activity
20222024
most citedLearning Pedestrian Group Representations for Multi-modal Trajectory Prediction

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Kinetic Typography Diffusion Model

Seonmi Park, Inhwan Bae, Seunghyun Shin +1

This paper introduces a method for realistic kinetic typography that generates user-preferred animatable 'text content'. We draw on recent advances in guided video diffusion models…

cs.CV2024

SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model

Inhwan Bae, Young-Jae Park, Hae-Gon Jeon

There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These associated tasks are defined by variou…

cs.CL2024

Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction

Inhwan Bae, Junoh Lee, Hae-Gon Jeon

Language models have demonstrated impressive ability in context understanding and generative performance. Inspired by the recent success of language foundation models, in this pape…

cs.CV2023

EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting

Inhwan Bae, Jean Oh, Hae-Gon Jeon

Capturing high-dimensional social interactions and feasible futures is essential for predicting trajectories. To address this complex nature, several attempts have been devoted to…

cs.CV20221 cited

Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction

Inhwan Bae, Jin-Hwi Park, Hae-Gon Jeon

Modeling the dynamics of people walking is a problem of long-standing interest in computer vision. Many previous works involving pedestrian trajectory prediction define a particula…