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• W4385142388 abstract "We propose a novel monocular human depth estimation method using video sequences as training data. We jointly train the depth and 3D motion flow networks with photometric and 3D geometric consistency constraints. Instead of depth ground truth, we take the surface normal as the pseudo-label to supervise the depth network learning. The estimated depth may exist texture copy artifact when the clothes on the human body have patterns and text marks (non-dominant color). Thus, we also propose an approach to alleviate the texture copy problem by estimating and adjusting the color of non-dominant color areas. Extensive experiments on public datasets and the Internet have been conducted. The comparison results prove that our method can produce competitive human depth estimation and has better generalization ability than state-of-the-art methods." @default.
• W4385142388 created "2023-07-23" @default.
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• W4385142388 date "2023-07-22" @default.
• W4385142388 modified "2023-10-16" @default.
• W4385142388 title "Monocular human depth estimation with 3D motion flow and surface normals" @default.
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• W4385142388 doi "https://doi.org/10.1007/s00371-023-02995-8" @default.
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