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Hybrid mesh-neural representation for 3D transparent object reconstruction

Computational Visual Media 2025年第1期11卷 123-140页

作者： Jiamin Xu Zihan Zhu Hujun Bao Weiwei Xu College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China College of Computer Science ETH ZurichZurich 8092Switzerland.

In this study,we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting *** combines the advantages of an explicit mesh and multi-layer perceptron(MLP)network as a hybrid representation to simplify the capture settings used in recent *** obtaining an initial shape through multi-view silhouettes,we introduced surface-based local MLPs to encode the vertex displacement field(VDF)for reconstructing surface *** design of local MLPs allowed representation of the VDF in a piecewise manner using two-layer MLP networks to support the optimization *** local MLPs on the surface instead of on the volume also reduced the search *** a hybrid representation enabled us to relax the ray–pixel correspondences that represent the light path constraint to our designed ray–cell correspondences,which significantly simplified the implementation of a single-image-based environment-matting *** evaluated our representation and reconstruction algorithm on several transparent objects based on ground truth *** experimental results show that our method produces high-quality reconstructions that are superior to those of state-of-the-art methods using a simplified data-acquisition setup.

关键词： transparent object 3D reconstruction environment matting neural rendering

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Solving Zero-Sum Markov Games with Continuous state via Spectral Dynamic Embedding 38

Solving Zero-Sum Markov Games with Continuous State via Spec...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Chenhao Shen, Zebang Zhang, Chao Zhao, Hanbin Qian, Hui College of Computer Science and Technology Zhejiang University China Department of Computer Science ETH Zurich Switzerland State Key Lab of CAD&CG Zhejiang University China

In this paper, we propose a provably efficient natural policy gradient algorithm called Spectral Dynamic Embedding Policy Optimization (SDEPO) for two-player zero-sum stochastic Markov games with continuous state space and finite action space. In the policy evaluation procedure of our algorithm, a novel kernel embedding method is employed to construct a finite-dimensional linear approximations to the state-action value function. We explicitly analyze the approximation error in policy evaluation, and show that SDEPO achieves an Õ((1−γ1)3ϵ) last-iterate convergence to the ϵ−optimal Nash equilibrium, which is independent of the cardinality of the state space. The complexity result matches the best-known results for global convergence of policy gradient algorithms for single agent setting. Moreover, we also propose a practical variant of SDEPO to deal with continuous action space and empirical results demonstrate the practical superiority of the proposed method. © 2024 Neural information processing systems foundation. All rights reserved.

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Identity-consistent transfer learning of portraits for digital apparel sample display

Identity-consistent transfer learning of portraits for digit...

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作者： Wang, Luyuan Wu, Yiqian Yang, Yong-Liang Liu, Chen Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University Hangzhou China Department of Computer Science University of Bath Bath United Kingdom

The rapid development of the online apparel shopping industry demands innovative solutions for high-quality digital apparel sample displays with virtual avatars. However, developing such displays is prohibitively expensive and prone to the well-known "uncanny valley" effect, where a nearly human-looking artifact arouses eeriness and repulsiveness, thus affecting the user experience. To effectively mitigate the "uncanny valley" effect and improve the overall authenticity of digital apparel sample displays, we present a novel photo-realistic portrait generation framework. Our key idea is to employ transfer learning to learn an identity-consistent mapping from the latent space of rendered portraits to that of real portraits. During the inference stage, the input portrait of an avatar can be directly transferred to a realistic portrait by changing its appearance style while maintaining the facial identity. To this end, we collect a new dataset, Daz-Rendered-Faces-HQ (DRFHQ), specifically designed for rendering-style portraits. We leverage this dataset to fine-tune the StyleGAN2-FFHQ generator, using our carefully crafted framework, which helps to preserve the geometric and color features relevant to facial identity. We evaluate our framework using portraits with diverse gender, age, and race variations. Qualitative and quantitative evaluations, along with ablation studies, highlight our method's advantages over state-of-the-art approaches. © 2024 John Wiley & Sons Ltd.

关键词： Authentication

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Self-Distillation Hashing for Efficient Hamming Space Retrieval 48

Self-Distillation Hashing for Efficient Hamming Space Retrie...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhai, Hongjia Li, Hai Zhang, Hanzhi Bao, Hujun Zhang, Guofeng Zhejiang University State Key Lab of Cad&cg Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9781728163277

Deep hashing-based approaches have become the optimal solutions for large-scale image retrieval task due to their high computational efficiency and low storage burden. Some methods leverage a large teacher network to improve the retrieval performance of the small student network through knowledge distillation, which incurs high computational and time costs. In this paper, we propose Self-Distillation Hashing (SeDH), which improves the image retrieval performance without introducing a complex teacher model and significantly reduces the overall computation costs. Specifically, we generate the soft targets via ensembling the logits of other similar images among the mini-batch. The ensembled soft targets can model the relations between different image samples, which can act as additional supervision for classification. Besides, to learn more compact features and accurate inter-sample similarities, we propose a similarity-preserving loss on the learned hashing features, which aligns the softened similarity distribution with the pairwise soft similarity. Extensive experiments demonstrate that our approach can yield state-of-the-art performance on deep supervised hashing retrieval. © 2023 IEEE.

关键词： Deep supervised hashing Image retrieval Pairwise similarity Self-distillation

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In Defence of Visual Analytics Systems: Replies to Critics

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 1026-1036页

作者： Wu, Aoyu Deng, Dazhen Cheng, Furui Wu, Yingcai Liu, Shixia Qu, Huamin Hong Kong University of Science and Technology China Zhejiang University State Key Lab of CAD&CG China School of Software Tsinghua University China

The last decade has witnessed many visual analytics (VA) systems that make successful applications to wide-ranging domains like urban analytics and explainable AI. However, their research rigor and contributions have been extensively challenged within the visualization community. We come in defence of VA systems by contributing two interview studies for gathering critics and responses to those criticisms. First, we interview 24 researchers to collect criticisms the review comments on their VA work. Through an iterative coding and refinement process, the interview feedback is summarized into a list of 36 common criticisms. Second, we interview 17 researchers to validate our list and collect their responses, thereby discussing implications for defending and improving the scientific values and rigor of VA systems. We highlight that the presented knowledge is deep, extensive, but also imperfect, provocative, and controversial, and thus recommend reading with an inclusive and critical eye. We hope our work can provide thoughts and foundations for conducting VA research and spark discussions to promote the research field forward more rigorously and vibrantly. © 2022 IEEE.

关键词： Iterative methods

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Implicit Surface Reconstruction via RBF Interpolation: A Review

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Recent Patents on Engineering 2022年第5期16卷 49-66页

作者： Mo, Jiahui Shou, Huahao Chen, Wei College of Science Zhejiang University of Technology Hangzhou China The State Key Lab of CAD&CG Zhejiang University Hangzhou China

Background: Implicit surface is a kind of surface modeling tool, which is widely used in point cloud reconstruction, deformation and fusion due to its advantages of good smoothness and Boolean operation. The most typical method is the surface reconstruction with Radial Basis Functions (RBF) under normal constraints. RBF has become one of the main methods of point cloud fitting because it has a strong mathematical foundation, an advantage of computation simplicity, and the ability of processing nonuniform points. Objective: Techniques and patents of implicit surface reconstruction interpolation with RBF are surveyed. Theory, algorithm, and application are discussed to provide a comprehensive summary for implicit surface reconstruction in RBF and Hermite Radial Basis Functions (HRBF) interpolation. Methods: RBF implicit surface reconstruction interpolation can be divided into RBF interpolation under the constraints of points and HRBF interpolation under the constraints of points and corresponding normals. Results: A total of 125 articles were reviewed, in which more than 30% were related to RBF in the last decade. The continuity properties and application fields of the popular global supported radial basis functions and compactly supported radial basis functions are analyzed. Different methods of RBF and HRBF implicit surface reconstruction are evaluated, and the challenges of these methods are discussed. Conclusion: In future work, implicit surface reconstruction via RBF and HRBF should be further studied in fitting accuracy, computation speed, and other fundamental problems. In addition, it is a more challenging but valuable research direction to construct a new RBF with both compact support and improved fitting accuracy. © 2022 Bentham science Publishers.

关键词： Interpolation

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Tac-Trainer: A Visual Analytics System for IoT-based Racket Sports Training

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 951-961页

作者： Wang, Jiachen Ma, Ji Hu, Kangping Zhou, Zheng Zhang, Hui Xie, Xiao Wu, Yingcai Zhejiang University State Key Lab of CAD&CG China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China Zhejiang University Department of Sports Science China

Conventional racket sports training highly relies on coaches' knowledge and experience, leading to biases in the guidance. To solve this problem, smart wearable devices based on Internet of Things technology (IoT) have been extensively investigated to support data-driven training. Considerable studies introduced methods to extract valuable information from the sensor data collected by IoT devices. However, the information cannot provide actionable insights for coaches due to the large data volume and high data dimensions. We proposed an IoT + VA framework, Tac-Trainer, to integrate the sensor data, the information, and coaches' knowledge to facilitate racket sports training. Tac-Trainer consists of four components: device configuration, data interpretation, training optimization, and result visualization. These components collect trainees' kinematic data through IoT devices, transform the data into attributes and indicators, generate training suggestions, and provide an interactive visualization interface for exploration, respectively. We further discuss new research opportunities and challenges inspired by our work from two perspectives, VA for IoT and IoT for VA. © 2022 IEEE.

关键词： Sports

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OBTracker: Visual Analytics of Off-ball Movements in Basketball

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 929-939页

作者： Wu, Yihong Deng, Dazhen Xie, Xiao He, Moqi Xu, Jie Zhang, Hongzeng Zhang, Hui Wu, Yingcai State Key Lab of CAD&CG Zhejiang University China Department of Sports Science Zhejiang University China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team's offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback. © 2022 IEEE.

关键词： Sports

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DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 690-700页

作者： Deng, Dazhen Wu, Aoyu Qu, Huamin Wu, Yingcai Zhejiang University State Key Lab of CAD&CG China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets. © 2022 IEEE.

关键词： Reinforcement learning

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Facial action units detection using temporal context and feature reassignment

Facial action units detection using temporal context and fea...

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作者： Yang, Sipeng Huang, Hongyu Huang, Ying Sophie Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University Hangzhou China School of Computer Science and Technology Zhejiang University Hangzhou China School of Management Zhejiang University Hangzhou China

Facial action units (AUs) encode the activations of facial muscle groups, playing a crucial role in expression analysis and facial animation. However, current deep learning AU detection methods primarily focus on single-image analysis, which limits the exploitation of rich temporal context for robust outcomes. Moreover, the scale of available datasets remains limited, leading models trained on these datasets to tend to suffer from overfitting issues. This paper proposes a novel AU detection method integrating spatial and temporal data with inter-subject feature reassignment for accurate and robust AU predictions. Our method first extracts regional features from facial images. Then, to effectively capture both the temporal context and identity-independent features, we introduce a temporal feature combination and feature reassignment (TC&FR) module, which transforms single-image features into a cohesive temporal sequence and fuses features across multiple subjects. This transformation encourages the model to utilize identity-independent features and temporal context, thus ensuring robust prediction outcomes. Experimental results demonstrate the enhancements brought by the proposed modules and the state-of-the-art (SOTA) results achieved by our method. © 2024 John Wiley & Sons Ltd.

关键词： Feature extraction

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