Pith. sign in

REVIEW 5 cited by

VistaDream: Sampling multiview consistent images for single-view scene reconstruction

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.16892 v1 pith:ICA3KKA2 submitted 2024-10-22 cs.CV

Haiping Wang , Yuan Liu , Ziwei Liu , Wenping Wang , Zhen Dong , Bisheng Yang This is my paper
classification cs.CV
keywords imagesconsistencyvistadreamsingle-viewdiffusiongeneratedimagemodels
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this paper, we propose VistaDream a novel framework to reconstruct a 3D scene from a single-view image. Recent diffusion models enable generating high-quality novel-view images from a single-view input image. Most existing methods only concentrate on building the consistency between the input image and the generated images while losing the consistency between the generated images. VistaDream addresses this problem by a two-stage pipeline. In the first stage, VistaDream begins with building a global coarse 3D scaffold by zooming out a little step with inpainted boundaries and an estimated depth map. Then, on this global scaffold, we use iterative diffusion-based RGB-D inpainting to generate novel-view images to inpaint the holes of the scaffold. In the second stage, we further enhance the consistency between the generated novel-view images by a novel training-free Multiview Consistency Sampling (MCS) that introduces multi-view consistency constraints in the reverse sampling process of diffusion models. Experimental results demonstrate that without training or fine-tuning existing diffusion models, VistaDream achieves consistent and high-quality novel view synthesis using just single-view images and outperforms baseline methods by a large margin. The code, videos, and interactive demos are available at https://vistadream-project-page.github.io/.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-Centric 3D Scene Generation

    cs.GR 2026-07 conditional novelty 6.0 of 10

    CGGS generates viewpoint-consistent, text-aligned ego-centric 3D scenes via consistency-augmented multi-view diffusion, flow-guided layout initialization, and mutual-information depth-refined Gaussian optimization.

  2. Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Single-image novel view synthesis is decomposed into panorama outpainting plus keyframe-conditioned video diffusion, producing loop-consistent scene tours.

  3. WAVE: Warp-Based View Guidance for Consistent Novel View Synthesis Using a Single Image

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A training-free method, WAVE, improves multi-view consistency in single-image novel view synthesis by using 3D-warped views to guide diffusion attention and initial noise.

  4. Non-invasive Assessment of Pancreatic Duct Hypertension Using Computational Flow Modeling

    physics.med-ph 2025-08 unverdicted novelty 5.0 of 10

    A computational model estimates pancreatic duct pressure non-invasively from MRCP geometry, with reported agreement against ERCP pressure measurements.

  5. Impact-driven Context Filtering For Cross-file Code Completion

    cs.SE 2025-08 unverdicted novelty 5.0 of 10

    The manuscript's abstract claims a new code-completion filtering method, yet the body contains an unrelated 3D animation paper, leaving the claimed work unverifiable.

Pith tools