pith. sign in

arxiv: 2311.07885 · v1 · pith:PNXQJWINnew · submitted 2023-11-14 · 💻 cs.CV · cs.AI· cs.GR

One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

Minghua Liu , Ruoxi Shi , Linghao Chen , Zhuoyang Zhang , Chao Xu , Xinyue Wei , Hansheng Chen , Chong Zeng
show 2 more authors
Jiayuan Gu Hao Su
This is my paper
classification 💻 cs.CV cs.AIcs.GR
keywords diffusiongenerationimagemodelsmulti-viewconsistentextensiveimages
0
0 comments X
read the original abstract

Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper, we present One-2-3-45++, an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially finetuning a 2D diffusion model for consistent multi-view image generation, followed by elevating these images to 3D with the aid of multi-view conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality, diverse 3D assets that closely mirror the original input image. Our project webpage: https://sudo-ai-3d.github.io/One2345plus_page.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

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

  1. CORGI: Consistency-Aware 3D Dog Reconstruction from a Single Image in the Wild

    cs.CV 2026-07 unverdicted novelty 7.0

    A new pipeline using canonical LoRAs for view synthesis, deformable 3D Gaussian splatting anchored on D-SMAL, and generative repair to produce animatable 3D dogs from single wild images without 3D supervision.

  2. CAT3D: Create Anything in 3D with Multi-View Diffusion Models

    cs.CV 2024-05 conditional novelty 6.0

    A multi-view diffusion model generates consistent novel views from sparse images to enable fast 3D scene reconstruction.

  3. Evaluating Real-World Robot Manipulation Policies in Simulation

    cs.RO 2024-05 conditional novelty 6.0

    SIMPLER simulated environments yield policy performance that correlates strongly with real-world robot manipulation results and captures similar sensitivity to distribution shifts.

  4. InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

    cs.CV 2024-04 unverdicted novelty 6.0

    InstantMesh produces diverse, high-quality 3D meshes from single images in seconds by combining a multi-view diffusion model with a sparse-view large reconstruction model and optimizing directly on meshes.

  5. InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving

    cs.CV 2026-06 unverdicted novelty 5.0

    InfiniVerse reconstructs 3D occupancy from one frame, extends scenes autoregressively, converts to video via diffusion, and uses re-projection feedback to achieve SOTA FID 6.4 and FVD 67.97 on Waymo and nuScenes.