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MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction

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arxiv 2402.12712 v3 pith:RZUY4OJ5 submitted 2024-02-20 cs.CV

Shitao Tang , Jiacheng Chen , Dilin Wang , Chengzhou Tang , Fuyang Zhang , Yuchen Fan , Vikas Chandra , Yasutaka Furukawa
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Rakesh Ranjan
This is my paper · ORCID
classification cs.CV
keywords mvdiffusiondensehigh-resolutionobjectreconstructionviewviewsarchitecture
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This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses. MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time. We use the Objaverse for training and the Google Scanned Objects for evaluation with standard novel view synthesis and 3D reconstruction metrics, where MVDiffusion++ significantly outperforms the current state of the arts. We also demonstrate a text-to-3D application example by combining MVDiffusion++ with a text-to-image generative model. The project page is at https://mvdiffusion-plusplus.github.io.

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Forward citations

Cited by 10 Pith papers

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

  1. PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models

    cs.CV 2024-12 conditional novelty 7.0 of 10

    A multi-view diffusion pipeline that segments 3D objects into parts, completes occluded or invisible parts, and reconstructs them into a compositional 3D asset.

  2. MeshGen: Generating PBR Textured Mesh with Render-Enhanced Auto-Encoder and Generative Data Augmentation

    cs.GR 2025-05 conditional novelty 6.0 of 10

    A single photo is converted into a 3D mesh with PBR textures using a render-enhanced auto-encoder, two data-augmentation schemes, and a multi-view texturing pipeline, with the claimed result being the best quality amo...

  3. LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors

    cs.CV 2024-12 conditional novelty 6.0 of 10

    LiftImage3D generates small-motion video clips from one image, registers them with MASt3R, and fits a distortion-aware 3D Gaussian field whose canonical scene renders new views.

  4. Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Two-side generation and matching of intermediate views with a score distillation loss improves single-reference object pose estimation under large viewpoint changes.

  5. FreeMesh: Boosting Mesh Generation with Coordinates Merging

    cs.GR 2025-05 conditional novelty 5.0 of 10

    FreeMesh shows that rearranging coordinates into same-axis groups before byte-pair encoding reduces per-token entropy and sequence length, improving point-cloud conditioned mesh generation quality.

  6. CheapNVS: Real-Time On-Device Narrow-Baseline Novel View Synthesis

    cs.CV 2025-01 conditional novelty 5.0 of 10

    CheapNVS performs narrow-baseline single-view novel view synthesis on mobile devices by learning warping and inpainting in parallel from a shared latent space.

  7. UnCommon Objects in 3D

    cs.CV 2025-01 conditional novelty 5.0 of 10

    uCO3D is a large, diverse, high-quality real-object video dataset with 3D annotations that improves training of feedforward 3D reconstruction and text-to-3D models.

  8. Pragmatist: Multiview Conditional Diffusion Models for High-Fidelity 3D Reconstruction from Unposed Sparse Views

    cs.CV 2024-12 conditional novelty 5.0 of 10

    Pragmatist turns sparse unposed photos of an object into a high-fidelity 3D mesh by generating consistent canonical views with a diffusion model, reconstructing a triplane mesh, then refining camera poses and texture ...

  9. Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A texture-generation pipeline that produces 1024x1024 textures from text in 3.07 seconds on an H100, with quality comparable to SyncMVD and other prior methods.

  10. MV-Adapter: Multi-view Consistent Image Generation Made Easy

    cs.CV 2024-12 conditional novelty 5.0 of 10

    An adapter bolts multi-view generation onto frozen text-to-image diffusion models, producing consistent views at up to 768 resolution on SDXL.

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