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Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior

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arxiv 2303.14184 v2 pith:JOR25BDB submitted 2023-03-24 cs.CV

Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior

Junshu Tang , Tengfei Wang , Bo Zhang , Ting Zhang , Ran Yi , Lizhuang Ma , Dong Chen This is my paper
classification cs.CV
keywords imagepriorcreationdiffusionsinglefirsthigh-fidelityhigh-quality
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen textures. To address this challenge, we leverage prior knowledge from a well-trained 2D diffusion model to act as 3D-aware supervision for 3D creation. Our approach, Make-It-3D, employs a two-stage optimization pipeline: the first stage optimizes a neural radiance field by incorporating constraints from the reference image at the frontal view and diffusion prior at novel views; the second stage transforms the coarse model into textured point clouds and further elevates the realism with diffusion prior while leveraging the high-quality textures from the reference image. Extensive experiments demonstrate that our method outperforms prior works by a large margin, resulting in faithful reconstructions and impressive visual quality. Our method presents the first attempt to achieve high-quality 3D creation from a single image for general objects and enables various applications such as text-to-3D creation and texture editing.

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Cited by 9 Pith papers

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  2. LRM: Large Reconstruction Model for Single Image to 3D

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  3. DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

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    DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.

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