mlsanityData to 7 Oct 2026

ShapeGen: Towards High-Quality 3D Shape Synthesis

arXiv 2511.20624 ↗First posted 25 Nov 2025cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 42 of its 61 references that are in the map. First posted on arXiv 25 Nov 2025.

Builds on

42 of 61 references.

References · 42

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30 more
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Citing papers per year: 2026: 1.

1 paper.

  1. 2026MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modelingcited by 0