3 citations · 11 across the 16 of their papers we have counts for
13 papers · 1 filter
Odoriko: A Shape-Aware Multimodal Diffusion Framework for Human Motion
Dongseok Shim, Julian Tanke, Kengo Uchida +5
Human motion generation has been widely studied across diverse input modalities, text, music, and video, and recent efforts have unified these into single multimodal frameworks. Ho…
PTC-Depth: Pose-Refined Monocular Depth Estimation with Temporal Consistency
Leezy Han, Seunggyu Kim, Dongseok Shim +1
Monocular depth estimation (MDE) has been widely adopted in the perception systems of autonomous vehicles and mobile robots. However, existing approaches often struggle to maintain…
Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models
Christian Simon, Masato Ishii, Wei-Yao Wang +8
Scaling multimodal alignment between video and audio is challenging, particularly due to limited data and the mismatch between text descriptions and frame-level video information.…
MVLight: Relightable Text-to-3D Generation via Light-conditioned Multi-View Diffusion
Dongseok Shim, Yichun Shi, Kejie Li +2
Recent advancements in text-to-3D generation, building on the success of high-performance text-to-image generative models, have made it possible to create imaginative and richly te…
EUGens: Efficient, Unified, and General Dense Layers
Sang Min Kim, Byeongchan Kim, Arijit Sehanobish +7
Efficient neural networks are essential for scaling machine learning models to real-time applications and resource-constrained environments. Fully-connected feedforward layers (FFL…
Object Remover Performance Evaluation Methods using Class-wise Object Removal Images
Changsuk Oh, Dongseok Shim, Taekbeom Lee +1
Object removal refers to the process of erasing designated objects from an image while preserving the overall appearance, and it is one area where image inpainting is widely used i…
AURA : Automatic Mask Generator using Randomized Input Sampling for Object Removal
Changsuk Oh, H. Jin Kim
The objective of the image inpainting task is to fill missing regions of an image in a visually plausible way. Recently, deep-learning-based image inpainting networks have generate…
SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network
Dongseok Shim, H. Jin Kim
Monocular depth estimation plays a critical role in various computer vision and robotics applications such as localization, mapping, and 3D object detection. Recently, learning-bas…
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model
Jeongjun Choi, Dongseok Shim, H. Jin Kim
Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D uplifting approaches have achieved remarkable improvements. Still, monocul…
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow
Ki-Ung Song, Dongseok Shim, Kang-wook Kim +2
Super-resolution suffers from an innate ill-posed problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images. Recent studies on the flow-based…
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss
Dongseok Shim, H. Jin Kim
Deep neural networks have been widely studied in autonomous driving applications such as semantic segmentation or depth estimation. However, training a neural network in a supervis…
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning
Dongseok Shim, H. Jin Kim
Previous studies on image classification have mainly focused on the performance of the networks, not on real-time operation or model compression. We propose a Gaussian Deep Recurre…
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss
Dongseok Shim, H. Jin Kim
Estimating a depth map from a single RGB image has been investigated widely for localization, mapping, and 3-dimensional object detection. Recent studies on a single-view depth est…