CNS-Edit++: 結合ニューラル形状表現によるカテゴリ非依存の3D編集
CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation
3D形状の編集を、大域的な意味情報と局所的な空間文脈を結合した表現の最適化として定式化し、カテゴリに依存しない編集を可能にするフレームワークを提案した。
詳しい要約
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著者: Jingyu Hu, Weilong Yan, Zhengzhe Liu, Haipeng Li, Ka-Hei Hui, Hao Zhang, Chi-Wing Fu
分類: cs.CV, cs.GR
原文アブストラクト
This paper presents a latent-space 3D shape editing framework built upon a coupled neural shape (CNS) representation and a neural feature volume optimization. This work extends CNS-Edit, built on Coupled Neural Shape optimization, to CNS-Edit++, by generalizing the category-specific coupled representation to category-agnostic 3D shape editing with foundation models. The Coupled Neural Shape (CNS) representation couples a global latent code that captures high-level shape semantics with a 3D neural feature volume that provides spatial context for local shape manipulation. Then we formulate a coupled neural shape optimization procedure that co-optimizes these two components subject to a given editing operation. Our framework can be instantiated on both the category-specific 3D inversion model and category-agnostic 3D foundation models. We provide various shape editing operators, including copy, resize, delete, mix, point-wise drag, and region-wise drag, each of which is formulated as an objective to guide the CNS optimization. To preserve regions outside the editing area, we further introduce two complementary region-wise control mechanisms, i.e., KV-cache replacement and latent feature regularization. Extensive quantitative and qualitative evaluations across different 3D generative models demonstrate the strong capabilities of our approach over state-of-the-art solutions.
