1 code implementation • 8 Apr 2024 • Fengrui Tian, Yueqi Duan, Angtian Wang, Jianfei Guo, Shaoyi Du
As there is 2D-to-3D ambiguity problem in the viewing direction when extracting 3D flow features from 2D video frames, we consider the volume densities as opacity priors that describe the contributions of flow features to the semantics on the frames.
no code implementations • 8 Apr 2024 • Fengrui Tian, Yaoyao Liu, Adam Kortylewski, Yueqi Duan, Shaoyi Du, Alan Yuille, Angtian Wang
Instead of using manually annotated images, we leverage diffusion models (e. g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images.
1 code implementation • 11 Mar 2024 • Yuxiang Lai, Xiaoxi Chen, Angtian Wang, Alan Yuille, Zongwei Zhou
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors.
1 code implementation • 7 Mar 2024 • Yuanhao Cai, Yixun Liang, Jiahao Wang, Angtian Wang, Yulun Zhang, Xiaokang Yang, Zongwei Zhou, Alan Yuille
X-ray is widely applied for transmission imaging due to its stronger penetration than natural light.
no code implementations • 28 Dec 2023 • Angtian Wang, Yuanlu Xu, Nikolaos Sarafianos, Robert Maier, Edmond Boyer, Alan Yuille, Tony Tung
This representation is composed of two surface layers that represent opaque and translucent regions on the clothed human body.
1 code implementation • 18 Nov 2023 • Yuanhao Cai, Jiahao Wang, Alan Yuille, Zongwei Zhou, Angtian Wang
In this paper, we propose a framework, Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF), for sparse-view X-ray 3D reconstruction.
Ranked #1 on Low-Dose X-Ray Ct Reconstruction on X3D
1 code implementation • ICCV 2023 • Yi Zhang, Pengliang Ji, Angtian Wang, Jieru Mei, Adam Kortylewski, Alan Yuille
Motivated by the recent success of generative models in rigid object pose estimation, we propose 3D-aware Neural Body Fitting (3DNBF) - an approximate analysis-by-synthesis approach to 3D human pose estimation with SOTA performance and occlusion robustness.
no code implementations • 13 Jun 2023 • Wufei Ma, Qihao Liu, Jiahao Wang, Angtian Wang, Xiaoding Yuan, Yi Zhang, Zihao Xiao, Guofeng Zhang, Beijia Lu, Ruxiao Duan, Yongrui Qi, Adam Kortylewski, Yaoyao Liu, Alan Yuille
With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically.
no code implementations • 31 May 2023 • Angtian Wang, Wufei Ma, Alan Yuille, Adam Kortylewski
Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios.
no code implementations • 25 May 2023 • Jiahao Yang, Wufei Ma, Angtian Wang, Xiaoding Yuan, Alan Yuille, Adam Kortylewski
In this work, we aim to narrow the performance gap between models trained on synthetic data and few real images and fully supervised models trained on large-scale data.
no code implementations • 24 May 2023 • Artur Jesslen, Guofeng Zhang, Angtian Wang, Alan Yuille, Adam Kortylewski
Using differentiable rendering, we estimate the 3D object pose by minimizing the reconstruction error between the mesh and the feature representation of the target image.
no code implementations • 10 Jan 2023 • Chen Wang, Angtian Wang, Junbo Li, Alan Yuille, Cihang Xie
We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models.
1 code implementation • 12 Sep 2022 • Wufei Ma, Angtian Wang, Alan Yuille, Adam Kortylewski
We consider the problem of category-level 6D pose estimation from a single RGB image.
1 code implementation • 30 May 2022 • Angtian Wang, Peng Wang, Jian Sun, Adam Kortylewski, Alan Yuille
The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation of object 3D geometry from meshes and point clouds.
no code implementations • 29 Nov 2021 • Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski
One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.
1 code implementation • NeurIPS 2021 • Angtian Wang, Shenxiao Mei, Alan Yuille, Adam Kortylewski
The model is initialized from a few labelled images and is subsequently used to synthesize feature representations of unseen 3D views.
1 code implementation • ICLR 2021 • Angtian Wang, Adam Kortylewski, Alan Yuille
Using differentiable rendering we estimate the 3D object pose by minimizing the reconstruction error between NeMo and the feature representation of the target image.
no code implementations • 29 Sep 2020 • Yutong Bai, Angtian Wang, Adam Kortylewski, Alan Yuille
In this paper, we introduce a contrastive learning framework for keypoint detection (CoKe).
no code implementations • 28 Jun 2020 • Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun, Alan Yuille
The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.
no code implementations • CVPR 2020 • Angtian Wang, Yihong Sun, Adam Kortylewski, Alan Yuille
In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.
no code implementations • 3 Sep 2019 • Yuyin Zhou, Yingwei Li, Zhishuai Zhang, Yan Wang, Angtian Wang, Elliot Fishman, Alan Yuille, Seyoun Park
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%.
no code implementations • ECCV 2018 • Peng Tang, Xinggang Wang, Angtian Wang, Yongluan Yan, Wenyu Liu, Junzhou Huang, Alan Yuille
The Convolutional Neural Network (CNN) based region proposal generation method (i. e. region proposal network), trained using bounding box annotations, is an essential component in modern fully supervised object detectors.