no code implementations • 14 Mar 2024 • Chaoyang Wang, Xiangtai Li, Henghui Ding, Lu Qi, Jiangning Zhang, Yunhai Tong, Chen Change Loy, Shuicheng Yan
In-context segmentation has drawn more attention with the introduction of vision foundation models.
no code implementations • 1 Feb 2024 • Guocheng Qian, Junli Cao, Aliaksandr Siarohin, Yash Kant, Chaoyang Wang, Michael Vasilkovsky, Hsin-Ying Lee, Yuwei Fang, Ivan Skorokhodov, Peiye Zhuang, Igor Gilitschenski, Jian Ren, Bernard Ghanem, Kfir Aberman, Sergey Tulyakov
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously.
no code implementations • 10 Jan 2024 • Chaoyang Wang, Peiye Zhuang, Aliaksandr Siarohin, Junli Cao, Guocheng Qian, Hsin-Ying Lee, Sergey Tulyakov
Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos.
1 code implementation • 10 Jan 2024 • Zhiqiang Guo, GuoHui Li, Jianjun Li, Chaoyang Wang, Si Shi
To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data.
1 code implementation • 27 Dec 2023 • Zhiqiang Guo, Jianjun Li, GuoHui Li, Chaoyang Wang, Si Shi, Bin Ruan
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e. g., purchases, clicks) and item various modalities (e. g., visual and textual).
no code implementations • 21 Dec 2023 • Yen-Chi Cheng, Chieh Hubert Lin, Chaoyang Wang, Yash Kant, Sergey Tulyakov, Alexander Schwing, LiangYan Gui, Hsin-Ying Lee
Toward unlocking the potential of generative models in immersive 4D experiences, we introduce Virtual Pet, a novel pipeline to model realistic and diverse motions for target animal species within a 3D environment.
no code implementations • 13 Dec 2023 • Qihang Zhang, Chaoyang Wang, Aliaksandr Siarohin, Peiye Zhuang, Yinghao Xu, Ceyuan Yang, Dahua Lin, Bolei Zhou, Sergey Tulyakov, Hsin-Ying Lee
We are witnessing significant breakthroughs in the technology for generating 3D objects from text.
no code implementations • 9 Oct 2023 • Francesco Dalla Serra, Chaoyang Wang, Fani Deligianni, Jeffrey Dalton, Alison Q O'Neil
Previous approaches to automated radiology reporting generally do not provide the prior study as input, precluding comparison which is required for clinical accuracy in some types of scans, and offer only unreliable methods of interpretability.
no code implementations • 30 Aug 2023 • Francesco Dalla Serra, Chaoyang Wang, Fani Deligianni, Jeffrey Dalton, Alison Q. O'Neil
Automated approaches to radiology reporting require the image to be encoded into a suitable token representation for input to the language model.
1 code implementation • NeurIPS 2023 • Evangelos Ntavelis, Aliaksandr Siarohin, Kyle Olszewski, Chaoyang Wang, Luc van Gool, Sergey Tulyakov
We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core.
2 code implementations • CVPR 2023 • Gengshan Yang, Chaoyang Wang, N Dinesh Reddy, Deva Ramanan
Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories.
3D Shape Reconstruction from Videos Dynamic Reconstruction +1
no code implementations • CVPR 2023 • Chaoyang Wang, Lachlan Ewen MacDonald, Laszlo A. Jeni, Simon Lucey
In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision.
1 code implementation • 19 Jan 2023 • Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O'Neil
Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.
1 code implementation • CIKM 2022 • GuoHui Li, Zhiqiang Guo, Jianjun Li, Chaoyang Wang
Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features.
2 code implementations • 4 Oct 2022 • Mosam Dabhi, Chaoyang Wang, Tim Clifford, Laszlo Attila Jeni, Ian R. Fasel, Simon Lucey
Our Multi-view Bootstrapping in the Wild (MBW) approach demonstrates impressive results on standard human datasets, as well as tigers, cheetahs, fish, colobus monkeys, chimpanzees, and flamingos from videos captured casually in a zoo.
3D Reconstruction Semi-supervised 2D and 3D landmark labeling +1
no code implementations • CVPR 2022 • Chaoyang Wang, Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
Here, we propose a neural trajectory prior to capture continuous spatio-temporal information without the need for offline data.
no code implementations • 22 Oct 2021 • Mosam Dabhi, Chaoyang Wang, Kunal Saluja, Laszlo Jeni, Ian Fasel, Simon Lucey
Multi-view triangulation is the gold standard for 3D reconstruction from 2D correspondences given known calibration and sufficient views.
no code implementations • 12 May 2021 • Chaoyang Wang, Ben Eckart, Simon Lucey, Orazio Gallo
Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes.
no code implementations • CVPR 2021 • Chaoyang Wang, Simon Lucey
Recent success in casting Non-rigid Structure from Motion (NRSfM) as an unsupervised deep learning problem has raised fundamental questions about what novelty in NRSfM prior could the deep learning offer.
1 code implementation • NeurIPS 2020 • Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey
Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the laborious process of creating paired image-shape datasets.
3D Object Reconstruction From A Single Image 3D Reconstruction
1 code implementation • 4 Oct 2020 • Chaoyang Wang, Zhiqiang Guo, GuoHui Li, Jianjun Li, Peng Pan, Ke Liu
Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity.
1 code implementation • 14 Apr 2020 • Chaoyang Wang, Zhiqiang Guo, Jianjun Li, Peng Pan, Guo-Hui Li
IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient.
1 code implementation • 27 Jan 2020 • Chaoyang Wang, Chen-Hsuan Lin, Simon Lucey
The recovery of 3D shape and pose from 2D landmarks stemming from a large ensemble of images can be viewed as a non-rigid structure from motion (NRSfM) problem.
no code implementations • ICCV 2019 • Chaoyang Wang, Chen Kong, Simon Lucey
This alleviates the data bottleneck, which is one of the major concern for supervised methods.
Ranked #21 on Weakly-supervised 3D Human Pose Estimation on Human3.6M
no code implementations • 25 Apr 2019 • Chaoyang Wang, Simon Lucey, Federico Perazzi, Oliver Wang
We present a fully data-driven method to compute depth from diverse monocular video sequences that contain large amounts of non-rigid objects, e. g., people.
no code implementations • 23 Mar 2018 • Nathaniel Chodosh, Chaoyang Wang, Simon Lucey
In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points.
1 code implementation • CVPR 2018 • Chaoyang Wang, Jose Miguel Buenaposada, Rui Zhu, Simon Lucey
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community.
no code implementations • 30 Nov 2017 • Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey
More recently, excellent results have been attained through the application of photometric bundle adjustment (PBA) methods -- which directly minimize the photometric error across frames.
no code implementations • 4 Nov 2017 • Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey
Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision.
no code implementations • ICCV 2017 • Rui Zhu, Hamed Kiani Galoogahi, Chaoyang Wang, Simon Lucey
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image.
no code implementations • 15 Jul 2017 • Rui Zhu, Hamed Kiani Galoogahi, Chaoyang Wang, Simon Lucey
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image.
no code implementations • 19 May 2017 • Chaoyang Wang, Hamed Kiani Galoogahi, Chen-Hsuan Lin, Simon Lucey
In this paper we present a new approach for efficient regression based object tracking which we refer to as Deep- LK.
no code implementations • CVPR 2015 • Chaoyang Wang, Long Zhao, Shuang Liang, Liqing Zhang, Jinyuan Jia, Yichen Wei
Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm.
no code implementations • IEEE International Conference on Consumer Electronics (ICCE) 2013 • Molin Jia, Chaoyang Wang, Kui-Ting Chen, Takaaki Baba
The conventional approach cannot meet the requirement of physiological signal analysis to extract the main component of the acquired signal.