3D人間ポーズ推定のための起源中心の部分ベースのポーズ分解
Zhijie Lin1, Jinxin Yao2, Juan Huang3
1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China. linzhijie@zust.edu.cn.
Scientific reports
|August 23, 2025
まとめ
この研究では,3Dの人間の姿勢の推定のためのOrigin-centric パーツトランスフォーマー (OPFormer) が導入されます. OPFormerは,骨格の部分を分離し再結合することで,ローカルとグローバルな特徴の学習を改善します.
科学分野:
- コンピュータ・ビジョン
- 機械学習
背景:
- トランスフォーマーモデルは 2D画像から 3Dの人間の姿勢の見積もりを示しています
- 現在の方法は グローバルな骨格の文脈と 局所的な関節の特徴のバランスをとるのに苦労しています
研究 の 目的:
- 改善された3D人間の姿勢の見積もりのために,新しいトランスフォーマーブロック,Origin-centric Part Transformer (OPFormer) を提案する.
- グローバルとローカルの両方の空間的依存性を2Dの人間の骨格で捉えるための制限に対処します.
主な方法:
- 骨格分離と骨格再結合のステップでOPFormerブロックを導入しました.
- 骨格分離は 細粒子の局所的な空間特性を抽出します
- スケルト・リコンビネーションは 人間の骨格の起源を使って 空間トランスフォーマー・エンコーダーで ローカルとグローバルの特徴を融合させます
主要な成果:
- Human3.6MとMPI-INF-3DHPのデータセットで最先端の性能を達成しました.
- OPFormerは,追加のトレーニングデータなしでHuman3.6Mで37.6mmの平均関節位置誤差 (MPJPE) を達成しました.
- 3D人間のポーズ評価の 卓越した精度を示した.
結論:
- OPFormerブロックは,正確な3D人間の姿勢の見積もりのために,ローカルとグローバル機能を効果的に統合します.
- この新しいアプローチは 2Dから3Dの人間のポーズ再構築の分野を前進させています
- OPFormerは,単眼2D入力から正確な人間のポーズ推定のための堅牢なソリューションを提供します.
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