TL;DR
A multi-author preprint titled "UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement" was posted as an arXiv submission (arXiv:2512.21185). The paper and a project page have been made available, but technical details, results and code availability are not confirmed in the source provided.
What happened
Researchers released a preprint titled "UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement," listed on arXiv as arXiv:2512.21185 and associated with a project page. The author list on the submission includes Tanghui Jia, Dongyu Yan, Dehao Hao, Yang Li, Kaiyi Zhang, Xianyi He, Lanjiong Li, Jinnan Chen, Lutao Jiang, Qishen Yin, Long Quan, Ying-Cong Chen and Li Yuan. The bibliographic record indicates a 2025 submission year, and the project page referenced in the source was posted on January 2, 2026. The available source does not include the body of the paper or experimental results; therefore precise descriptions of the methods, quantitative performance, datasets, or whether code and models will be released are not confirmed in the source.
Why it matters
- High-fidelity 3D shape generation, if substantiated, can improve realism in graphics, simulation and virtual environments.
- Scalable geometric refinement techniques could enable larger or more detailed shape outputs without linear growth in cost, depending on implementation.
- Advances in generation methods often affect downstream tasks such as 3D modeling, augmented reality, robotics and digital content creation.
- A new preprint with a project page signals the authors intend to share details publicly, which can accelerate validation and wider use if code and data are released.
Key facts
- Title: "UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement."
- Authors: Tanghui Jia, Dongyu Yan, Dehao Hao, Yang Li, Kaiyi Zhang, Xianyi He, Lanjiong Li, Jinnan Chen, Lutao Jiang, Qishen Yin, Long Quan, Ying-Cong Chen, Li Yuan.
- Document type: arXiv preprint, cited as arXiv:2512.21185 (submission year listed as 2025).
- Project page: https://pku-yuangroup.github.io/UltraShape-1.0/ (linked in the source).
- Project page timestamp in the provided metadata: 2026-01-02T11:15:52+00:00.
- The source provided does not include the paper's main text, experimental tables, or figures.
- Claims about performance, datasets, implementation details and code release are not confirmed in the source.
- The entry appears in a standard academic preprint format (BibTeX citation shown in the source).
What to watch next
- Whether the full paper on arXiv provides reproducible details (architectures, training regimes, datasets) — not confirmed in the source.
- If and when the authors release code, pretrained models or data on the project page or a repository — not confirmed in the source.
- Peer review, follow-up publications, or independent evaluations that verify any performance claims — not confirmed in the source.
Quick glossary
- 3D shape generation: Techniques and models that create three-dimensional object representations, often used in graphics, simulation and CAD.
- Geometric refinement: A process that iteratively improves the geometric detail or accuracy of a shape representation.
- Preprint: A version of a scientific manuscript shared publicly before formal peer review and journal publication.
- arXiv: An open-access repository where researchers post preprints across fields such as computer science, physics and mathematics.
Reader FAQ
What is UltraShape 1.0?
It is the name of a preprint titled "UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement" listed on arXiv; detailed content is not confirmed in the source.
Who are the authors?
The authors listed are Tanghui Jia, Dongyu Yan, Dehao Hao, Yang Li, Kaiyi Zhang, Xianyi He, Lanjiong Li, Jinnan Chen, Lutao Jiang, Qishen Yin, Long Quan, Ying-Cong Chen and Li Yuan.
Is the paper available to read?
The work is cited as an arXiv preprint (arXiv:2512.21185) and a project page URL is provided; access to the full manuscript and materials is not confirmed in the source.
Are code and models provided?
Not confirmed in the source.
@article{jia2025ultrashape, title={ UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement}, author={ Jia, Tanghui and Yan, Dongyu and Hao, Dehao and Li, Yang and Zhang, Kaiyi and He, Xianyi…
Sources
- High-Fidelity 3D Shape Generation
- High-Fidelity 3D Shape Generation via Scalable Geometric …
- (PDF) UltraShape 1.0: High-Fidelity 3D Shape Generation …
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