Synthetic Data

Solution

Synthetic Data

To overcome challenges in AI development—such as depletion of large-scale training data, privacy regulations, and data bias—we leverage computer vision and computer graphics to generate virtual environment–based synthetic data. This enables enhanced training data productivity and improved AI model performance across both industrial and sports applications.

Car (object)

Implement 3D modeling by analyzing real vehicle (object) characteristics
Design and meticulously create 3D backgrounds for synthetic data generation, simulating parameters such as weather, lighting, and climate
Produce custom datasets tailored to training objectives, varying camera perspectives for backgrounds, vehicles (objects), and scenarios
Generate diverse data types—segmentation maps, 2D/3D bounding boxes, depth images, etc.—according to specific purposes

Special purpose (defense)

Implement precise 3D modeling of target objects (equipment, weapons, etc.) based on detailed analysis
Acquire hard-to-capture real-world data by simulating 3D-modeled objects and environments
Deliver accurately labeled datasets even in scenarios difficult to discern with the naked eye
Generate diverse data types—segmentation maps, 2D/3D bounding boxes, depth images, etc.—tailored to specific use cases

Golf/Taekwondo (Motion)

Accurate acquisition of real motion data using optical motion capture systems
Refined processing of 3D motion data derived from actual recordings
Generation of precise 2D and 3D keypoint labeling datasets based on 3D-rendered images and motion data
Data augmentation through diverse 3D models featuring varied genders, body types, and costumes

Download Sample Golf Swing Synthesis Data

Download synthetic golf swing data and use it in your AI development.

Indoor driving range

다운로드 수: 1회

Outdoor golf driving range

다운로드 수: 1회

Outdoor golf courses

다운로드 수: 1회

In-Cabin Automotive

Collect diverse driving and in-cabin scenario data using optical motion capture systems and facial expression recognition devices
Establish precise labeling environments by 3D modeling vehicle interiors, drivers, and occupants
Generate optimized, customized datasets by varying gender, ethnicity, body type, attire, camera count, and placement
Create and deliver accurate, diverse labeling data (2D/3D keypoints, segmentation, etc.) to meet automotive regulatory requirements (e.g., DMS, OMS)

Face recognition

3D model backgrounds alongside diverse genders, ethnicities, body types, and costumes for varied scenarios
Capture and apply precise data using facial expression recognition devices and high-precision optical motion-capture systems
Integrate landmarks (eyes, nose, mouth, ears) into 3D characters to produce customized facial-expression datasets
Generate and deliver purpose-specific data—including 2D/3D keypoints, bounding boxes, segmentation maps, and depth maps

Solution

Synthetic Data

To overcome the challenges of AI development, such as the depletion of large training data for AI models, privacy laws, and data bias, we are utilizing computer vision and computer graphics technology to create synthetic data based on virtual environments. They are applying the technology in general industry and sports to improve training data productivity and AI model performance.

Car (object)

Analyze real-world vehicle (object) features to implement 3D modeling
Design backgrounds for synthetic data generation and recreate them in 3D with parameters such as weather, light, climate, etc.
Create customized data for learning purposes by composing backgrounds, vehicles (objects), and situations from different camera perspectives.
Create a variety of purposeful data, including segmentation, 2D/3D bounding boxes, depth images, and more.

Special purpose (defense)

Analyze target objects (equipment, weapons, etc.) for sophisticated 3D modeling
Simulate 3D modeled objects and backgrounds to capture data that is difficult to obtain in the real world.
Provide accurate labeling data, even in situations where visual identification is difficult
Create a variety of purposeful data, including segmentation, 2D/3D bounding boxes, depth images, and more.

Golf/Taekwondo (Motion)

Accurately capture real-world motion based on optical motion capture data
Sophisticated 3D motion data processing with real-world motion data
Create accurate 2D and 3D keypoint labeling data based on images and motion data recreated with 3D modeling.
Data augmentation with 3D modeling for gender, body shape, clothing, and more

In-Cabin Automotive Monitoring (In-Cabin Automotive)

Utilize optical motion capture systems and facial expression recognition devices to collect a variety of driving and vehicle interior context data
3D model the vehicle interior environment, driver, and passengers to create accurate labeling data
Generate optimized, personalized data by adjusting for gender, race, body type, clothing, as well as camera quantity and location
Provide accurate and diverse labeling data (2D/3D keypoint, segmentation, etc.) in compliance with automotive regulations such as DMS, OMS, etc.

Face recognition

Enable 3D modeling that includes gender, race, body type, clothing, and even backgrounds for a variety of situations.
Acquire and apply accurate data with facial expression recognition devices and optical high-precision motion capture systems
Create personalized data by grafting various landmarks such as eyes, nose, mouth, ears, and more onto 3D characters.
Create and deliver fit-for-purpose data, including 2D/3D keypoints, bounding boxes, segmentation, depth maps, and more.

Solution

Synthetic Data

Car (object)

Special purpose (defense)

Golf/Taekwondo (Motion)

Download Sample Golf Swing Synthesis Data

Indoor driving range

Outdoor golf driving range

Outdoor golf courses

In-Cabin Automotive

Face recognition

Solution

Synthetic Data

Car (object)

Special purpose (defense)

Golf/Taekwondo (Motion)

In-Cabin Automotive Monitoring (In-Cabin Automotive)

Face recognition

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