- The paper introduces the omage representation, a 12-channel 64x64 pixel image that encapsulates 3D geometry and material properties.
- It employs Diffusion Transformers to sequentially generate geometric and material channels, effectively addressing challenges in 3D modeling.
- Evaluations on the ABO dataset demonstrate competitive p-FID and p-KID scores, highlighting the promise of image-based 3D object generation.
An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion
The paper introduces an innovative approach for generating realistic 3D models using a novel representation called "Object Images" or "omages." This method effectively converts complex 3D shapes into a more manageable 2D format by encapsulating surface geometry, appearance, and patch structures within a 64x64 pixel image. The authors demonstrate that this transformation allows existing image generation models, such as Diffusion Transformers, to be directly applied to 3D shape generation, thereby addressing the challenges associated with geometric and semantic irregularity inherent in polygonal meshes.
Methodology
Object Images
The core of the method lies in the omage representation, which captures 3D geometric details and photorealistic materials in a 12-channel image. This image includes four channels for geometry and occupancy, eight channels for materials such as albedo, normal, metalness, and roughness maps. The mapping from 3D to 2D is facilitated by a step involving UV-alignment, which translates the surface patches of a 3D object into a UV space that can be rasterized efficiently.
The UV patches are repacked using a method that prioritizes preserving the connectivity and integrity of the patches, mitigating issues such as overlapping regions, touching boundaries, and excessive patch counts. The image is then downsampled with edge snapping to ensure that the boundaries are preserved accurately.
Generative Modeling
The authors leverage Diffusion Transformers to model the distribution of these omages. This choice is premised on the capability of transformers to capture long-range dependencies and their success in set generation tasks, which aligns well with the nature of omages that combine aspects of both image and set generation. The pipeline involves first generating the geometric channels and then conditionally generating the material channels.
Results and Evaluations
The paper evaluates the approach on the Amazon Berkeley Objects (ABO) dataset, containing high-quality designer-made 3D models with UV atlases across various categories. The proposed method demonstrates a remarkable ability to generate challenging geometric structures and photorealistic materials, outperforming or being on par with state-of-the-art methods like MeshGPT and 3DShape2VecSet in terms of point cloud FID and p-KID metrics.
Numerical Results
- Point Cloud FID (p-FID): The method achieves comparable p-FID scores to 3DShape2VecSet and performs better than MeshGPT, validating the efficacy of omages in capturing complex geometric structures.
- p-KID: Similar trends are observed where the method's p-KID values indicate high fidelity in generating realistic shapes.
Implications
The ability to generate detailed 3D models with photorealistic materials from a compact 64x64 pixel representation has significant implications. It simplifies the processing of 3D shapes, making it feasible to apply neural networks designed for 2D image data to 3D object generation tasks. This approach can potentially streamline the creation of 3D assets in various industries, from gaming and film to manufacturing and robotics.
Theoretically, the transformation from 3D to omages could open new avenues for research in generative modeling, enabling the exploration of hybrid models that utilize both 2D and 3D representations. Future developments could focus on enhancing the resolution and fidelity of omages, improving the patch-repacking algorithms, and extending the approach to more complex and diverse datasets.
Conclusion
The paper presents a novel and effective paradigm for 3D object generation, bridging the gap between image-based generative models and 3D shape modeling. By converting intricate 3D geometries into a regular 2D image format, the authors provide a robust framework that leverages the strengths of diffusion models to produce high-quality, realistic 3D objects. While the current resolution is limited to 64x64 pixels, future work could extend this to higher resolutions and broader applications, ultimately enhancing the capabilities of automated 3D asset generation.
