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学者姓名：孔德慧
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Abstract ：
Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D lifting approaches have achieved remarkable improvements. However, monocular 3D HPE is still a challenging problem due to the inherent depth ambiguities and occlusions. Recently, diffusion models have achieved great success in the field of image generation. Inspired by this, we transform 3D human pose estimation problem into a reverse diffusion process, and propose a dual-branch diffusion model so as to handle the indeterminacy and uncertainty of 3D pose and fully explore the global and local correlations between joints. Furthermore, we propose conditional dual-branch diffusion model to enhance the performance of 3D human pose estimation, in which the joint-level semantic information are regarded as the condition of the diffusion model, and integrated into the joint-level representations of 2D pose to enhance the expression of joints. The proposed method is verified on two widely used datasets and the experimental results have demonstrated the superiority.
Keyword ：
Human pose estimation Human pose estimation Diffusion model Diffusion model Joint semantics Joint semantics Dual-branch Dual-branch
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| GB/T 7714 | Li, Jinghua , Bai, Zhuowei , Kong, Dehui et al. 3d human pose estimation based on conditional dual-branch diffusion [J]. | MULTIMEDIA SYSTEMS , 2025 , 31 (1) . |
| MLA | Li, Jinghua et al. "3d human pose estimation based on conditional dual-branch diffusion" . | MULTIMEDIA SYSTEMS 31 . 1 (2025) . |
| APA | Li, Jinghua , Bai, Zhuowei , Kong, Dehui , Chen, Dongpan , Li, Qianxing , Yin, Baocai . 3d human pose estimation based on conditional dual-branch diffusion . | MULTIMEDIA SYSTEMS , 2025 , 31 (1) . |
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Abstract ：
Human pose estimation based on monocular video has always been the focus of research in the human computer interaction community, which suffers mainly from depth ambiguity and self-occlusion challenges. While the recently proposed learning-based approaches have demonstrated promising performance, they do not fully explore the complementarity of features. In this paper, the authors propose a novel multi-feature and multi-level fusion network (MMF-Net), which extracts and combines joint features, bone features and trajectory features at multiple levels to estimate 3D human pose. In MMF-Net, firstly, the bone length estimation module and the trajectory multi-level fusion module are used to extract the geometric size information of the human body and multi-level trajectory information of human motion, respectively. Then, the fusion attention-based combination (FABC) module is used to extract multi-level topological structure information of the human body, and effectively fuse topological structure information, geometric size information and trajectory information. Extensive experiments show that MMF-Net achieves competitive results on Human3.6M, HumanEva-I and MPI-INF-3DHP datasets.
Keyword ：
image processing image processing pose estimation pose estimation computer vision computer vision image reconstruction image reconstruction
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| GB/T 7714 | Li, Qianxing , Kong, Dehui , Li, Jinghua et al. MMF-Net: A novel multi-feature and multi-level fusion network for 3D human pose estimation [J]. | IET COMPUTER VISION , 2025 , 19 (1) . |
| MLA | Li, Qianxing et al. "MMF-Net: A novel multi-feature and multi-level fusion network for 3D human pose estimation" . | IET COMPUTER VISION 19 . 1 (2025) . |
| APA | Li, Qianxing , Kong, Dehui , Li, Jinghua , Yin, Baocai . MMF-Net: A novel multi-feature and multi-level fusion network for 3D human pose estimation . | IET COMPUTER VISION , 2025 , 19 (1) . |
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Abstract ：
弱监督时序动作定位任务的目标是在只有视频级标签的情况下,对未分割的视频中的动作进行分类和时序上的定位。目前基于神经网络模型的方法,大多训练分类器以预测视频片段级的类别分数,再融合其为视频级的类别分数。这些方法只关注视频的视觉特征,却忽视了视频语义结构信息。为进一步提升视频动作定位的质量,本文提出了一种视频语义结构信息辅助的弱监督时序动作定位方法。该方法首先以分类模块作为基础模型,然后基于视频在时序结构上的稀疏性和语义连续性等辅助信息设计一种平滑注意力模块,修正分类结果;另外,加入视频片段级语义标签预测模块,改善弱监督标签信息不充足问题;最后将三个模块共同训练以融合提升时序动作定位的精度。通过在THUMOS14和ActivityNet数据集上的实验,表明本文方法的性能指标明显优于目前现有方法。
Keyword ：
语义结构信息 语义结构信息 伪标签 伪标签 注意力值 注意力值 动作定位 动作定位 弱监督 弱监督
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| GB/T 7714 | 孔德慧 , 许梦文 , 李敬华 et al. 一种视频语义结构信息辅助的弱监督时序动作定位方法 [C] //2021中国自动化大会论文集 . 2022 . |
| MLA | 孔德慧 et al. "一种视频语义结构信息辅助的弱监督时序动作定位方法" 2021中国自动化大会论文集 . (2022) . |
| APA | 孔德慧 , 许梦文 , 李敬华 , 王少帆 , 尹宝才 . 一种视频语义结构信息辅助的弱监督时序动作定位方法 2021中国自动化大会论文集 . (2022) . |
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Abstract ：
3D pose estimation remains a challenging task since human poses exhibit high ambiguity and multigranularity. Traditional graph convolution networks (GCNs) accomplish the task by modeling all skeletons as an entire graph, and are unable to fuse combinable part-based features. By observing that human movements occur due to part of human body (i.e. related skeletons and body components, known as the poselet) and those poselets contribute to each movement in a hierarchical fashion, we propose a hierarchical poselet-guided graph convolutional network (HPGCN) for 3D pose estimation from 2D poses. HPGCN sets five primitives of human body as basic poselets, and constitutes high-level poselets according to the kinematic configuration of human body. Moreover, HPGCN forms a fundamental unit by using a diagonally dominant graph convolution layer and a non-local layer, which corporately capture the multi-granular feature of human poses from local to global perspective. Finally HPGCN designs a geometric constraint loss function with constraints on lengths and directions of bone vectors, which help produce reasonable pose regression. We verify the effectiveness of HPGCN on three public 3D human pose benchmarks. Experimental results show that HPGCN outperforms several state-of-the-art methods. (c) 2021 Elsevier B.V.
Keyword ：
Graph convolutional network Graph convolutional network Hierarchical poselet Hierarchical poselet Geometric constraint Geometric constraint Diagonally dominant graph convolution Diagonally dominant graph convolution Pose estimation Pose estimation
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| GB/T 7714 | Wu, Yongpeng , Kong, Dehui , Wang, Shaofan et al. HPGCN: Hierarchical poselet-guided graph convolutional network for 3D pose estimation [J]. | NEUROCOMPUTING , 2022 , 487 : 243-256 . |
| MLA | Wu, Yongpeng et al. "HPGCN: Hierarchical poselet-guided graph convolutional network for 3D pose estimation" . | NEUROCOMPUTING 487 (2022) : 243-256 . |
| APA | Wu, Yongpeng , Kong, Dehui , Wang, Shaofan , Li, Jinghua , Yin, Baocai . HPGCN: Hierarchical poselet-guided graph convolutional network for 3D pose estimation . | NEUROCOMPUTING , 2022 , 487 , 243-256 . |
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Abstract ：
弱监督时序动作定位任务的目标是在只有视频级标签的情况下,对未分割的视频中的动作进行分类和时序上的定位。目前基于神经网络模型的方法,大多训练分类器以预测视频片段级的类别分数,再融合其为视频级的类别分数。这些方法只关注视频的视觉特征,却忽视了视频语义结构信息。为进一步提升视频动作定位的质量,本文提出了一种视频语义结构信息辅助的弱监督时序动作定位方法。该方法首先以分类模块作为基础模型,然后基于视频在时序结构上的稀疏性和语义连续性等辅助信息设计一种平滑注意力模块,修正分类结果;另外,加入视频片段级语义标签预测模块,改善弱监督标签信息不充足问题;最后将三个模块共同训练以融合提升时序动作定位的精度。通过在THUMOS14和ActivityNet数据集上的实验,表明本文方法的性能指标明显优于目前现有方法。
Keyword ：
语义结构信息 语义结构信息 弱监督 弱监督 动作定位 动作定位 注意力值 注意力值 伪标签 伪标签
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| GB/T 7714 | 孔德慧 , 许梦文 , 李敬华 et al. 一种视频语义结构信息辅助的弱监督时序动作定位方法 [C] //2021中国自动化大会——中国自动化学会60周年会庆暨纪念钱学森诞辰110周年 . 2021 . |
| MLA | 孔德慧 et al. "一种视频语义结构信息辅助的弱监督时序动作定位方法" 2021中国自动化大会——中国自动化学会60周年会庆暨纪念钱学森诞辰110周年 . (2021) . |
| APA | 孔德慧 , 许梦文 , 李敬华 , 王少帆 , 尹宝才 . 一种视频语义结构信息辅助的弱监督时序动作定位方法 2021中国自动化大会——中国自动化学会60周年会庆暨纪念钱学森诞辰110周年 . (2021) . |
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Abstract ：
3-D action recognition is referred to as the classification of action sequences which consist of 3-D skeleton joints. While many research works are devoted to 3-D action recognition, it mainly suffers from three problems: 1) highly complicated articulation; 2) a great amount of noise; and 3) low implementation efficiency. To tackle all these problems, we propose a real-time 3-D action-recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first define the skeletonlet as a few combinations of joint offsets grouped in terms of the kinematic principle and then represent an action sequence using LAKS, which consists of a denoising phase and a locally aggregating phase. The denoising phase detects the noisy action data and adjusts it by replacing all the features within it with the features of the corresponding previous frame, while the locally aggregating phase sums the difference between an offset feature of the skeletonlet and its cluster center together over all the offset features of the sequence. Finally, the SHA model combines sparse representation with a hashing model, aiming at promoting the recognition accuracy while maintaining high efficiency. Experimental results on MSRAction3D, UTKinectAction3D, and Florence3DAction datasets demonstrate that the proposed method outperforms state-of-the-art methods in both recognition accuracy and implementation efficiency.
Keyword ：
Joints Joints skeletonlet skeletonlet sparse representation sparse representation skeleton joints skeleton joints Solid modeling Solid modeling Feature extraction Feature extraction Computational modeling Computational modeling Kinematics Kinematics Action recognition Action recognition Real-time systems Real-time systems hashing hashing Analytical models Analytical models
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| GB/T 7714 | Sun, Bin , Wang, Shaofan , Kong, Dehui et al. Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2021 , 52 (6) : 4837-4849 . |
| MLA | Sun, Bin et al. "Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model" . | IEEE TRANSACTIONS ON CYBERNETICS 52 . 6 (2021) : 4837-4849 . |
| APA | Sun, Bin , Wang, Shaofan , Kong, Dehui , Wang, Lichun , Yin, Baocai . Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model . | IEEE TRANSACTIONS ON CYBERNETICS , 2021 , 52 (6) , 4837-4849 . |
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Abstract ：
Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4(2), and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.
Keyword ：
sparse representation sparse representation Action recognition Action recognition transfer learning transfer learning multi-view multi-view
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| GB/T 7714 | Sun, Bin , Kong, Dehui , Wang, Shaofan et al. Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (2) . |
| MLA | Sun, Bin et al. "Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 15 . 2 (2021) . |
| APA | Sun, Bin , Kong, Dehui , Wang, Shaofan , Wang, Lichun , Yin, Baocai . Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (2) . |
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Abstract ：
Although deep networks based methods outperform traditional 3D reconstruction methods which require multiocular images or class labels to recover the full 3D geometry, they may produce incomplete recovery and unfaithful reconstruction when facing occluded parts of 3D objects. To address these issues, we propose Depth-preserving Latent Generative Adversarial Network (DLGAN) which consists of 3D Encoder-Decoder based GAN (EDGAN, serving as a generator and a discriminator) and Extreme Learning Machine (ELM, serving as a classifier) for 3D reconstruction from a monocular depth image of an object. Firstly, EDGAN decodes a latent vector from the 2.5D voxel grid representation of an input image, and generates the initial 3D occupancy grid under common GAN losses, a latent vector loss and a depth loss. For the latent vector loss, we design 3D deep AutoEncoder (AE) to learn a target latent vector from ground truth 3D voxel grid and utilize the vector to penalize the latent vector encoded from the input 2.5D data. For the depth loss, we utilize the input 2.5D data to penalize the initial 3D voxel grid from 2.5D views. Afterwards, ELM transforms float values of the initial 3D voxel grid to binary values under a binary reconstruction loss. Experimental results show that DLGAN not only outperforms several state-of-the-art methods by a large margin on both a synthetic dataset and a real-world dataset, but also predicts more occluded parts of 3D objects accurately without class labels.
Keyword ：
depth loss depth loss monocular depth image monocular depth image Three-dimensional displays Three-dimensional displays 3D reconstruction 3D reconstruction Transforms Transforms ELM ELM Image reconstruction Image reconstruction Shape Shape Generative adversarial networks Generative adversarial networks latent vector latent vector Two dimensional displays Two dimensional displays Gallium nitride Gallium nitride
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| GB/T 7714 | Liu, Caixia , Kong, Dehui , Wang, Shaofan et al. DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D Reconstruction [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 : 2843-2856 . |
| MLA | Liu, Caixia et al. "DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D Reconstruction" . | IEEE TRANSACTIONS ON MULTIMEDIA 23 (2021) : 2843-2856 . |
| APA | Liu, Caixia , Kong, Dehui , Wang, Shaofan , Li, Jinghua , Yin, Baocai . DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D Reconstruction . | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 , 2843-2856 . |
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Abstract ：
为了实现更为精确的视频目标跟踪,提出一种以时空上下文模型为基础的RGB-D序列目标跟踪算法.通过引入更新模板的深度信息,该模型精准地区分了输入序列的目标区域与背景区域,实现了深度权值和颜色权值的融合;基于目标序列的深度及目标动量计算,该模型有效地实现了尺度更新与遮挡处理.通过在RGB-D图像序列数据集上的详细实验评估,该时空上下文模型相对于其他先进的同类方法表现出更好的性能.因此,该方法实现了更为精确可靠的视频目标跟踪.
Keyword ：
目标跟踪 目标跟踪 机器学习 机器学习 RGB-D RGB-D 目标动量 目标动量 计算机视觉 计算机视觉 时空上下文 时空上下文
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| GB/T 7714 | 孔德慧 , 荣子豪 , 贾思宇 et al. 基于时空上下文模型的RGB-D序列目标跟踪方法 [J]. | 北京工业大学学报 , 2021 , 47 (03) : 224-230 . |
| MLA | 孔德慧 et al. "基于时空上下文模型的RGB-D序列目标跟踪方法" . | 北京工业大学学报 47 . 03 (2021) : 224-230 . |
| APA | 孔德慧 , 荣子豪 , 贾思宇 , 王少帆 , 尹宝才 . 基于时空上下文模型的RGB-D序列目标跟踪方法 . | 北京工业大学学报 , 2021 , 47 (03) , 224-230 . |
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Abstract ：
基于时空上下文模型的RGB-D序列目标跟踪方法
Keyword ：
目标跟踪 目标跟踪 时空上下文 时空上下文 计算机视觉 计算机视觉 RGB-D RGB-D 目标动量 目标动量 机器学习 机器学习
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| GB/T 7714 | 孔德慧 , 荣子豪 , 贾思宇 et al. 基于时空上下文模型的RGB-D序列目标跟踪方法 [J]. | 孔德慧 , 2021 , 47 (3) : 224-230 . |
| MLA | 孔德慧 et al. "基于时空上下文模型的RGB-D序列目标跟踪方法" . | 孔德慧 47 . 3 (2021) : 224-230 . |
| APA | 孔德慧 , 荣子豪 , 贾思宇 , 王少帆 , 尹宝才 , 北京工业大学学报 . 基于时空上下文模型的RGB-D序列目标跟踪方法 . | 孔德慧 , 2021 , 47 (3) , 224-230 . |
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