18 citations · 65 across the 16 of their papers we have counts for
16 papers
Text-guided Controllable Mesh Refinement for Interactive 3D Modeling
Yun-Chun Chen, Selena Ling, Zhiqin Chen +3
We propose a novel technique for adding geometric details to an input coarse 3D mesh guided by a text prompt. Our method is composed of three stages. First, we generate a single-vi…
PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution
Shulei Ni, Yisheng Qiu, Yunchun Chen +4
In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task. However, deconvolution presents a classical and…
Neural Progressive Meshes
Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman +1
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e.g., 3D meshes, over the Inter…
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
Silvia Sellán, Yun-Chun Chen, Ziyi Wu +2
We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fract…
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands
Dylan Turpin, Liquan Wang, Eric Heiden +5
The study of hand-object interaction requires generating viable grasp poses for high-dimensional multi-finger models, often relying on analytic grasp synthesis which tends to produ…
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
Yun-Chun Chen, Adithyavairavan Murali, Balakumar Sundaralingam +3
The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, plann…
Self-Attentive 3D Human Pose and Shape Estimation from Videos
Yun-Chun Chen, Marco Piccirilli, Robinson Piramuthu +1
We consider the task of estimating 3D human pose and shape from videos. While existing frame-based approaches have made significant progress, these methods are independently applie…
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos
Haoyu Xiong, Quanzhou Li, Yun-Chun Chen +3
Learning from visual data opens the potential to accrue a large range of manipulation behaviors by leveraging human demonstrations without specifying each of them mathematically, b…
Learning to Learn in a Semi-Supervised Fashion
Yun-Chun Chen, Chao-Te Chou, Yu-Chiang Frank Wang
To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share di…
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Yun-Chun Chen, Po-Hsiang Huang, Li-Yu Yu +3
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intr…
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin +1
Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismat…
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang +1
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do no…
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification
Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin +2
Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mism…
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Yun-Chun Chen, Yu-Jhe Li, Xiaofei Du +1
Person re-identification (re-ID) solves the task of matching images across cameras and is among the research topics in vision community. Since query images in real-world scenarios…
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang +1
We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the t…
Deep Learning for Malicious Flow Detection
Yun-Chun Chen, Yu-Jhe Li, Aragorn Tseng +1
Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approa…