DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flow

2024-11-25

arXiv.org

AI Summary

Existing generative 3D models—particularly single-view or sparse multi-view approaches—are computationally expensive and often produce coarse shapes with insufficient geometric detail. Method: We propose a lightweight, data-driven latent-space streaming enhancement framework that establishes the first “coarse-to-fine” geometric refinement paradigm. Our method jointly optimizes local detail synthesis and global structural consistency via data-dependent latent-space flow modeling, and introduces a learnable token-matching mechanism to explicitly enforce spatial correspondence. Crucially, it requires no fine-tuning of the upstream generator and achieves broad compatibility across diverse 3D generation backbones with minimal training overhead. Contribution/Results: Experiments demonstrate substantial improvements in surface fidelity and geometric richness across multiple benchmarks. The approach is highly efficient to train, flexible to deploy, and provides a novel low-cost pathway toward high-fidelity 3D content generation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: 3D Computer Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present DetailGen3D, a generative approach specifically designed to enhance these generated 3D shapes. Our key insight is to model the coarse-to-fine transformation directly through data-dependent flows in latent space, avoiding the computational overhead of large-scale 3D generative models. We introduce a token matching strategy that ensures accurate spatial correspondence during refinement, enabling local detail synthesis while preserving global structure. By carefully designing our training data to match the characteristics of synthesized coarse shapes, our method can effectively enhance shapes produced by various 3D generation and reconstruction approaches, from single-view to sparse multi-view inputs. Extensive experiments demonstrate that DetailGen3D achieves high-fidelity geometric detail synthesis while maintaining efficiency in training.

Problem

• Enhancing geometric detail in generated 3D shapes
• Modeling coarse-to-fine transformation via data-dependent flows
• Ensuring spatial correspondence during local detail synthesis

Innovation

• Data-dependent flows in latent space
• Token matching for spatial correspondence
• Training data matching coarse shapes