Research Areas
Language Model (sLM/LLM)
We conduct full-cycle research on language models—covering pre-training, post-training, and fine-tuning—focused on enabling machines to understand and generate human language.
Our goal is to develop game-specialized language model agents capable of comprehension, reasoning, decision-making, and emotional expression. We explore a wide spectrum of models, from large-scale LLMs to small, lightweight sLMs suitable for on-device deployment.
KRAFTON AI seeks to create natural and emotionally resonant human-computer interactions that enrich player immersion.
Multi-modal Learning
Our multi-modal learning research focuses on enabling AI to understand and process diverse data types—such as images, text, speech, and video—in an integrated manner. We aim to fully leverage the unique information each modality provides, for example, by combining images and text to build thematic databases, or merging language models with TTS systems to generate contextually appropriate speech. Ultimately, we envision multi-modal AI that can comprehend real-time video, voice, and text during gameplay, communicate with players via voice, and play games alongside them.
At KRAFTON AI, we are committed to going beyond siloed AI fields—fusing technologies to solve complex, ambitious challenges and realizing immersive human-AI interaction through seamless integration of all AI modalities.
Reinforcement Learning
(Vision & Animation / Speech)
Generative AI (Vision & Animation / Speech)
At KRAFTON AI, we research generative AI technologies to automate and enhance the creation of visual and audio assets essential for game development. Our work includes inferring and modeling 3D structures from 2D images, reconstructing character motion from human video footage (Video-to-Motion), and generating emotionally expressive speech from text (Text-to-Speech).
We also explore Voice Conversion technologies that can naturally transform one person’s voice into another. Through these innovations, we aim to maximize both production efficiency and immersive gameplay experiences.
Data-centric AI
Data-centric AI 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.
Application
Virtual Friend
We create agents in various forms of friends who can play games with users like you. Those agents can understand the current situation in the game, chat, and play with you.
Singing Voice Synthesis
We create songs by applying the unique voice and pitch of the character to the melody and lyrics. We have produced a song for ANA, one of KRAFTON’s Virtual Humans.
3D Avatar Generation
We can generate 3D avatars trained in specific styles using photos of actual people as input. We can also create new styles by adding data.
In-house Application
We create various tools that can help with game development and distribute them throughout KRAFTON. In this process, we aim to discover the potential to bring innovation to the gaming industry as a whole.