arXiv subjects

Seong-Eun Hong

Publications and source records attributed to Seong-Eun Hong.

6 recordsLinked to original sources

arXiv2026

CrowdVLA: Embodied Vision-Language-Action Agents for Context-Aware Crowd Simulation

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. Sidewalks invite walking, crosswalks invite crossing, and deviations are weighed against urgency and safety. Yet most crowd simulation methods reduce navigation to geometry and collision avoidance, producing motion that is plausible but rarely intentional. We introduce CrowdVLA, a new formulation of crowd simulation that models each pedestrian as a Vision-Language-Action (VLA) agent. Instead of replaying recorded trajectories, CrowdVLA enables agents to interpret scene semantics and social norms from visual observations and language instructions, and to select actions through consequence-aware reasoning. CrowdVLA addresses three key challenges-limited agent-centric supervision in crowd datasets, unstable per-frame control, and success-biased datasets-through: (i) agent-centric visual supervision via semantically reconstructed environments and Low-Rank Adaptation (LoRA) fine-tuning of a pretrained vision-language model, (ii) a motion skill action space that bridges symbolic decision making and continuous locomotion, and (iii) exploration-based question answering that exposes agents to counterfactual actions and their outcomes through simulation rollouts. Our results shift crowd simulation from motion-centric synthesis toward perception-driven, consequence-aware decision making, enabling crowds that move not just realistically, but meaningfully.

cs.GR↗

arXiv2026

ORACLE: Orchestrate NPC Daily Activities using Contrastive Learning with Transformer-CVAE

The integration of Non-player characters (NPCs) within digital environments has been increasingly recognized for its potential to augment user immersion and cognitive engagement. The sophisticated orchestration of their daily activities, reflecting the nuances of human daily routines, contributes significantly to the realism of digital environments. Nevertheless, conventional approaches often produce monotonous repetition, falling short of capturing the intricacies of real human activity plans. In response to this, we introduce ORACLE, a novel generative model for the synthesis of realistic indoor daily activity plans, ensuring NPCs' authentic presence in digital habitats. Exploiting the CASAS smart home dataset's 24-hour indoor activity sequences, ORACLE addresses challenges in the dataset, including its imbalanced sequential data, the scarcity of training samples, and the absence of pre-trained models encapsulating human daily activity patterns. ORACLE's training leverages the sequential data processing prowess of Transformers, the generative controllability of Conditional Variational Autoencoders (CVAE), and the discriminative refinement of contrastive learning. Our experimental results validate the superiority of generating NPC activity plans and the efficacy of our design strategies over existing methods.

cs.GR↗

arXiv2026

Event-T2M: Event-level Conditioning for Complex Text-to-Motion Synthesis

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, reordering, or unnatural transitions. In this work, we shift perspective by introducing a principled definition of an event as the smallest semantically self-contained action or state change in a text prompt that can be temporally aligned with a motion segment. Building on this definition, we propose Event-T2M, a diffusion-based framework that decomposes prompts into events, encodes each with a motion-aware retrieval model, and integrates them through event-based cross-attention in Conformer blocks. Existing benchmarks mix simple and multi-event prompts, making it unclear whether models that succeed on single actions generalize to multi-action cases. To address this, we construct HumanML3D-E, the first benchmark stratified by event count. Experiments on HumanML3D, KIT-ML, and HumanML3D-E show that Event-T2M matches state-of-the-art baselines on standard tests while outperforming them as event complexity increases. Human studies validate the plausibility of our event definition, the reliability of HumanML3D-E, and the superiority of Event-T2M in generating multi-event motions that preserve order and naturalness close to ground-truth. These results establish event-level conditioning as a generalizable principle for advancing text-to-motion generation beyond single-action prompts.

cs.GR↗

arXiv2026

BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis

Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics and capturing nuanced motion patterns over extended sequences that accurately reflect the given text. To address this, we introduce BiPO, Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis, a novel model that enhances text-to-motion synthesis by integrating part-based generation with a bidirectional autoregressive architecture. This integration allows BiPO to consider both past and future contexts during generation while enhancing detailed control over individual body parts without requiring ground-truth motion length. To relax the interdependency among body parts caused by the integration, we devise the Partial Occlusion technique, which probabilistically occludes the certain motion part information during training. In our comprehensive experiments, BiPO achieves state-of-the-art performance on the HumanML3D dataset, outperforming recent methods such as ParCo, MoMask, and BAMM in terms of FID scores and overall motion quality. Notably, BiPO excels not only in the text-to-motion generation task but also in motion editing tasks that synthesize motion based on partially generated motion sequences and textual descriptions. These results reveal the BiPO's effectiveness in advancing text-to-motion synthesis and its potential for practical applications.

cs.CV↗

arXiv2026

How Does a Virtual Agent Decide Where to Look? Symbolic Cognitive Reasoning for Embodied Head Rotation

Natural head rotation is critical for believable embodied virtual agents, yet this micro-level behavior remains largely underexplored. While head-rotation prediction algorithms could, in principle, reproduce this behavior, they typically focus on visually salient stimuli and overlook the cognitive motives that guide head rotation. This yields agents that look at conspicuous objects while overlooking obstacles or task-relevant cues, diminishing realism in a virtual environment. We introduce SCORE, a Symbolic Cognitive Reasoning framework for Embodied Head Rotation, a data-agnostic framework that produces context-aware head movements without task-specific training or hand-tuned heuristics. A controlled VR study (N=20) identifies five motivational drivers of human head movements: Interest, Information Seeking, Safety, Social Schema, and Habit. SCORE encodes these drivers as symbolic predicates, perceives the scene with a Vision-Language Model (VLM), and plans head poses with a Large Language Model (LLM). The framework employs a hybrid workflow: the VLM-LLM reasoning is executed offline, after which a lightweight FastVLM performs online validation to suppress hallucinations while maintaining responsiveness to scene dynamics. The result is an agent that predicts not only where to look but also why, generalizing to unseen scenes and multi-agent crowds while retaining behavioral plausibility.

cs.GR↗

arXiv2025

ViRAC: A Vision-Reasoning Agent Head Movement Control Framework in Arbitrary Virtual Environments

Creating lifelike virtual agents capable of interacting with their environments is a longstanding goal in computer graphics. This paper addresses the challenge of generating natural head rotations, a critical aspect of believable agent behavior for visual information gathering and dynamic responses to environmental cues. Although earlier methods have made significant strides, many rely on data-driven or saliency-based approaches, which often underperform in diverse settings and fail to capture deeper cognitive factors such as risk assessment, information seeking, and contextual prioritization. Consequently, generated behaviors can appear rigid or overlook critical scene elements, thereby diminishing the sense of realism. In this paper, we propose \textbf{ViRAC}, a \textbf{Vi}sion-\textbf{R}easoning \textbf{A}gent Head Movement \textbf{C}ontrol framework, which exploits the common-sense knowledge and reasoning capabilities of large-scale models, including Vision-Language Models (VLMs) and Large-Language Models (LLMs). Rather than explicitly modeling every cognitive mechanism, ViRAC leverages the biases and patterns internalized by these models from extensive training, thus emulating human-like perceptual processes without hand-tuned heuristics. Experimental results in multiple scenarios reveal that ViRAC produces more natural and context-aware head rotations than recent state-of-the-art techniques. Quantitative evaluations show a closer alignment with real human head-movement data, while user studies confirm improved realism and cognitive plausibility.

cs.GR↗