no code implementations • 10 Aug 2023 • Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman, Alec Jacobson
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 Internet.
1 code implementation • 20 Oct 2022 • Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, Alec Jacobson
We introduce Breaking Bad, a large-scale dataset of fractured objects.
no code implementations • 29 Jun 2022 • Yun-Chun Chen, Adithyavairavan Murali, Balakumar Sundaralingam, Wei Yang, Animesh Garg, Dieter Fox
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, planning a collision-free trajectory, and then executing the open-loop trajectory to the grasp pose with a low-level tracking controller.
no code implementations • CVPR 2022 • Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg
While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit.
no code implementations • 26 Mar 2021 • Yun-Chun Chen, Marco Piccirilli, Robinson Piramuthu, Ming-Hsuan Yang
The key insights of our method are two-fold.
Ranked #53 on 3D Human Pose Estimation on MPI-INF-3DHP
no code implementations • 18 Jan 2021 • Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg
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, but rather through natural task specification.
1 code implementation • ECCV 2020 • Yun-Chun Chen, Chen Gao, Esther Robb, Jia-Bin Huang
Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior for solving various inverse image restoration tasks.
no code implementations • ECCV 2020 • 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.
no code implementations • 31 Mar 2020 • Yun-Chun Chen, Po-Hsiang Huang, Li-Yu Yu, Jia-Bin Huang, Ming-Hsuan Yang, Yen-Yu Lin
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations.
no code implementations • 19 Feb 2020 • Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin, Yu-Chiang Frank Wang
Person re-identification (re-ID) aims at matching images of the same person across camera views.
no code implementations • CVPR 2019 • Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e. g., synthetic to real images).
no code implementations • ICCV 2019 • Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin, Xiaofei Du, Yu-Chiang Frank Wang
Person re-identification (re-ID) aims at matching images of the same identity across camera views.
no code implementations • 25 Jul 2019 • Yun-Chun Chen, Yu-Jhe Li, Xiaofei Du, Yu-Chiang Frank Wang
Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.
1 code implementation • 13 Jun 2019 • Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang
In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks.
no code implementations • 9 Feb 2018 • Yun-Chun Chen, Yu-Jhe Li, Aragorn Tseng, Tsungnan Lin
We also conduct a partial flow experiment which shows the feasibility of real-time detection and a zero-shot learning experiment which justifies the generalization capability of deep learning in cyber security.