A Neural Network for Detailed Human Depth Estimation from a Single Image Article Swipe

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Sicong Tang , Feitong Tan , Kelvin Cheng , Zhaoyang Li , Siyu Zhu , Ping Tan ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1910.01275

This paper presents a neural network to estimate a detailed depth map of the foreground human in a single RGB image. The result captures geometry details such as cloth wrinkles, which are important in visualization applications. To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively. We design a training strategy to ensure both base and detail shapes can be faithfully learned by the corresponding network branches. Furthermore, we introduce a novel network layer to fuse a rough depth map and surface normals to further improve the final result. Quantitative comparison with fused `ground truth' captured by real depth cameras and qualitative examples on unconstrained Internet images demonstrate the strength of the proposed method. The code is available at https://github.com/sfu-gruvi-3dv/deep_human.

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Artificial intelligence Computer science Fuse (electrical) Artificial neural network Depth map Ground truth RGB color model Residual Base (topology) Computer vision Code (set theory) Image (mathematics) Surface (topology) Visualization Scheme (mathematics) Pattern recognition (psychology) Algorithm Mathematics Geometry Engineering Programming language Set (abstract data type) Electrical engineering Mathematical analysis

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1910.01275
PDF: https://arxiv.org/pdf/1910.01275
OA Status: green
Cited By: 1
References: 36
Related Works: 10
OpenAlex ID: https://openalex.org/W2979168069

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DOI: https://doi.org/10.48550/arxiv.1910.01275

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Title: A Neural Network for Detailed Human Depth Estimation from a Single Image

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-10-03

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Authors: Sicong Tang, Feitong Tan, Kelvin Cheng, Zhaoyang Li, Siyu Zhu, Ping Tan

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Landing page: https://arxiv.org/abs/1910.01275

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PDF URL: https://arxiv.org/pdf/1910.01275

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Concepts: Artificial intelligence, Computer science, Fuse (electrical), Artificial neural network, Depth map, Ground truth, RGB color model, Residual, Base (topology), Computer vision, Code (set theory), Image (mathematics), Surface (topology), Visualization, Scheme (mathematics), Pattern recognition (psychology), Algorithm, Mathematics, Geometry, Engineering, Programming language, Set (abstract data type), Electrical engineering, Mathematical analysis

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Cited by: 1

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cited_by_percentile_year
countries_distinct_count	0
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citation_normalized_percentile