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Related Experiment Video

Updated: Apr 3, 2026

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
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DreamLifting: A Plug-in Module Lifting MV Diffusion Models for 3D Asset Generation.

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    Summary
    This summary is machine-generated.

    Lightweight Gaussian Asset Adapter (LGAA) automates 3D asset creation by unifying geometry and PBR material generation. This novel framework efficiently leverages multi-view diffusion priors for data-efficient fine-tuning.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • 3D Modeling

    Background:

    • Creating 3D assets with physically based rendering (PBR) materials is labor-intensive.
    • Existing 3D generation methods often separate geometry modeling from texture synthesis.
    • There is a need for autonomous pipelines for end-to-end PBR-ready 3D asset creation.

    Purpose of the Study:

    • To present a novel framework, Lightweight Gaussian Asset Adapter (LGAA), for unified 3D asset generation.
    • To enable end-to-end generation of geometry and PBR materials using multi-view diffusion priors.
    • To achieve data-efficient fine-tuning of diffusion models for 3D asset creation.

    Main Methods:

    • LGAA employs a modular design with a Wrapper to adapt network layers from multi-view diffusion models.
    • A Switcher component aligns multiple Wrapper layers for diverse diffusion priors (geometry, PBR).
    • A tamed variational autoencoder (VAE) Decoder predicts 2D Gaussian Splatting (2DGS) with PBR channels, followed by mesh extraction.

    Main Results:

    • LGAA demonstrates superior performance with both text- and image-conditioned multi-view diffusion models.
    • The modular design allows flexible incorporation of multiple diffusion priors.
    • The knowledge-preserving scheme enables efficient fine-tuning using only 69k multi-view instances.

    Conclusions:

    • LGAA successfully unifies geometry and PBR material generation for 3D assets.
    • The framework significantly improves data efficiency in adapting diffusion models for 3D tasks.
    • LGAA offers a promising direction for autonomous and high-quality 3D asset creation.