SPLICE: Part-Level 3D Shape Editing from Local Semantic Extraction to Global Neural Mixing

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dc.contributor.authorZhou, Jinen_US
dc.contributor.authorYang, Hongliangen_US
dc.contributor.authorXu, Pengfeien_US
dc.contributor.authorHuang, Huien_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:04:12Z
dc.date.available2025-10-07T06:04:12Z
dc.date.issued2025
dc.description.abstractNeural implicit representations of 3D shapes have shown great potential in 3D shape editing due to their ability to model highlevel semantics and continuous geometric representations. However, existing methods often suffer from limited editability, lack of part-level control, and unnatural results when modifying or rearranging shape parts. In this work, we present SPLICE, a novel part-level neural implicit representation of 3D shapes that enables intuitive, structure-aware, and high-fidelity shape editing. By encoding each shape part independently and positioning them using parameterized Gaussian ellipsoids, SPLICE effectively isolates part-specific features while discarding global context that may hinder flexible manipulation. A global attention-based decoder is then employed to integrate parts coherently, further enhanced by an attention-guiding filtering mechanism that prevents information leakage across symmetric or adjacent components. Through this architecture, SPLICE supports various part-level editing operations, including translation, rotation, scaling, deletion, duplication, and cross-shape part mixing. These operations enable users to flexibly explore design variations while preserving semantic consistency and maintaining structural plausibility. Extensive experiments demonstrate that SPLICE outperforms existing approaches both qualitatively and quantitatively across a diverse set of shape-editing tasks.en_US
dc.description.sectionheadersShape Extraction or Editing
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251288
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251288
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251288
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Artificial intelligence; Graphics systems and interfaces; Shape analysis
dc.subjectComputing methodologies → Artificial intelligence
dc.subjectGraphics systems and interfaces
dc.subjectShape analysis
dc.titleSPLICE: Part-Level 3D Shape Editing from Local Semantic Extraction to Global Neural Mixingen_US
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