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    V3D enhances 3D generation by using video diffusion models for detailed object creation. This approach generates high-quality 3D models and novel views efficiently from single images.

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    Area of Science:

    • Computer Vision
    • 3D Computer Graphics
    • Artificial Intelligence

    Background:

    • Automatic 3D generation is gaining traction, but current methods often sacrifice detail for speed.
    • Limited model capacity and 3D data hinder the creation of highly detailed objects.

    Purpose of the Study:

    • To leverage pre-trained video diffusion models for improved 3D generation.
    • To introduce a geometrical consistency prior for multi-view consistent 3D generation.
    • To enable high-quality 3D object and scene generation from limited input.

    Main Methods:

    • V3D fine-tunes state-of-the-art video diffusion models for 3D generation.
    • A geometrical consistency prior is integrated to ensure multi-view consistency.
    • The model generates $360^{\circ }$ orbit frames from a single image, enabling reconstruction of meshes or 3D Gaussians.

    Main Results:

    • High-quality 3D meshes and 3D Gaussians are generated within 3 minutes.
    • The method achieves superior generation quality and multi-view consistency compared to existing approaches.
    • Scene-level novel view synthesis is enabled with precise camera path control.

    Conclusions:

    • V3D effectively utilizes video diffusion models for advanced 3D content creation.
    • The proposed approach significantly improves detail and consistency in 3D generation.
    • V3D offers a fast and versatile solution for generating 3D objects and scenes.