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スタイライズGS: 3Dガウスのスプラッティングのための制御可能なスタイライズ
IEEE transactions on pattern analysis and machine intelligence
|August 28, 2025
まとめ
StylizedGSは,3D Gaussian Splatting (3DGS) を使用して効率的な3Dニューラルスタイル転送を提供し,3Dモデルの幾何学的な精度と芸術的な制御を改善します. この方法は3Dコンテンツの作成におけるユーザー体験と創造的な柔軟性を高めます.
科学分野:
- コンピュータ・ビジョン
- コンピュータ・グラフィックス
- 人工知能
背景:
- 3Dの生成と編集は XR技術の進歩に不可欠です
- Neural Radiance Fields (NeRF) ベースの3Dスタイライゼーション方法は,効率と幾何学的な精度の制限に直面しています.
- 3Dシーンのスタイリゼーションには柔軟な芸術的なコントロールが求められます.
研究 の 目的:
- 効率的な3Dニューラルスタイル転送フレームワークを導入します.
- 3Dスタイライゼーションにおける知覚要因に対する適応制御を可能にします.
- 既存の方法の効率と幾何学的な精度の問題に対処する.
主な方法:
- 効率的な3Dスタイリゼーションのために3Dガウスのスプラッティング (3DGS) 表現を使用した.
- フィルターベースの精錬を実装し,アーティファクトを削除し,近隣ベースのスタイル損失を細かく調整する幾何学と色.
- 積もった深さ保全の損失と,幾何学的な整合性を維持するための規則化.
主要な成果:
- 高品質の3Dスタイリゼーションを 忠実なブラシストロークと幾何学的な一貫性で達成しました
- 色,スケール,地域に対する柔軟なユーザー制御で効率的なスタイライゼーションが実証されました.
- スタイライゼーションの品質と推論の速度の両方で既存の方法を上回りました.
結論:
- 3Dニューラルスタイル転送の効率的で制御可能なソリューションを提供します.
- このフレームワークは3Dコンテンツの作成におけるユーザー体験と創造的可能性を高めます.
- 立体的な細部を保存しながら 芸術的なスタイルを3Dシーンに 移すための強力な方法を提供します
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