DECOR-GAN: 3D Shape Detailization by Conditional Refinement (2012.09159v2)

Abstract: We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties of high-resolution and detailed 3D geometry from a small set of exemplars by treating the problem as that of geometric detail transfer. Given a low-resolution coarse voxel shape, our network refines it, via voxel upsampling, into a higher-resolution shape enriched with geometric details. The output shape preserves the overall structure (or content) of the input, while its detail generation is conditioned on an input "style code" corresponding to a detailed exemplar. Our 3D detailization via conditional refinement is realized by a generative adversarial network, coined DECOR-GAN. The network utilizes a 3D CNN generator for upsampling coarse voxels and a 3D PatchGAN discriminator to enforce local patches of the generated model to be similar to those in the training detailed shapes. During testing, a style code is fed into the generator to condition the refinement. We demonstrate that our method can refine a coarse shape into a variety of detailed shapes with different styles. The generated results are evaluated in terms of content preservation, plausibility, and diversity. Comprehensive ablation studies are conducted to validate our network designs. Code is available at https://github.com/czq142857/DECOR-GAN.

• The paper introduces a novel GAN framework that refines coarse 3D shapes by conditioning detail transfer with masked convolutions.
• It achieves high-fidelity outputs on datasets like ShapeNet by integrating reconstruction loss and dual-branch style supervision to prevent mode collapse.
• The approach has practical implications for augmented reality, gaming, and CAD by enabling efficient, diverse, and enhanced 3D model generation.

Insightful Overview of DECOR-GAN: 3D Shape Detailization by Conditional Refinement

The paper "DECOR-GAN: 3D Shape Detailization by Conditional Refinement" presents a sophisticated framework for enhancing the resolution and geometric details of 3D voxel-based shapes through a generative adversarial network (GAN). This approach addresses a critical challenge in 3D modeling: creating diverse, high-fidelity 3D shapes from coarse, low-resolution inputs with minimal dependency on large datasets of detailed examples.

Conceptual Foundation

DECOR-GAN operates within the domain of geometric detail transfer, parallel to 2D image stylization methods but focusing on 3D shapes. It simplifies the production of various high-resolution shapes by conditioning the generated details on an input style code derived from exemplar shapes. The network structure includes a 3D convolutional neural network (CNN) as a generator for voxel upsampling and a 3D PatchGAN discriminator to ensure local realism based on detailed training exemplars.

Methodology

The innovation in DECOR-GAN lies in its ability to preserve the original content of the coarse input shape while integrating stylistic details from the designated style code. This is achieved through the following key elements:

• Masked Convolutions: The generator incorporates mask usage to refine voxel shapes efficiently, focusing computational resources within a valid area defined by the input shape.
• Conditioned Discrimination: The discriminator uses both global and style-specific assessment branches to prevent mode collapse—a common problem in GANs where the generator outputs repetitive results.
• Reconstruction Loss: During training, when content and style originate from the same example, reconstruction losses enforce output fidelity to the detailed target.

Numerical Results and Evaluation

The robustness of DECOR-GAN is demonstrated on categories like cars, airplanes, and chairs from the ShapeNet dataset. Metrics such as Loose-IOU, Local Plausibility, and Cls-score showcase the model's effectiveness in balancing content fidelity, local realism, and stylistic diversity.

Implications and Future Directions

The practical implications of DECOR-GAN extend to fields like augmented reality, gaming, and computer-aided design, where high-resolution 3D models are crucial. The method aligns with a growing trend towards neural approaches capable of synthesizing richly detailed 3D content from sparse initial data.

Theoretically, DECOR-GAN opens avenues for research in:

• Hierarchical Detail Representation: Exploring multi-level abstractions of detail may enhance the model's adaptability to different scales and complexities of 3D shapes.
• Broader Applications: Extending the framework's applicability beyond voxel grids to other 3D representations such as meshes or point clouds.
• Integration with Other Modalities: Investigating how DECOR-GAN could integrate with other domains, such as image-driven 3D reconstruction or mixed-media designs.

The evolving landscape of AI-powered design can leverage DECOR-GAN's conditional refinement capabilities to push the boundaries of automated 3D model generation, fostering creativity and efficiency in digital content creation.