DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement

• URL: http://arxiv.org/abs/2409.06129v1
• Date: Tue, 10 Sep 2024 00:51:49 GMT
• Title: DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement
• Authors: Qimin Chen, Zhiqin Chen, Vladimir G. Kim, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri,
• Abstract summary: We present a 3D modeling method which enables end-users to refine or detailize 3D shapes using machine learning. We show that our ability to localize details enables novel interactive creative and applications.
• Score: 38.719572669042925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a 3D modeling method which enables end-users to refine or detailize 3D shapes using machine learning, expanding the capabilities of AI-assisted 3D content creation. Given a coarse voxel shape (e.g., one produced with a simple box extrusion tool or via generative modeling), a user can directly "paint" desired target styles representing compelling geometric details, from input exemplar shapes, over different regions of the coarse shape. These regions are then up-sampled into high-resolution geometries which adhere with the painted styles. To achieve such controllable and localized 3D detailization, we build on top of a Pyramid GAN by making it masking-aware. We devise novel structural losses and priors to ensure that our method preserves both desired coarse structures and fine-grained features even if the painted styles are borrowed from diverse sources, e.g., different semantic parts and even different shape categories. Through extensive experiments, we show that our ability to localize details enables novel interactive creative workflows and applications. Our experiments further demonstrate that in comparison to prior techniques built on global detailization, our method generates structure-preserving, high-resolution stylized geometries with more coherent shape details and style transitions.

Related papers

• DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flow [44.72037991063735]
  DetailGen3D is a generative approach specifically designed to enhance generated 3D shapes. Our key insight is to model the coarse-to-fine transformation directly through data-dependent flows in latent space. We introduce a token matching strategy that ensures accurate spatial correspondence during refinement.
  arXiv  Detail & Related papers  (2024-11-25T17:08:17Z)
• DreamPolish: Domain Score Distillation With Progressive Geometry Generation [66.94803919328815]
  We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain.
  arXiv  Detail & Related papers  (2024-11-03T15:15:01Z)
• CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner [34.78919665494048]
  CraftsMan can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces. Our method achieves high efficacy in producing superior-quality 3D assets compared to existing methods.
  arXiv  Detail & Related papers  (2024-05-23T18:30:12Z)
• ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering [24.622120688131616]
  ShaDDR is an example-based deep generative neural network which produces a high-resolution textured 3D shape. Our method learns to detailize the geometry via multi-resolution voxel upsampling and generate textures on voxel surfaces. The generated shape preserves the overall structure of the input coarse voxel model.
  arXiv  Detail & Related papers  (2023-06-08T02:35:30Z)
• DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation [105.97545053660619]
  We present a new text-guided 3D shape generation approach DreamStone. It uses images as a stepping stone to bridge the gap between text and shape modalities for generating 3D shapes without requiring paired text and 3D data. Our approach is generic, flexible, and scalable, and it can be easily integrated with various SVR models to expand the generative space and improve the generative fidelity.
  arXiv  Detail & Related papers  (2023-03-24T03:56:23Z)
• 3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations [81.45521258652734]
  We propose a method to create plausible geometric and texture style variations of 3D objects. Our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation.
  arXiv  Detail & Related papers  (2021-08-30T02:28:31Z)
• SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation [50.53931728235875]
  We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds. Compared with existing models, SP-GAN is able to synthesize diverse and high-quality shapes with fine details.
  arXiv  Detail & Related papers  (2021-08-10T06:49:45Z)
• DECOR-GAN: 3D Shape Detailization by Conditional Refinement [50.8801457082181]
  We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We demonstrate that our method can refine a coarse shape into a variety of detailed shapes with different styles.
  arXiv  Detail & Related papers  (2020-12-16T18:52:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.