3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement [66.8116563135326]
  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.
  arXiv  Detail & Related papers  (2024-12-24T17:36:34Z)
• 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.<n>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.<n>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)
• 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)
• 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)
• 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)
• Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models [16.326276673056334]
  Consistent-1-to-3 is a generative framework that significantly mitigates this issue. We decompose the NVS task into two stages: (i) transforming observed regions to a novel view, and (ii) hallucinating unseen regions. We propose to employ epipolor-guided attention to incorporate geometry constraints, and multi-view attention to better aggregate multi-view information.
  arXiv  Detail & Related papers  (2023-10-04T17:58:57Z)

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.