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Owner3D framework enables training-free localized 3D stylization

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Owner3D, a novel framework designed for localized 3D stylization that operates without requiring additional training. This method integrates style control directly into the large reconstruction model (LRM) process, addressing challenges like style leakage and boundary ambiguity. Owner3D employs ownership-guided style writing to confine style injection to specific regions and utilizes boundary dual slots to maintain separate feature sources for target and non-target areas, thereby improving style fidelity and appearance preservation. AI

IMPACT This research could lead to more precise and efficient methods for editing and stylizing 3D models, impacting fields like content creation and virtual environments.

RANK_REASON This is a research paper detailing a new technical framework for 3D stylization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Owner3D framework enables training-free localized 3D stylization

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This is a research paper detailing a new technical framework for 3D stylization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Suchang Tao, Kaifeng Shi, Zhiyan Liu, Zhuoyuan Jiang, Yuqi Ouyang ·

    Owner3D: Ownership-Guided Style Writing for Training-Free Localized 3D Stylization

    arXiv:2608.14078v1 Announce Type: new Abstract: Localized 3D stylization aims to modify the appearance of a specified object part while preserving the remaining surfaces. In large reconstruction models (LRMs), this task is challenging because style is injected into intermediate a…

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