Research
* indicates equal contribution
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ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model
Fangfu Liu*,
Wenqiang Sun* ,
Hanyang Wang* ,
Yikai Wang ,
Haowen Sun ,
Junliang Ye ,
Jun Zhang ,
Yueqi Duan
Arxiv, 2024
[arXiv]
[Code]
[Project Page]
In this paper, we propose ReconX, a novel 3D scene reconstruction paradigm that reframes the ambiguous reconstruction challenge as a temporal generation task. The key insight is to unleash the strong generative prior of large pre-trained video diffusion models for sparse-view reconstruction.
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Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
Fangfu Liu*,
Hanyang Wang*
Shunyu Yao ,
Shengjun Zhang,
Jie Zhou,
Yueqi Duan
Arxiv, 2024
[arXiv]
[Code]
[Project Page]
In this paper, we propose Physics3D, a novel method for learning various physical properties of 3D objects through a video diffusion model. Our approach involves designing a highly generalizable physical simulation system based on a viscoelastic material model, which enables us to simulate a wide range of materials with high-fidelity capabilities.
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Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
Kailu Wu ,
Fangfu Liu,
Zhihan Cai,
Runjie Yan,
Hanyang Wang,
Yating Hu,
Yueqi Duan ,
Kaisheng Ma
Conference on Neural Information Processing Systems (NeurIPS), 2024
[arXiv]
[Code]
[Project Page]
In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability. Unique3D can generate a high-fidelity textured mesh from a single orthogonal RGB image of any object in under 30 seconds.
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Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
Fangfu Liu,
Hanyang Wang,
Weiliang Chen,
Haowen Sun,
Yueqi Duan
European Conference on Computer Vision (ECCV), 2024
[arXiv]
[Code]
[Project Page]
We introduce a novel 3D customization method, dubbed Make-Your-3D that can personalize high-fidelity and consistent 3D content from only a single image of a subject with text description within 5
minutes.
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