Introducing NALBI’s state-of-the-art deep learning model.

From the beginning of the 21st century, pilot studies on Machine Learning drew attention to making a machine learn by itself to overcome limitations on high opportunity costs to build logic and data for creating a system.

Research on Deep Learning Technology has come a long way since 2012, and self-learning machines have achieved meaningful results in various fields.

Although different services exist in the Virtual Human industry at present, rapid growth in advanced technology requires higher-level services.

Therefore, a service-based perspective in the field, as well as technological growth, is also essential.

Indeed, NALBI is looking forward to launching a Virtual Human Creation Service that enables anyone to comfortably create a virtual human on mobile devices through real-time 3D Motion Capture technology with accurate movement detection.

NALBI’s model, as the ultimate aggregation of our past seven years of investment in R&D, can dispose of full-body tracking, including natural face, body, and hand movement in real-time, to create high-quality videos without heterogeneity.

Our objective is to maximize the joy of communication by building a compelling connection between people worldwide.

We research for lighter and faster technology applications.

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Face Reenactment

Able to express a high degree of freedom in head angle and delicate facial expressions through the 3D engine and Deep-Learning methods.

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3D Motion Capture

3D Motion Capture- Perform real-time and highly accurate full-body tracking without professional equipment.

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Depth map upsampling

Depth Map Upsampling- Utilizes 4K RGB images and a low-resolution depth map to attain a high-resolution depth map.

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Face&Age Recognition

Face & Age Recognition- Estimates sex and age through face scan on the Cortex-M7 MCU (for device personalization usages).

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Human segmentation

Separates a human object from the background in real-time from imported images and videos. 

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Depth estimation

Approximates relative depth on a single RGB camera via neural networks. @ARM CPU or Mobile GPU

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Single-RGB Bokeh

Produces a natural bokeh effect with a single RGB image. (Blur Intensity Control and Focus Point function)

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Hair segmentation

Segregates the hair area in real-time. @ARM CPU and dyeing effect @ Mobile GPU (OpenGLE2.0)

Face Detection with Thermal Imaging Camera

Competent in implementing efficient face detection algorithms on the thermal imaging camera without false alarms on high-temperature objects aside from the human facial area.

NALBI
FACENET

Recognizes multiple biological information (face, sex, and age) with a single Deep Learning algorithm model optimized for MCU’s low RAM. 

Image
Segmentation

Separates human objects and background areas effortlessly with a single image on mobile; Capable of natural Bokeh effect production (blur intensity and focus point) Segregates hair, background, human object, and per-instance area.

Single-RGB Bokeh

Produces a natural bokeh effect with a single RGB image. (Blur Intensity Control and Focus Point function)

Hair Segmentation

Segregates the hair area in real-time. @ARM CPU and dyeing effect @ Mobile GPU (OpenGLE2.0)

Human Segmentation

Separates a human object from the background in real-time from imported images and videos