Vision & animation technology generates visual assets or objects in 2D or 3D form. Our focus in this field is to interpret and understand images or videos, then convert them into various formats for application.
For example, we study domains such as video-to-motion — where we build 3D models from 2D images or transfer human movement in a video to a target object — or lip-syncing, where we generate lip animations that correspond to the input text.
KRAFTON AI’s goal is to use these studies to streamline processes that require designers or animators to execute them manually, while at the same time maintaining realistic quality.
Voice synthesis technology allows computers to imitate human speech and speak like humans. Our focus in this field is to mix emotion into natural, human-like voices.
For example, we study how to add emotions and tone to text-to-speech (TTS) output, where TTS is a technology that converts input sentences to human voices. We also study voice conversion, which transforms an input voice into a different type of voice.
KRAFTON AI uses voice technology to develop innovative solutions that can express rich emotion, tone, and accents. Our goal is to transcend physical limitations that might be inherent in speech to expand the potential of communication.
Language models help computers understand and generate text in human languages. Our focus in this field is to solve specific problems using the many applications that emerged with the introduction of foundational models.
For example, we could build a casual, conversational system using GPT-based models or build a Q&A system specialized in a specific field.
KRAFTON AI tries to improve the capacity of computers to understand human language, a crucial part of computer-human interaction, and build an experience that allows the user to forge deeper relationships with the system.
Multi-modal learning is the ability of a model to understand and process different types of data at once. Our focus in this field is to use a mix of data containing various formats such as images, text, audio, and videos to its fullest capacity by leveraging the unique information that each data format contains.
For example, we could build large-scale databases that contain both images and text for specific topics, or link language models to TTS technology so we can generate audio that fits the context and meaning of what is to be said.
KRAFTON AI no longer classifies deep learning applications into individual fields, but rather tries to take a more general approach in solving more complicated and difficult problems.
We must fuse together technologies spanning all fields to make possible the interaction between humans and computers that we envision.
Reinforcement learning is a technique where an agent is developed to interact with its environment so it can learn how to find the optimal action strategy (policy). This technology is closest to KRAFTON’s core drive as a game developer and maker of masterpieces.
For example, we could train the agent on important rules and strategies from a game and have it compete with other users or help developers test the game as it is being designed.
Reinforcement learning will play a crucial role in interactions between humans and computers as it helps AI develop the human-like capacity to learn and make decisions about complicated problems.
Data-centric approaches focus on the quality of data used to enhance model performance. We study data management strategies surrounding data collection, processing, labeling, augmentation, and other processes that play an important role in building high-quality datasets.
For example, we study whether it is possible to achieve the same level of performance with less data or what type of data we would need to supplement for improved model performance.
Data-centric research based on deep learning enables AI models to make more accurate and powerful predictions. This field contributes to improving the reliability and fairness of AIs and is crucial in establishing a long-term direction and foundation for research organizations.
KRAFTON AI proactively researches deep learning technology to improve the efficiency of game creation, publishing, and operation, and provide users with new and innovative gaming experiences. We also strive to establish a procedure that will allow us to carefully examine potential AI ethics issues (such as hate speech or privacy issues). In April 2023, the ‘KRAFTON AI Ethics Committee’ was launched to facilitate ongoing discussion and debate on AI ethics issues. Members from various teams such as legal, data, and privacy have voluntarily participated in the committee. The ‘KRAFTON AI Ethics Committee’ also holds regular round table discussions every month to establish ethical guidelines and codes of conduct, and monitor compliance. Through these ongoing discussions on ethical issues, we aim to prevent potential problems in advance.
KRAFTON AI will continue to collaborate with internal and external experts to actively respond to AI ethics issues, ensuring our technology creates social value and achieves sustainable development.