14 citations · 32 across the 12 of their papers we have counts for
12 papers
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction
Qingze, Liu, Danrui Li +4
Accurate prediction of human or vehicle trajectories with good diversity that captures their stochastic nature is an essential task for many applications. However, many trajectory…
From Words to Worlds: Transforming One-line Prompt into Immersive Multi-modal Digital Stories with Communicative LLM Agent
Samuel S. Sohn, Danrui Li, Sen Zhang +2
Digital storytelling, essential in entertainment, education, and marketing, faces challenges in production scalability and flexibility. The StoryAgent framework, introduced in this…
Microscopic modeling of attention-based movement behaviors
Danrui Li, Mathew Schwartz, Samuel S. Sohn +3
For transportation hubs, leveraging pedestrian flows for commercial activities presents an effective strategy for funding maintenance and infrastructure improvements. However, this…
The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents
Che-Jui Chang, Samuel S. Sohn, Sen Zhang +3
Previous studies regarding the perception of emotions for embodied virtual agents have shown the effectiveness of using virtual characters in conveying emotions through interaction…
M3Act: Learning from Synthetic Human Group Activities
Che-Jui Chang, Danrui Li, Deep Patel +7
The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the…
MSI: Maximize Support-Set Information for Few-Shot Segmentation
Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou +4
FSS(Few-shot segmentation) aims to segment a target class using a small number of labeled images(support set). To extract information relevant to the target class, a dominant appro…
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction
Gang Qiao, Kaidong Hu, Seonghyeon Moon +4
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection a…
HM: Hybrid Masking for Few-Shot Segmentation
Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou +4
We study few-shot semantic segmentation that aims to segment a target object from a query image when provided with a few annotated support images of the target class. Several recen…
MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction
Mihee Lee, Samuel S. Sohn, Seonghyeon Moon +3
Accurate long-term trajectory prediction in complex scenes, where multiple agents (e.g., pedestrians or vehicles) interact with each other and the environment while attempting to a…
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Honglu Zhou, Advith Chegu, Samuel S. Sohn +3
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed…
Deep Crowd-Flow Prediction in Built Environments
Samuel S. Sohn, Seonghyeon Moon, Honglu Zhou +3
Predicting the behavior of crowds in complex environments is a key requirement in a multitude of application areas, including crowd and disaster management, architectural design, a…
Cognitive Agent Based Simulation Model For Improving Disaster Response Procedures
Rohit K. Dubey, Samuel S. Sohn, Christoph Hoelscher +1
In the event of a disaster, saving human lives is of utmost importance. For developing proper evacuation procedures and guidance systems, behavioural data on how people respond dur…