no code implementations • 15 Mar 2024 • Jinxia Xie, Bineng Zhong, Zhiyi Mo, Shengping Zhang, Liangtao Shi, Shuxiang Song, Rongrong Ji
Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion.
1 code implementation • 3 Mar 2024 • Qinglin Liu, Shengping Zhang, Quanling Meng, Bineng Zhong, Peiqiang Liu, Hongxun Yao
Finally, an instance matting network decodes the image features and united semantics guidance to predict all instance-level alpha mattes.
1 code implementation • 28 Feb 2024 • Qinglin Liu, Xiaoqian Lv, Wei Yu, Changyong Guo, Shengping Zhang
However, existing matting methods are designed for specific objects or guidance, neglecting the common requirement of aggregating global and local contexts in image matting.
Ranked #3 on Image Matting on Distinctions-646
1 code implementation • 6 Jan 2024 • Liangtao Shi, Bineng Zhong, Qihua Liang, Ning li, Shengping Zhang, Xianxian Li
Specifically, we utilize spatio-temporal tokens to propagate information between consecutive frames without focusing on updating templates.
1 code implementation • 3 Jan 2024 • Yaozong Zheng, Bineng Zhong, Qihua Liang, Zhiyi Mo, Shengping Zhang, Xianxian Li
To alleviate the above problem, we propose a simple, flexible and effective video-level tracking pipeline, named \textbf{ODTrack}, which densely associates the contextual relationships of video frames in an online token propagation manner.
Ranked #1 on Visual Object Tracking on TrackingNet
Semi-Supervised Video Object Segmentation Visual Object Tracking +1
1 code implementation • 14 Dec 2023 • Ru Li, Jia Liu, Guanghui Liu, Shengping Zhang, Bing Zeng, Shuaicheng Liu
We modify the classical spectral rendering into two main steps, 1) the generation of a series of spectrum maps spanning different wavelengths, 2) the combination of these spectrum maps for the RGB output.
no code implementations • 7 Dec 2023 • Yufan Chen, Lizhen Wang, Qijing Li, Hongjiang Xiao, Shengping Zhang, Hongxun Yao, Yebin Liu
In response to these challenges, we propose MonoGaussianAvatar (Monocular Gaussian Point-based Head Avatar), a novel approach that harnesses 3D Gaussian point representation coupled with a Gaussian deformation field to learn explicit head avatars from monocular portrait videos.
1 code implementation • 4 Dec 2023 • Liangxiao Hu, Hongwen Zhang, Yuxiang Zhang, Boyao Zhou, Boning Liu, Shengping Zhang, Liqiang Nie
We present GaussianAvatar, an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video.
1 code implementation • 4 Dec 2023 • Shunyuan Zheng, Boyao Zhou, Ruizhi Shao, Boning Liu, Shengping Zhang, Liqiang Nie, Yebin Liu
We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner.
no code implementations • ICCV 2023 • Zhaoqi Su, Liangxiao Hu, Siyou Lin, Hongwen Zhang, Shengping Zhang, Justus Thies, Yebin Liu
In contrast to previous work on 3D avatar reconstruction, our method is able to generalize to novel poses with realistic dynamic cloth deformations.
no code implementations • 3 Jul 2023 • Yuxiang Zhang, Hongwen Zhang, Liangxiao Hu, Jiajun Zhang, Hongwei Yi, Shengping Zhang, Yebin Liu
For more accurate and physically plausible predictions in world space, our network is designed to learn human motions from a human-centric perspective, which enables the understanding of the same motion captured with different camera trajectories.
Ranked #208 on 3D Human Pose Estimation on Human3.6M
2 code implementations • International Journal of Computer Vision 2023 • Shengping Zhang, Xianzhu Liu, Haozhe Xie, Liqiang Nie, Huiyu Zhou, DaCheng Tao, Xuelong Li
It exploits the repetitive geometric structures in common 3D objects to recover the complete shapes, which contains three sub-networks: geometric patch network, structure transformation network, and detail refinement network.
Ranked #4 on Point Cloud Completion on ShapeNet
1 code implementation • 3 Apr 2023 • Qinglin Liu, Shengping Zhang, Quanling Meng, Ru Li, Bineng Zhong, Liqiang Nie
For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background.
Ranked #1 on Image Matting on Composition-1K
1 code implementation • 25 Sep 2021 • Qinglin Liu, Haozhe Xie, Shengping Zhang, Bineng Zhong, Rongrong Ji
Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte.
Ranked #6 on Image Matting on Composition-1K (using extra training data)
1 code implementation • CVPR 2021 • Zikai Zhang, Bineng Zhong, Shengping Zhang, Zhenjun Tang, Xin Liu, Zhaoxiang Zhang
A practical long-term tracker typically contains three key properties, i. e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
1 code implementation • CVPR 2021 • Haozhe Xie, Hongxun Yao, Shangchen Zhou, Shengping Zhang, Wenxiu Sun
For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame.
no code implementations • 22 Mar 2021 • Chunzhi Yi, Feng Jiang, Shengping Zhang, Hao Guo, Chifu Yang, Zhen Ding, Baichun Wei, Xiangyuan Lan, Huiyu Zhou
Challenges of exoskeletons motor intent decoding schemes remain in making a continuous prediction to compensate for the hysteretic response caused by mechanical transmission.
no code implementations • ECCV 2020 • Yuankai Qi, Zizheng Pan, Shengping Zhang, Anton Van Den Hengel, Qi Wu
The first is object description (e. g., 'table', 'door'), each presenting as a tip for the agent to determine the next action by finding the item visible in the environment, and the second is action specification (e. g., 'go straight', 'turn left') which allows the robot to directly predict the next movements without relying on visual perceptions.
3 code implementations • 22 Jun 2020 • Haozhe Xie, Hongxun Yao, Shengping Zhang, Shangchen Zhou, Wenxiu Sun
A multi-scale context-aware fusion module is then introduced to adaptively select high-quality reconstructions for different parts from all coarse 3D volumes to obtain a fused 3D volume.
Ranked #3 on 3D Object Reconstruction on Data3D−R2N2
1 code implementation • ECCV 2020 • Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, Wenxiu Sun
In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information.
Ranked #3 on Point Cloud Completion on Completion3D
no code implementations • 18 Mar 2020 • Xinjie Feng, Hongxun Yao, Yuankai Qi, Jun Zhang, Shengping Zhang
Different from previous transformer based models [56, 34], which just use the decoder of the transformer to decode the convolutional attention, the proposed method use a convolutional feature maps as word embedding input into transformer.
2 code implementations • CVPR 2020 • Zedu Chen, Bineng Zhong, Guorong Li, Shengping Zhang, Rongrong Ji
Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target.
1 code implementation • 18 Oct 2019 • Haozhe Xie, Hongxun Yao, Shangchen Zhou, Shengping Zhang, Xiaoshuai Sun, Wenxiu Sun
Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem.
no code implementations • 14 Oct 2019 • Ying Zheng, Hongxun Yao, Xiaoshuai Sun, Shengping Zhang, Sicheng Zhao, Fatih Porikli
Conventional methods for this task often rely on the availability of the temporal order of sketch strokes, additional cues acquired from different modalities and supervised augmentation of sketch datasets with real images, which also limit the applicability and feasibility of these methods in real scenarios.
2 code implementations • 19 Jun 2019 • Jun Zhang, Yamei Liu, Shengping Zhang, Ronald Poppe, Meng Wang
Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light.
1 code implementation • 12 May 2019 • Jun Zhang, Tong Zheng, Shengping Zhang, Meng Wang
First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches.
5 code implementations • ICCV 2019 • Haozhe Xie, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhou, Shengping Zhang
Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e. g., table legs) from different coarse 3D volumes to obtain a fused 3D volume.
Ranked #4 on 3D Object Reconstruction on Data3D−R2N2
no code implementations • 17 Aug 2018 • Wenxue Cui, Tao Zhang, Shengping Zhang, Feng Jiang, WangMeng Zuo, Debin Zhao
To overcome this problem, in this paper, an intra prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block.
1 code implementation • 13 Apr 2018 • Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao
To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network.
Ranked #1 on Compressive Sensing on Set5
no code implementations • 22 Jul 2017 • Wuzhen Shi, Feng Jiang, Shengping Zhang, Debin Zhao
First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process.
no code implementations • 30 Jul 2016 • Yashar Deldjoo, Shengping Zhang, Bahman Zanj, Paolo Cremonesi, Matteo Matteucci
Recently, sparse representation based visual tracking methods have attracted increasing attention in the computer vision community.
no code implementations • CVPR 2016 • Yuankai Qi, Shengping Zhang, Lei Qin, Hongxun Yao, Qingming Huang, Jongwoo Lim, Ming-Hsuan Yang
In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking.