ShapeGen: Towards High-Quality 3D Shape Synthesis

2025-11-25

AI Summary

Current single-image 3D generation methods suffer from insufficient geometric detail, over-smoothed surfaces, and structural discontinuities—particularly in thin-shell geometries—rendering them inadequate for industrial-grade applications. To address these limitations, we propose a multi-dimensional collaborative optimization framework: (1) a geometry-aware implicit 3D representation tailored for high-fidelity surface modeling; (2) a linear Transformer architecture to enhance long-range geometric coherence; and (3) a progressive super-resolution strategy integrated with strengthened multi-view 3D supervision. Our approach significantly improves geometric fidelity and structural integrity, achieving state-of-the-art performance on ShapeNet and Objaverse benchmarks. The generated models exhibit high-precision geometry, topologically consistent thin-wall structures, and immediate usability—enabling seamless integration into professional 3D production pipelines.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Inspired by generative paradigms in image and video, 3D shape generation has made notable progress, enabling the rapid synthesis of high-fidelity 3D assets from a single image. However, current methods still face challenges, including the lack of intricate details, overly smoothed surfaces, and fragmented thin-shell structures. These limitations leave the generated 3D assets still one step short of meeting the standards favored by artists. In this paper, we present ShapeGen, which achieves high-quality image-to-3D shape generation through 3D representation and supervision improvements, resolution scaling up, and the advantages of linear transformers. These advancements allow the generated assets to be seamlessly integrated into 3D pipelines, facilitating their widespread adoption across various applications. Through extensive experiments, we validate the impact of these improvements on overall performance. Ultimately, thanks to the synergistic effects of these enhancements, ShapeGen achieves a significant leap in image-to-3D generation, establishing a new state-of-the-art performance.

Problem

• Generating 3D shapes with intricate details from images
• Overcoming overly smoothed surfaces in 3D shape synthesis
• Resolving fragmented thin-shell structures in generated assets

Innovation

• Improved 3D representation and supervision methods
• Scaled up resolution for enhanced detail
• Utilized linear transformers for efficient processing