Search Results for author: Junuk Cha

Found 6 papers, 2 papers with code

Text2HOI: Text-guided 3D Motion Generation for Hand-Object Interaction

1 code implementation31 Mar 2024 Junuk Cha, Jihyeon Kim, Jae Shin Yoon, Seungryul Baek

For contact generation, a VAE-based network takes as input a text and an object mesh, and generates the probability of contacts between the surfaces of hands and the object during the interaction.

Object

VLM-PL: Advanced Pseudo Labeling approach Class Incremental Object Detection with Vision-Language Model

no code implementations8 Mar 2024 Junsu Kim, Yunhoe Ku, Jihyeon Kim, Junuk Cha, Seungryul Baek

This technique uses Vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training.

Class-Incremental Object Detection Incremental Learning +3

3D Reconstruction of Interacting Multi-Person in Clothing from a Single Image

no code implementations12 Jan 2024 Junuk Cha, Hansol Lee, Jaewon Kim, Nhat Nguyen Bao Truong, Jae Shin Yoon, Seungryul Baek

This paper introduces a novel pipeline to reconstruct the geometry of interacting multi-person in clothing on a globally coherent scene space from a single image.

3D Reconstruction

Dynamic Appearance Modeling of Clothed 3D Human Avatars using a Single Camera

no code implementations28 Dec 2023 Hansol Lee, Junuk Cha, Yunhoe Ku, Jae Shin Yoon, Seungryul Baek

For implicit modeling, an implicit network combines the appearance and 3D motion features to decode high-fidelity clothed 3D human avatars with motion-dependent geometry and texture.

HOReeNet: 3D-aware Hand-Object Grasping Reenactment

no code implementations11 Nov 2022 Changhwa Lee, Junuk Cha, Hansol Lee, Seongyeong Lee, Donguk Kim, Seungryul Baek

At the same time, to obtain high-quality 2D images from 3D space, well-designed 3D-to-2D projection and image refinement are required.

3D Reconstruction Object

Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement

1 code implementation24 Oct 2022 Junuk Cha, Muhammad Saqlain, GeonU Kim, Mingyu Shin, Seungryul Baek

To tackle the challenges, we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics from the occlusion-robust 3D skeleton estimation and 2) Transformer-based relation-aware refinement techniques.

 Ranked #1 on 3D Multi-Person Pose Estimation on MuPoTS-3D (using extra training data)

3D Multi-Person Pose Estimation

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