通过改进3D表示与线性注意力，实现更高质量的图像转3D生成。
ShapeGen: Towards High-Quality 3D Shape Synthesis
- 采用新型3D表示与线性变压器提升生成细节
- 生成结果表面更精细，无碎片化结构
- 适合需要高保真3D资产的工业设计与影视制作
受图像与视频生成范式启发，3D形状生成已取得显著进展，能够从单张图像快速合成高保真3D资产。然而，当前方法仍存在细节不足、表面过度平滑及细薄结构断裂等问题，导致生成结果难以满足艺术家的高标准要求。本文提出ShapeGen，通过改进3D表示与监督机制、提升分辨率、利用线性变压器的优势，实现高质量图像到3D形状的生成。实验验证了各项改进对整体性能的影响。得益于这些技术的协同效应，ShapeGen在图像到3D生成任务上实现显著提升，达到新的最先进水平。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。