2 citations · 2 across the 4 of their papers we have counts for
6 papers
CrowdVLA: Embodied Vision-Language-Action Agents for Context-Aware Crowd Simulation
Juyeong Hwang, Seong-Eun Hong, Jinhyun Kim +4
Crowds do not merely move; they decide. Human navigation is inherently contextual: people interpret the meaning of space, social norms, and potential consequences before acting. Si…
Edit-As-Act: Goal-Regressive Planning for Open-Vocabulary 3D Indoor Scene Editing
Seongrae Noh, SeungWon Seo, Gyeong-Moon Park +1
Editing a 3D indoor scene from natural language is conceptually straightforward but technically challenging. Existing open-vocabulary systems often regenerate large portions of a s…
Event-T2M: Event-level Conditioning for Complex Text-to-Motion Synthesis
Seong-Eun Hong, JaeYoung Seon, JuYeong Hwang +2
Text-to-motion generation has advanced with diffusion models, yet existing systems often collapse complex multi-action prompts into a single embedding, leading to omissions, reorde…
BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis
Seong-Eun Hong, Soobin Lim, Juyeong Hwang +2
Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics and capturing nuanced motion patte…
How Does a Virtual Agent Decide Where to Look? Symbolic Cognitive Reasoning for Embodied Head Rotation
Juyeong Hwang, Seong-Eun Hong, JaeYoung Seon +1
Natural head rotation is critical for believable embodied virtual agents, yet this micro-level behavior remains largely underexplored. While head-rotation prediction algorithms cou…
ViRAC: A Vision-Reasoning Agent Head Movement Control Framework in Arbitrary Virtual Environments
Juyeong Hwang, Seong-Eun Hong, Hyeongyeop Kang
Creating lifelike virtual agents capable of interacting with their environments is a longstanding goal in computer graphics. This paper addresses the challenge of generating natura…