Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image (2405.20343v3)

Abstract: 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. Previous methods based on Score Distillation Sampling (SDS) can produce diversified 3D results by distilling 3D knowledge from large 2D diffusion models, but they usually suffer from long per-case optimization time with inconsistent issues. Recent works address the problem and generate better 3D results either by finetuning a multi-view diffusion model or training a fast feed-forward model. However, they still lack intricate textures and complex geometries due to inconsistency and limited generated resolution. To simultaneously achieve high fidelity, consistency, and efficiency in single image-to-3D, we propose a novel framework Unique3D that includes a multi-view diffusion model with a corresponding normal diffusion model to generate multi-view images with their normal maps, a multi-level upscale process to progressively improve the resolution of generated orthographic multi-views, as well as an instant and consistent mesh reconstruction algorithm called ISOMER, which fully integrates the color and geometric priors into mesh results. Extensive experiments demonstrate that our Unique3D significantly outperforms other image-to-3D baselines in terms of geometric and textural details.

• The paper introduces Unique3D, a novel approach integrating multi-view diffusion and the ISOMER algorithm to generate detailed 3D meshes from a single image.
• The methodology overcomes Score Distillation constraints by delivering consistent, high-fidelity geometric and textural details in approximately 30 seconds.
• Empirical results show superior performance over traditional baselines, providing significant advances for applications in gaming, architecture, and 3D generative research.

Insights on Unique3D: Efficient 3D Mesh Generation from Single Images

The research paper on Unique3D presents a sophisticated framework designed to efficiently generate high-quality 3D meshes from single-view images. This approach addresses long-standing challenges in the domain of 3D computer vision, emphasizing the integration of multi-view diffusion models and innovative reconstruction techniques to improve fidelity, consistency, and generalizability.

Unique3D's contribution lies primarily in its novel image-to-3D framework, which surpasses existing methods in producing intricate geometric and textural details. The framework effectively mitigates the limitations of prior approaches reliant on Score Distillation Sampling (SDS), which are often hampered by extensive per-case optimization times and inconsistencies, commonly referred to as the Janus problem.

Unique3D includes a multi-view diffusion model that finely tunes large 2D diffusion models to generate multi-view images and their corresponding normal maps effectively. A crucial component of this framework is the multi-level upscale strategy, which incrementally enhances resolution, reaching high fidelity in 3D mesh reconstruction. The introduction of the \underline{i}nstant and c\underline{o}nsistent \underline{me}sh \underline{r}econstruction algorithm, ISOMER, significantly bolsters the capacity to integrate color and geometric priors into final mesh results, achieving this within approximately 30 seconds.

The rigorous design and execution of this methodology are exemplified through thorough empirical validation, which shows Unique3D's superiority over other image-to-3D baselines. Resultant meshes exhibit enhanced geometric and textural details compared to conventional methods, as substantiated by both qualitative and quantitative evaluations across various metrics like PSNR, SSIM, and Chamfer Distance (CD).

The implications of this research extend both practically and theoretically in the field of 3D generative AI. Practically, Unique3D can facilitate advancements in gaming, architecture, and entertainment industries by providing a rapid and reliable method for 3D content creation from simple 2D images. Theoretically, the combination of diffusion-based models with mesh reconstruction suggests new avenues for further research into enhancing 3D generation techniques, particularly in reducing computational costs while increasing output fidelity.

Future research should aim to overcome current limitations such as handling skewed or non-perspective inputs in multi-view predictions and ensuring robust model performance with a more diverse dataset. Expanding the framework to support texture maps in geometric coloring could also enhance the utility of generated models in professional and creative domains. Overall, Unique3D marks a significant step forward in automated 3D mesh generation from 2D imagery, providing a compelling foundation for subsequent exploration and development in this critical area of computer vision.