3 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…