7 papers
MorphGS: Morphology-Adaptive Articulated 3D Motion Transfer from Videos
Taeyeon Kim, Youngju Na, Jumin Lee +3
Transferring articulated motion from monocular videos to rigged 3D characters is challenging due to pose ambiguity in 2D observations and morphological differences between source a…
DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations
Danny Dongyeop Han, Yonghyeon Gwon, Ahhyun Lucy Lee +10
Intracranial EEG (iEEG) provides direct, millisecond-scale recordings of human neural activity, but reusable representation learning is difficult because electrode layouts, anatomi…
CLaD: Planning with Grounded Foresight via Cross-Modal Latent Dynamics
Andrew Jeong, Jaemin Kim, Sebin Lee +1
Robotic manipulation involves kinematic and semantic transitions that are inherently coupled via underlying actions. However, existing approaches plan within either semantic or lat…
Visual-RRT: Finding Paths toward Visual-Goals via Differentiable Rendering
Sebin Lee, Jumin Lee, Taeyeon Kim +3
Rapidly-exploring random trees (RRTs) have been widely adopted for robot motion planning due to their robustness and theoretical guarantees. However, existing RRT-based planners re…
Beyond the Patch: Exploring Vulnerabilities of Visuomotor Policies via Viewpoint-Consistent 3D Adversarial Object
Chanmi Lee, Minsung Yoon, Woojae Kim +2
Neural network-based visuomotor policies enable robots to perform manipulation tasks but remain susceptible to perceptual attacks. For example, conventional 2D adversarial patches…
Hybrid Quantum Temporal Convolutional Networks
Junghoon Justin Park, Maria Pak, Sebin Lee +4
Quantum machine learning models for sequential data face scalability challenges with complex multivariate signals. We introduce the Hybrid Quantum Temporal Convolutional Network (H…