LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors

• URL: http://arxiv.org/abs/2412.09597v1
• Date: Thu, 12 Dec 2024 18:58:42 GMT
• Title: LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors
• Authors: Yabo Chen, Chen Yang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Wei Shen, Wenrui Dai, Hongkai Xiong, Qi Tian,
• Abstract summary: Single-image 3D reconstruction remains a fundamental challenge in computer vision. Recent advances in Latent Video Diffusion Models offer promising 3D priors learned from large-scale video data. We propose LiftImage3D, a framework that effectively releases LVDMs' generative priors while ensuring 3D consistency.
• Score: 107.83398512719981
• License:
• Abstract: Single-image 3D reconstruction remains a fundamental challenge in computer vision due to inherent geometric ambiguities and limited viewpoint information. Recent advances in Latent Video Diffusion Models (LVDMs) offer promising 3D priors learned from large-scale video data. However, leveraging these priors effectively faces three key challenges: (1) degradation in quality across large camera motions, (2) difficulties in achieving precise camera control, and (3) geometric distortions inherent to the diffusion process that damage 3D consistency. We address these challenges by proposing LiftImage3D, a framework that effectively releases LVDMs' generative priors while ensuring 3D consistency. Specifically, we design an articulated trajectory strategy to generate video frames, which decomposes video sequences with large camera motions into ones with controllable small motions. Then we use robust neural matching models, i.e. MASt3R, to calibrate the camera poses of generated frames and produce corresponding point clouds. Finally, we propose a distortion-aware 3D Gaussian splatting representation, which can learn independent distortions between frames and output undistorted canonical Gaussians. Extensive experiments demonstrate that LiftImage3D achieves state-of-the-art performance on two challenging datasets, i.e. LLFF, DL3DV, and Tanks and Temples, and generalizes well to diverse in-the-wild images, from cartoon illustrations to complex real-world scenes.

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