LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors (2412.09597v1)

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.

• The paper introduces a framework that uses latent video diffusion priors to convert 2D images into 3D Gaussians, achieving enhanced consistency and visual quality.
• It employs an articulated trajectory generation and robust neural matching strategy to manage large camera motions and calibrate accurate point clouds.
• Experimental results demonstrate significant PSNR improvements on datasets like LLFF and DL3DV, paving the way for advanced VR and AR applications.

An Overview of LiftImage3D: From Single Image to 3D Gaussians with LVDM

The paper "LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors" presents a comprehensive framework for converting a single 2D image into a 3D representation using 3D Gaussians, leveraging Latent Video Diffusion Models (LVDMs). This research addresses multifaceted challenges associated with 3D reconstruction from a single perspective, such as large camera motion-induced quality degradation, camera control precision, and diffusion process distortions.

Technical Contributions and Methodology

LiftImage3D introduces several novel techniques to ensure that generative priors from LVDMs contribute effectively to reconstructing 3D scenes while maintaining consistency across dimensions. Key components of the framework include:

1. Articulated Trajectory Generation: The paper introduces an articulated trajectory strategy that decomposes large camera movements into smaller, manageable motions. This strategy allows for wider view coverage and ensures stable frame generation.
2. Robust Neural Matching for Calibration: The authors utilize a neural matching model, MASt3R, to calibrate camera poses and facilitate the creation of corresponding point clouds from generated frames. This approach addresses the challenges posed by the lack of intrinsic 3D information and enables accurate point cloud registration.
3. Distortion-aware 3D Gaussian Splatting: The framework proposes a distortion-aware 3D Gaussian splatting (3DGS) representation, which adapts to frame-specific distortions and outputs consistent canonical Gaussians. This model is built upon robust 3D Gaussian splatting techniques and includes a deformation field to counteract geometric inconsistencies.
4. Integration of Depth Priors: The method incorporates depth priors from monocular depth estimation calibrated with coarse scales from neural matching, enhancing 3D consistency and depth accuracy.

Experimental Evaluation and Results

The efficacy of LiftImage3D was demonstrated through extensive experiments on datasets such as LLFF, DL3DV, and Tanks and Temples. The results indicated significant improvements in visual quality and 3D consistency, outperforming existing methods by considerable margins. Notably, the method achieved a PSNR increase of 4.73 on the LLFF dataset and 3.92 for DL3DV scenes. The comparative studies underscored the ability of the model to generalize across varied inputs, highlighting its strength in both cartoon-like illustrations and complex real-world scenes.

Implications and Future Developments

The framework presents profound implications for applications requiring 3D scene reconstruction from a single image, such as virtual and augmented reality content development. By effectively harnessing video generation priors, this research paves the way for more immersive and interactive media experiences, reducing reliance on extensive multi-view inputs.

Further research can explore optimizing the fusion of different LVDM backbones within LiftImage3D, as experiments show potential performance gains across various setups. Exploration into real-time processing enhancements and extending the framework to handle more complex environmental conditions might also yield significant advancements.

LiftImage3D embodies a key step forward in addressing the limitations associated with single-image 3D reconstruction, and its integration of video generative models marks a promising avenue for future exploration in the domain of computer vision and graphics.