17 citations · 29 across the 12 of their papers we have counts for
13 papers
Learned Image Compression for Vision-Language-Action Models
Hyeonjun Kim, Jegwang Ryu, Sangbeom Ha +4
Vision-language-action (VLA) models increasingly rely on high-frequency multi-camera observations, making visual communication a major bottleneck for real-time robotic control in b…
A Self-Supervised Approach on Motion Calibration for Enhancing Physical Plausibility in Text-to-Motion
Gahyeon Shim, Soogeun Park, Hyemin Ahn
Generating semantically aligned human motion from textual descriptions has made rapid progress, but ensuring both semantic and physical realism in motion remains a challenge. In th…
Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangement
Hogun Kee, Wooseok Oh, Minjae Kang +2
In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D came…
A Unified Masked Autoencoder with Patchified Skeletons for Motion Synthesis
Esteve Valls Mascaro, Hyemin Ahn, Dongheui Lee
The synthesis of human motion has traditionally been addressed through task-dependent models that focus on specific challenges, such as predicting future motions or filling in inte…
Human-Object Interaction Prediction in Videos through Gaze Following
Zhifan Ni, Esteve Valls Mascaró, Hyemin Ahn +1
Understanding the human-object interactions (HOIs) from a video is essential to fully comprehend a visual scene. This line of research has been addressed by detecting HOIs from ima…
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?
Hyemin Ahn, Esteve Valls Mascaro, Dongheui Lee
After many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days. In this paper, our…
Robust Human Motion Forecasting using Transformer-based Model
Esteve Valls Mascaro, Shuo Ma, Hyemin Ahn +1
Comprehending human motion is a fundamental challenge for developing Human-Robot Collaborative applications. Computer vision researchers have addressed this field by only focusing…
Intention-Conditioned Long-Term Human Egocentric Action Forecasting
Esteve Valls Mascaro, Hyemin Ahn, Dongheui Lee
To anticipate how a human would act in the future, it is essential to understand the human intention since it guides the human towards a certain goal. In this paper, we propose a h…
Self-Supervised Motion Retargeting with Safety Guarantee
Sungjoon Choi, Min Jae Song, Hyemin Ahn +1
In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots…
Visually Grounding Language Instruction for History-Dependent Manipulation
Hyemin Ahn, Obin Kwon, Kyoungdo Kim +5
This paper emphasizes the importance of a robot's ability to refer to its task history, especially when it executes a series of pick-and-place manipulations by following language i…
Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music
Hyemin Ahn, Jaehun Kim, Kihyun Kim +1
This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components:…
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
Hyemin Ahn, Sungjoon Choi, Nuri Kim +2
In this paper, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration which enables an effective interaction with a human user despite the ambiguity in…
Text2Action: Generative Adversarial Synthesis from Language to Action
Hyemin Ahn, Timothy Ha, Yunho Choi +2
In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describin…