在本論文中，我們提出一個系統——SegmentedFusion，該系統通過使用具有骨架資訊與單目深度相機來重建非剛體運動的人體模型。我們的方法將身體分割成不同的部分並為每個部分建立標準座標空間，通過估測一個稠密空間的六自由度動作場，將累積的各個模型變形到即時影格中。這項研究的關鍵點是為每個身體部分建立一個變形的連接標準邊界框，並將其用作一個空間來累積深度資訊。透過使用兩組剛體變換參數來有效地表示一個空間的稠密空間動作場。總體而言，SegmentedFusion是一種能夠通過使用消費級深度相機高效率地掃描非剛體變形人體表面以及估算稠密動作場的系統。
實驗結果表明，我們的系統在快速運動和拓撲變化有穩健的結果。由於我們的方法不需要事先假設，因此SegmentedFusion可以廣泛應用於現實的人體運動。

In this paper, we present SegmentedFusion, a system which has the capability of reconstructing non-rigid motion of a human model by using a single depth camera with skeleton information. Our approach estimates a dense volumetric 6D motion field that warps the integrated model into the live frame by segmenting body into different parts and building a canonical space for each part. The key feature of this work is that a deformed and connected canonical bounding-box for each part is created, and is used as a volume to integrate data. The dense volumetric warp field of one volume is represented efficiently by using two sets of rigid transformation parameters. Overall, SegmentedFusion is a system which is able to memory-efficiently scan non-rigidly deformed human surface as well as estimating dense motion field by using a consumer-grade depth camera.
The experimental results demonstrate that our system is robust against fast inter-frame motion and topology changes. Since our method does not require prior assumption, the SegmentedFusion can be applied to real data which contains a wide range of human motion.

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