논문

강화학습, PCG, 멀티모달 표현, 인체 감지 분야 논문 6편.

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프리프린트 2025 · arXiv

Shared Representation for 3D Pose Estimation, Action Classification, and Progress Prediction from Tactile Signals

Isaac Han , Seoyoung Lee , Sangyeon Park , Ecehan Akan , Yiyue Luo , Jeffrey DelPreto , Kyung-Joong Kim

SCOTTI (Shared COnvolutional Transformer for Tactile Inference) simultaneously addresses three tasks from foot tactile signals: 3D human pose estimation, action class categorization, and action completion progress estimation. This is the first work to explore action progress prediction using foot tactile signals.

인간 자세 추정 촉각 센싱 멀티태스크 학습 트랜스포머 행동 인식
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심사 중 2025 · IEEE Conference on Games (CoG 2026)

Multi-Objective Instruction-Aware Representation Learning in PCGRL

Sung-Hyun Kim , Geumhwan Hwang , In-Chang Baek , Seo-Young Lee , Kyung-Joong Kim

MIPCGRL proposes a multi-objective representation learning method for language-instructed PCGRL. MIPCGRL introduces a task-specific encoder trained with multi-label classification and multi-head regression to disentangle task representations, achieving up to 13.8% improvement in controllability over IPCGRL on multi-objective instructions.

절차적 콘텐츠 생성 강화학습 다목적 최적화 표현 학습
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심사 중 2025 · IEEE Transactions on Games (ToG)

Human-Aligned Procedural Level Generation RL via Text-Level-Sketch Shared Representation

In-Chang Baek* , Seo-Young Lee* , Sung-Hyun Kim , Geumhwan Hwang , Kyung-Joong Kim

Human-aligned AI is a critical component of co-creativity. This paper proposes VIPCGRL (Vision-Instruction PCGRL), a novel deep RL framework that incorporates three modalities — text, level, and sketches — to extend control modality and enhance human-likeness in procedural content generation. A shared embedding space is trained via quadruple contrastive learning across modalities and human-AI styles. The policy is aligned using an auxiliary reward based on embedding similarity. Experimental results show VIPCGRL outperforms existing baselines in human-likeness and demonstrates zero-shot cross-modal generalization.

절차적 콘텐츠 생성 강화학습 멀티모달 표현 학습 인간-AI 정렬 대조 학습
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게재됨 2025 · IEEE Conference on Games (CoG 2025)

IPCGRL: Language-Instructed RL for Procedural Level Generation

In-Chang Baek , Sung-Hyun Kim , Seo-Young Lee , Dong-Hyeon Kim , Kyung-Joong Kim

IPCGRL introduces a language-instructed PCGRL framework that uses sentence embeddings to condition a deep RL agent for procedural level generation. IPCGRL fine-tunes task-specific embedding representations to compress game-level conditions from natural language, achieving up to 21.4% improvement in controllability and 17.2% improvement in generalizability for unseen instructions.

절차적 콘텐츠 생성 강화학습 자연어 처리 명령어 추종
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게재됨 2025 · IEEE Access

Automatic Curriculum Design for Zero-Shot Human-AI Coordination

Won-Sang You , Tae-Gwan Ha , Seo-Young Lee , Kyung-Joong Kim

Zero-shot human-AI coordination trains an ego-agent to coordinate with humans without human data. This paper extends multi-agent UED to zero-shot human-AI coordination by proposing a return-based utility function and prioritized co-player sampling. Evaluated in the Overcooked-AI environment with real humans (N=20), the method outperforms baseline approaches, achieving higher collaborativeness ratings and human preference scores.

인간-AI 협업 강화학습 커리큘럼 학습 제로샷 일반화 멀티 에이전트
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게재됨 2024 · NeurIPS Workshop on Touch Processing: From Data to Knowledge (2024)

Smart Insole: Predicting 3D Human Pose from Foot Pressure

Isaac Han , Seoyoung Lee , Sangyeon Park , Ecehan Akan , Yiyue Luo , Kyung-Joong Kim

This study introduces a novel method of 3D human pose estimation using foot pressure data captured by a low-cost, high-resolution smart insole with over 600 pressure sensors per foot. A deep neural network predicts 3D human poses using only foot pressure data, achieving 7.43 cm average localization error and 96.88% action classification accuracy.

인간 자세 추정 촉각 센싱 발 압력 웨어러블 컴퓨팅 딥러닝
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