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V3D: Video Diffusion Models are Effective 3D Generators

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arxiv 2403.06738 v1 pith:ODC2A7DS submitted 2024-03-11 cs.CV

Zilong Chen , Yikai Wang , Feng Wang , Zhengyi Wang , Huaping Liu This is my paper
classification cs.CV
keywords diffusionvideogenerationmodelmodelscapacityconsistencygenerate
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Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency. Our code is available at https://github.com/heheyas/V3D

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Cited by 11 Pith papers

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

  1. Geometrically Consistent Multi-View Scene Generation from Freehand Sketches

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    A single freehand sketch can generate a full orbit of photorealistic views in one pass, trained on a 9k synthetic sketch-to-multiview dataset with camera-aware adapters and SfM-supervised correspondences.

  2. MVGBench: Comprehensive Benchmark for Multi-view Generation Models

    cs.GR 2025-06 conditional novelty 7.0 of 10

    MVGBench evaluates multi-view generators through self-consistency of 3D reconstructions and uses this protocol to rank 12 models and build a better one.

  3. Epipolar Geometry Improves Video Generation Models

    cs.CV 2025-10 conditional novelty 6.0 of 10

    Ranking generated videos by their epipolar (Sampson) error and fine-tuning Wan2.1 with Flow-DPO cuts epipolar error 31% and raises human-rated 3D consistency from 54% to 72%.

  4. 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.

  5. Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A video diffusion backbone fine-tuned on 4M densely captioned 360-degree renderings generates spatially consistent multi-view images for 3D assets from image plus detailed text input.

  6. Make Your MoVe: Make Your 3D Contents by Adapting Multi-View Diffusion Models to External Editing

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A tuning-free dual-pipeline that injects original normal latents into an edited multi-view diffusion stream, preserving geometry during 2D-to-3D appearance editing.

  7. Edit360: 2D Image Edits to 3D Assets from Any Angle

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A training-free method that propagates a 2D edit applied at any chosen viewpoint across a full 360-degree orbit by fusing anchor-view and front-view video-diffusion trajectories.

  8. NOVA3D: Normal Aligned Video Diffusion Model for Single Image to 3D Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A video diffusion model fine-tuned to output both color and normal maps, aligned by a geometry-temporal attention block, reconstructs textured 3D meshes from a single image.

  9. FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    FVGen uses GAN-based adversarial distillation and softened reverse KL divergence to compress a video diffusion teacher for novel-view synthesis into a four-step student with comparable quality.

  10. Reconstructing 4D Spatial Intelligence: A Survey

    cs.CV 2025-07 accept novelty 4.0 of 10

    A review that classifies 4D scene reconstruction methods into five progressive levels: low-level cues, scene components, dynamic scenes, interactions, and physics.

  11. Sparse-View 3D Reconstruction: Recent Advances and Open Challenges

    cs.CV 2025-07 conditional novelty 3.0 of 10

    A comprehensive survey that organizes sparse-view 3D reconstruction methods into geometry-based, NeRF, 3DGS, and diffusion-based categories, with benchmarks and open challenges.

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