18 citations · 19 across the 10 of their papers we have counts for
8 papers · 1 filter
EggHand: A Multimodal Foundation Model for Egocentric Hand Pose Forecasting
Jaeyoung Choi, Hyeondong Kim, Yujin Kim +1
Forecasting future 3D hand pose sequences from egocentric video is essential for understanding human intention and enabling embodied applications such as AR/VR assistance and human…
Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model
Daehee Park, Monu Surana, Pranav Desai +3
While data-driven trajectory prediction has enhanced the reliability of autonomous driving systems, it still struggles with rarely observed long-tail scenarios. Prior works address…
Multi-modal Knowledge Distillation-based Human Trajectory Forecasting
Jaewoo Jeong, Seohee Lee, Daehee Park +2
Pedestrian trajectory forecasting is crucial in various applications such as autonomous driving and mobile robot navigation. In such applications, camera-based perception enables t…
Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning
Jaewoo Jeong, Daehee Park, Kuk-Jin Yoon
Human pose forecasting garners attention for its diverse applications. However, challenges in modeling the multi-modal nature of human motion and intricate interactions among agent…
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Daehee Park, Jaeseok Jeong, Sung-Hoon Yoon +2
Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been…
Improving Transferability for Cross-domain Trajectory Prediction via Neural Stochastic Differential Equation
Daehee Park, Jaewoo Jeong, Kuk-Jin Yoon
Multi-agent trajectory prediction is crucial for various practical applications, spurring the construction of many large-scale trajectory datasets, including vehicles and pedestria…
Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
Daehee Park, Hobin Ryu, Yunseo Yang +3
Understanding the interaction between multiple agents is crucial for realistic vehicle trajectory prediction. Existing methods have attempted to infer the interaction from the obse…
BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning
Changgyoon Oh, Wonjune Cho, Daehee Park +3
Providing omnidirectional depth along with RGB information is important for numerous applications, eg, VR/AR. However, as omnidirectional RGB-D data is not always available, synthe…