5 citations · 16 across the 18 of their papers we have counts for
24 papers
HOIGS: Human-Object Interaction Gaussian Splatting
Taewoo Kim, Suwoong Yeom, Jaehyun Pyun +6
Reconstructing dynamic scenes with complex human-object interactions is a fundamental challenge in computer vision and graphics. Existing Gaussian Splatting methods either rely on…
SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models
Jiwoo Chung, Sangeek Hyun, MinKyu Lee +5
Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by cac…
HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction
Sangmin Kim, Minhyuk Hwang, Geonho Cha +2
Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independently trained human and scene pri…
Video-Oasis: Rethinking Evaluation of Video Understanding
Geuntaek Lim, Sungjune Park, Jaeyun Lee +5
The inherent complexity of video understanding makes it difficult to determine whether Video-LLM benchmark performance stems from visual perception, linguistic reasoning, or knowle…
Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition
Geo Ahn, Inwoong Lee, Taeoh Kim +3
Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key fa…
Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning Segmentation
Su Ho Han, Jeongseok Hyun, Pilhyeon Lee +3
Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to textual queries. To directly adapt this for localization i…
Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs
Jeongseok Hyun, Sukjun Hwang, Su Ho Han +6
Video large language models (LLMs) achieve strong video understanding by leveraging a large number of spatio-temporal tokens, but suffer from quadratic computational scaling with t…
Prototypes are Balanced Units for Efficient and Effective Partially Relevant Video Retrieval
WonJun Moon, Cheol-Ho Cho, Woojin Jun +5
In a retrieval system, simultaneously achieving search accuracy and efficiency is inherently challenging. This challenge is particularly pronounced in partially relevant video retr…
CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images
Jungho Lee, Donghyeong Kim, Dogyoon Lee +6
3D Gaussian Splatting (3DGS) has gained significant attention due to its high-quality novel view rendering, motivating research to address real-world challenges. A critical issue i…
Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from Unsynchronized and Uncalibrated Videos
Changwoon Choi, Jeongjun Kim, Geonho Cha +3
Recent works on dynamic 3D neural field reconstruction assume the input from synchronized multi-view videos whose poses are known. The input constraints are often not satisfied in…
CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images
Jungho Lee, Suhwan Cho, Taeoh Kim +6
3D Gaussian Splatting (3DGS) has attracted significant attention for its high-quality novel view rendering, inspiring research to address real-world challenges. While conventional…
A Simple Baseline with Single-encoder for Referring Image Segmentation
Seonghoon Yu, Ilchae Jung, Byeongju Han +4
Referring image segmentation (RIS) requires dense vision-language interactions between visual pixels and textual words to segment objects based on a given description. However, com…
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…
Motion-Oriented Compositional Neural Radiance Fields for Monocular Dynamic Human Modeling
Jaehyeok Kim, Dongyoon Wee, Dan Xu
This paper introduces Motion-oriented Compositional Neural Radiance Fields (MoCo-NeRF), a framework designed to perform free-viewpoint rendering of monocular human videos via novel…
Classification Matters: Improving Video Action Detection with Class-Specific Attention
Jinsung Lee, Taeoh Kim, Inwoong Lee +4
Video action detection (VAD) aims to detect actors and classify their actions in a video. We figure that VAD suffers more from classification rather than localization of actors. He…
Masked Autoencoder for Unsupervised Video Summarization
Minho Shim, Taeoh Kim, Jinhyung Kim +1
Summarizing a video requires a diverse understanding of the video, ranging from recognizing scenes to evaluating how much each frame is essential enough to be selected as a summary…
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection
Pilhyeon Lee, Taeoh Kim, Minho Shim +2
Temporal action detection aims to predict the time intervals and the classes of action instances in the video. Despite the promising performance, existing two-stream models exhibit…
You Only Train Once: Multi-Identity Free-Viewpoint Neural Human Rendering from Monocular Videos
Jaehyeok Kim, Dongyoon Wee, Dan Xu
We introduce You Only Train Once (YOTO), a dynamic human generation framework, which performs free-viewpoint rendering of different human identities with distinct motions, via only…
MEEV: Body Mesh Estimation On Egocentric Video
Nicolas Monet, Dongyoon Wee
This technical report introduces our solution, MEEV, proposed to the EgoBody Challenge at ECCV 2022. Captured from head-mounted devices, the dataset consists of human body shape an…
Exploring Temporally Dynamic Data Augmentation for Video Recognition
Taeoh Kim, Jinhyung Kim, Minho Shim +4
Data augmentation has recently emerged as an essential component of modern training recipes for visual recognition tasks. However, data augmentation for video recognition has been…
Out of Sight, Out of Mind: A Source-View-Wise Feature Aggregation for Multi-View Image-Based Rendering
Geonho Cha, Chaehun Shin, Sungroh Yoon +1
To estimate the volume density and color of a 3D point in the multi-view image-based rendering, a common approach is to inspect the consensus existence among the given source image…
Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning
Geonho Cha, Ho-Deok Jang, Dongyoon Wee
Managing the dynamic regions in the photometric loss formulation has been a main issue for handling the self-supervised depth estimation problem. Most previous methods have allevia…
Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
Jeongseok Hyun, Myunggu Kang, Dongyoon Wee +1
In existing joint detection and tracking methods, pairwise relational features are used to match previous tracklets to current detections. However, the features may not be discrimi…
Frequency Selective Augmentation for Video Representation Learning
Jinhyung Kim, Taeoh Kim, Minho Shim +3
Recent self-supervised video representation learning methods focus on maximizing the similarity between multiple augmented views from the same video and largely rely on the quality…