3 citations · 3 across the 12 of their papers we have counts for
13 papers
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…
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…
DIVER-0 : A Fully Channel Equivariant EEG Foundation Model
Danny Dongyeop Han, Ahhyun Lucy Lee, Taeyang Lee +7
Electroencephalography (EEG) is a non-invasive technique widely used in brain-computer interfaces and clinical applications, yet existing EEG foundation models face limitations in…
Regularizing Dynamic Radiance Fields with Kinematic Fields
Woobin Im, Geonho Cha, Sebin Lee +4
This paper presents a novel approach for reconstructing dynamic radiance fields from monocular videos. We integrate kinematics with dynamic radiance fields, bridging the gap betwee…
Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation
Juhyeong Seon, Woobin Im, Sebin Lee +2
Audio-visual segmentation (AVS) aims to segment sound sources in the video sequence, requiring a pixel-level understanding of audio-visual correspondence. As the Segment Anything M…
SemCity: Semantic Scene Generation with Triplane Diffusion
Jumin Lee, Sebin Lee, Changho Jo +3
We present "SemCity," a 3D diffusion model for semantic scene generation in real-world outdoor environments. Most 3D diffusion models focus on generating a single object, synthetic…
Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data
Jumin Lee, Woobin Im, Sebin Lee +1
In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion res…
Semi-Supervised Learning of Optical Flow by Flow Supervisor
Woobin Im, Sebin Lee, Sung-Eui Yoon
A training pipeline for optical flow CNNs consists of a pretraining stage on a synthetic dataset followed by a fine tuning stage on a target dataset. However, obtaining ground trut…
Dynamic Humanoid Locomotion over Uneven Terrain With Streamlined Perception-Control Pipeline
Moonyoung Lee, Youngsun Kwon, Sebin Lee +8
Although bipedal locomotion provides the ability to traverse unstructured environments, it requires careful planning and control to safely walk across without falling. This poses a…