18509647. METHOD OF GENERATING A HUMAN MODEL AND DEVICE THEREFOR simplified abstract (ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE)
Contents
- 1 METHOD OF GENERATING A HUMAN MODEL AND DEVICE THEREFOR
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD OF GENERATING A HUMAN MODEL AND DEVICE THEREFOR - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD OF GENERATING A HUMAN MODEL AND DEVICE THEREFOR
Organization Name
ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE
Inventor(s)
METHOD OF GENERATING A HUMAN MODEL AND DEVICE THEREFOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 18509647 titled 'METHOD OF GENERATING A HUMAN MODEL AND DEVICE THEREFOR
Simplified Explanation
The method described in the abstract involves generating a human model from an input image by utilizing various feature maps and parameters.
- Feature maps are generated from the input image, including a body center map, part index map, body part map, and parameter map.
- A part-attentive feature is created for each body part based on the part index map and body part map.
- The part-attentive feature is readjusted based on the parameter map.
- A pose parameter is generated based on the readjusted part-attentive feature.
Potential Applications
This technology could be used in various fields such as computer vision, virtual reality, augmented reality, and human-computer interaction.
Problems Solved
This method solves the problem of accurately generating a human model from an input image by utilizing feature maps and parameters to capture body parts and poses effectively.
Benefits
The benefits of this technology include improved accuracy in human modeling, better understanding of body poses, and enhanced applications in various industries.
Potential Commercial Applications
Potential commercial applications of this technology include virtual try-on solutions for clothing retailers, personalized fitness apps, and virtual fitting rooms for e-commerce websites.
Possible Prior Art
One possible prior art for this technology could be the use of convolutional neural networks for human pose estimation in computer vision applications.
What are the limitations of this method in generating human models accurately from input images?
The limitations of this method may include the complexity of the feature maps and parameters used, which could affect the accuracy of the generated human model.
How does this method compare to existing techniques for human modeling in terms of efficiency and computational resources?
This method may offer improved efficiency and require fewer computational resources compared to existing techniques, as it utilizes part-attentive features and pose parameters to generate human models from input images.
Original Abstract Submitted
A method of generating a human model according to the present disclosure may include generating a plurality of feature maps from an input image, wherein the plurality of feature maps include a body center map, a part index map, a body part map and a parameter map, generating a part-attentive feature configured with feature maps for each body part based on the part index map and the body part map, readjusting the part-attentive feature based on the parameter map and generating a pose parameter based on the readjusted part-attentive feature.
