24 citations · 65 across the 13 of their papers we have counts for
14 papers
FlowVLA: Enabling World Dynamics Understanding through Pluggable 3D Flow Learning
Junhyeong Go, Mingyu Jung, Woobin Im +1
Recent advances in 3D reconstruction enable the extraction of temporally consistent 3D motion from RGB observations; however, current Vision-Language-Action (VLA) models still pred…
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…
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…
Fine-grained Background Representation for Weakly Supervised Semantic Segmentation
Xu Yin, Woobin Im, Dongbo Min +3
Generating reliable pseudo masks from image-level labels is challenging in the weakly supervised semantic segmentation (WSSS) task due to the lack of spatial information. Prevalent…
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…