AGG: Amortized Generative 3D Gaussians for Single Image to 3D (2401.04099v1)

Abstract: Given the growing need for automatic 3D content creation pipelines, various 3D representations have been studied to generate 3D objects from a single image. Due to its superior rendering efficiency, 3D Gaussian splatting-based models have recently excelled in both 3D reconstruction and generation. 3D Gaussian splatting approaches for image to 3D generation are often optimization-based, requiring many computationally expensive score-distillation steps. To overcome these challenges, we introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image, eliminating the need for per-instance optimization. Utilizing an intermediate hybrid representation, AGG decomposes the generation of 3D Gaussian locations and other appearance attributes for joint optimization. Moreover, we propose a cascaded pipeline that first generates a coarse representation of the 3D data and later upsamples it with a 3D Gaussian super-resolution module. Our method is evaluated against existing optimization-based 3D Gaussian frameworks and sampling-based pipelines utilizing other 3D representations, where AGG showcases competitive generation abilities both qualitatively and quantitatively while being several orders of magnitude faster. Project page: https://ir1d.github.io/AGG/

• The paper introduces a novel AGG framework that decomposes geometry and texture generation to bypass intensive per-instance optimization.
• It employs a cascaded generation pipeline combining a coarse 3D Gaussian representation with a super-resolution module enhanced by point-voxel convolutions.
• AGG achieves competitive quantitative and qualitative results with significantly reduced inference time, offering practical advances for 3D reconstruction in AR/VR and robotics.

Amortized Generative 3D Gaussians for Single Image 3D Reconstruction

The paper presents an approach to address the computational challenges in generating 3D models from single images using a novel framework known as Amortized Generative 3D Gaussians (AGG). This work introduces a method that significantly reduces the computational overhead typically associated with per-instance optimization, leveraging efficient 3D Gaussian splatting techniques.

Technical Contributions

The central contribution of this paper is the development of the AGG framework, which bypasses the intensive score-distillation steps generally required for 3D Gaussian approaches. The proposed method decomposes the generation of 3D Gaussian locations and their appearance attributes, enabling simultaneous optimization and eliminating the need for per-instance fine-tuning.

Key innovations in this research include:

1. Hybrid Representation Decomposition: The design of a hybrid generator, which separately handles geometry and texture generation using distinct transformer networks. This decomposition stabilizes the training process by isolating the generation challenges associated with geometry and texture.
2. Cascaded Generation Pipeline: The introduction of a two-stage pipeline, where the coarse generator produces a preliminary 3D Gaussian representation later refined by a super-resolution module, significantly enhancing the fidelity and scalability of the models.
3. Incorporation of Efficient Point-Voxel Convolutions: The application of point-voxel convolutional networks in the super-resolution stage, augmenting local feature extraction and facilitating texture refinement through RGB integration.

Strong Numerical Results

AGG demonstrates competitive performance both qualitatively and quantitatively against existing optimization-based frameworks and sampling-based methods using alternative 3D representations. The approach is reported to operate several orders of magnitude faster than traditional models, showcasing a significant reduction in inference time, which is crucial for practical applications in virtual and augmented reality.

Implications and Future Directions

The implications of this research extend to both theoretical and practical domains within AI and computer vision. The method introduces a new paradigm for efficient 3D reconstruction, offering scalability and speed that can benefit applications requiring rapid model generation from limited visual inputs.

The ability of AGG to generalize across various object classes without per-instance adaptation underscores its potential utility in diverse real-world scenarios, from content creation in gaming and movies to applications in robotic vision and e-commerce.

Future work could explore expanding AGG's capabilities to handle more complex scenes with occlusions and multiple objects. Additionally, further enhancing the resolution and detail of generated models could be pursued by integrating more sophisticated neural architectures and exploring alternative explicit 3D representations.

In conclusion, the AGG framework represents a significant advancement in single-image-to-3D generation, balancing the trade-offs between computational efficiency and generation fidelity, and setting a foundation for future exploration in amortized 3D model generation techniques.