A data-driven editing framework for automatic 3D garment modeling

Liu, Li; Su, Zhuo; Fu, Xiaodong; Liu, Lijun; Wang, Ruomei; Luo, Xiangfeng

doi:10.1007/s11042-016-3688-4

Cited by 10 publications

(5 citation statements)

References 63 publications

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“…are shown in Figure 4, which are used to dress the different 3D gait models with different poses and body shapes. The clothes recognition method and virtual dressing [34] involves four steps. First, obtain the initial 3D pose.…”

Section: D Parametric Gait Semantic Data Extraction Under Covariate mentioning

confidence: 99%

“…are shown in Figure 4, which are used to dress the different 3D gait models with different poses and body shapes. The clothes recognition method and virtual dressing [34] make our 3D gait semantic data extraction accurate. In order to extract 3D semantic parameters of the body, including 3D body shape semantic parameter , 3D joint semantic data , and 3D clothing semantic parameter , where denotes the type of clothing, an iterative process is conducted to minimize Equation (2) with a good initial pose and clothing condition .…”

Section: D Parametric Gait Semantic Data Extraction Under Covariate mentioning

confidence: 99%

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Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

Luo

Tjahjadi

2020

Sensors

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Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness.

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Section: D Parametric Gait Semantic Data Extraction Under Covariate mentioning

confidence: 99%

Section: D Parametric Gait Semantic Data Extraction Under Covariate mentioning

confidence: 99%

Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

Luo

Tjahjadi

2020

Sensors

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“…It can predict clothing category, attribute and landmarks, that help to determine the length of clothes. According to the basic category of clothing, the prior designed virtual clothes are selected to dress (using virtual dressing [38]) the 3D body before shape deformation.…”

Section: D Gait Semantic Data Optimizationmentioning

confidence: 99%

View and Clothing Invariant Gait Recognition via 3D Human Semantic Folding

Luo

Tjahjadi

2020

IEEE Access

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A novel 3-dimensional (3D) human semantic folding is introduced to provide a robust and efficient gait recognition method which is invariant to camera view and clothing style. The proposed gait recognition method comprises three modules: (1) 3D body pose, shape and viewing data estimation network (3D-BPSVeNet); (2) gait semantic parameter folding model; and (3) gait semantic feature refining network. First, 3D-BPSVeNet is constructed based on a convolution gated recurrent unit (ConvGRU) to extract 2dimensional (2D) to 3D body pose and shape semantic descriptors (2D-3D-BPSDs) from a sequence of gait parsed RGB images. A 3D gait model with virtual dressing is then constructed by morphing the template of 3D body model using the estimated 2D-3D-BPSDs and the recognized clothing styles. The more accurate 2D-3D-BPSDs without clothes are then obtained by using the silhouette similarity function when updating the 3D body model to fit the 2D gait. Second, the intrinsic 2D-3D-BPSDs without interference from clothes are encoded by sparse distributed representation (SDR) to gain the binary gait semantic image (SD-BGSI) in a topographical semantic space. By averaging the SD-BGSIs in a gait cycle, a gait semantic folding image (GSFI) is obtained to give a high-level representation of gait. Third, a gait semantic feature refining network is trained to refine the semantic feature extracted directly from GSFI using three types of prior knowledge, i.e., viewing angles, clothing styles and carrying condition. Experimental analyses on CMU MoBo, CASIA B, KY4D, OU-MVLP and OU-ISIR datasets show a significant performance gain in gait recognition in terms of accuracy and robustness.

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“…4 Liu Li proposed a customer-driven rapid 3D clothing modeling method. 5 This method inputs the designed clothing component model and classifies the components through shape and style analysis, which builds a virtual clothing component library.…”

Section: Introductionmentioning

confidence: 99%

3D modeling design and rapid style recommendation of polo shirt based on interactive genetic algorithm

Zhang

Cong

2020

Journal of Engineered Fibers and Fabrics

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This paper proposes a design method for personalized 3D modeling and a rapid style recommendation of polo shirts based on interactive genetic algorithms. Through the research on the parametric multi-style design method of polo shirts, this paper proposes 3D design methods such as the conversion of 3D models, component splicing, and stripes matching mapping rules. In addition, we establish a coded component library of style parts and striped designs to realize the component modeling of tailor-made styles for polo shirts. Besides, the Interactive Genetic Algorithm (IGA) is introduced in the article. Through the algorithm coding, initial population generation, selection, using the scoring mechanism to obtain the user’s suitability evaluation of the program, crossover, and mutation, etc., gradually generating a user-satisfied polo shirt style model design plan, a personalized rapid style recommendation of the user-oriented polo shirt is established. This system can present tailored polo shirt style for customers, and WYSIWYG, to find the most favorite clothing styles for customers and lower the threshold of clothing design expertise.

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A data-driven editing framework for automatic 3D garment modeling

Cited by 10 publications

References 63 publications

Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

View and Clothing Invariant Gait Recognition via 3D Human Semantic Folding

3D modeling design and rapid style recommendation of polo shirt based on interactive genetic algorithm

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