Data Structure ?
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Data Structure ?
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README.md
我々は、様々な制約のない日常活動において、3D人体姿勢をセルフセンターでリアルタイムに推定する初の方法を提案しました。この設定には、ハードウェア設定の移動性や、追跡障害からの迅速な回復が必要な長時間のキャプチャセッションの堅牢性など、独自の一連の課題があります。我々は、標準的な野球帽を、帽子に取り付けた単一の魚眼レンズに基づく高品質な姿勢推定デバイスに変換する、新しい軽量設定に基づいてこれらの課題を解決します。キャプチャされたセルフセンターのリアルタイムストリームから、CNNベースの3D姿勢推定方法は、消費者向けGPU上で60Hzの頻度で動作します。新しいハードウェア設定に加えて、我々の他の主な貢献は、1）上から下への魚眼画像の大規模なグラウンドトゥルース訓練コーパス、2）セルフセンターの視点の独自の属性を考慮した新しい解きほぐし3D姿勢推定方法です。我々の評価が示すように、既存のベースラインと比較して、我々はより低い3D関節誤差とより良い2Dカバレッジを達成しています。
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