Can Wang

1.1k total citations · 1 hit paper
30 papers, 488 citations indexed

About

Can Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Can Wang has authored 30 papers receiving a total of 488 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 5 papers in Information Systems. Recurrent topics in Can Wang's work include Generative Adversarial Networks and Image Synthesis (9 papers), Face recognition and analysis (6 papers) and Computer Graphics and Visualization Techniques (4 papers). Can Wang is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (9 papers), Face recognition and analysis (6 papers) and Computer Graphics and Visualization Techniques (4 papers). Can Wang collaborates with scholars based in China, Hong Kong and United States. Can Wang's co-authors include Jing Liao, Mingming He, Dongdong Chen, Menglei Chai, Shangfei Wang, Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Ruixiang Jiang and Shenda Hong and has published in prestigious journals such as Physical Review Letters, Scientific Reports and Pattern Recognition.

In The Last Decade

Can Wang

26 papers receiving 476 citations

Hit Papers

CLIP-NeRF: Text-and-Image Driven Manipulation of Neural R... 2022 2026 2023 2024 2022 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields
    2022 202 citations Can Wang, Menglei Chai et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Can Wang China 10 293 181 143 57 50 30 488
Zhiyong Huang China 12 246 0.8× 102 0.6× 164 1.1× 64 1.1× 33 0.7× 57 469
Peter Sand United States 10 654 2.2× 107 0.6× 111 0.8× 71 1.2× 63 1.3× 11 727
Yannick Hold-Geoffroy United States 10 333 1.1× 191 1.1× 74 0.5× 23 0.4× 14 0.3× 31 417
Giulia Fracastoro Italy 10 194 0.7× 61 0.3× 100 0.7× 49 0.9× 5 0.1× 34 352
Claus Madsen Denmark 12 318 1.1× 68 0.4× 23 0.2× 27 0.5× 54 1.1× 78 533
Zhong Ren China 11 284 1.0× 317 1.8× 180 1.3× 15 0.3× 38 0.8× 41 474
Fujiichi Yoshimoto Japan 8 125 0.4× 72 0.4× 204 1.4× 20 0.4× 16 0.3× 24 338
Stefan Hiller Germany 6 494 1.7× 570 3.1× 342 2.4× 27 0.5× 18 0.4× 10 722
Erhan Gündoğdu Türkiye 10 340 1.2× 43 0.2× 56 0.4× 43 0.8× 16 0.3× 23 424

Countries citing papers authored by Can Wang

Since Specialization
Citations

This map shows the geographic impact of Can Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Can Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Can Wang more than expected).

Fields of papers citing papers by Can Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Can Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Can Wang. The network helps show where Can Wang may publish in the future.

Co-authorship network of co-authors of Can Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Can Wang. A scholar is included among the top collaborators of Can Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Can Wang. Can Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Can, et al.. (2025). Chat2Layout: Interactive 3D Furniture Layout With a Multimodal LLM. IEEE Transactions on Visualization and Computer Graphics. 32(2). 2243–2259.
2.
Wang, Can, et al.. (2025). Generative object insertion in Gaussian splatting with a multi-view diffusion model. Visual Informatics. 9(2). 100238–100238.
3.
Liu, Bei, Ningbo Wang, Can Wang, & Hua‐Ping Wan. (2025). A computer vision-based real-time monitoring method for swivel bridges spatial rotation. Measurement. 250. 117236–117236.
4.
Wang, Shangfei, et al.. (2024). Pose-robust personalized facial expression recognition through unsupervised multi-source domain adaptation. Pattern Recognition. 150. 110311–110311. 3 indexed citations
5.
Zhang, Jingbo, Xiaoyu Li, Ziyu Wan, Can Wang, & Jing Liao. (2024). Text2NeRF: Text-Driven 3D Scene Generation With Neural Radiance Fields. IEEE Transactions on Visualization and Computer Graphics. 30(12). 7749–7762. 34 indexed citations
6.
Wang, Can, et al.. (2023). Data Generation with GAN Networks for Sidescan Sonar in Semantic Segmentation Applications. Journal of Marine Science and Engineering. 11(9). 1792–1792. 5 indexed citations
7.
8.
Wu, Dian, Qi Zhao, Can Wang, et al.. (2022). Closing the Locality and Detection Loopholes in Multiparticle Entanglement Self-Testing. Physical Review Letters. 128(25). 250401–250401. 9 indexed citations
9.
Wang, Can, Menglei Chai, Mingming He, Dongdong Chen, & Jing Liao. (2022). Cross-Domain and Disentangled Face Manipulation With 3D Guidance. IEEE Transactions on Visualization and Computer Graphics. 29(4). 2053–2066. 14 indexed citations
10.
Wang, Can, et al.. (2022). HITS: Binarizing physiological time series with deep hashing neural network. Pattern Recognition Letters. 156. 23–28. 1 indexed citations
11.
Wang, Can, et al.. (2022). An Efficient In-Memory Analytics System Based on Persistent Memory. 2022 IEEE International Conference on Big Data (Big Data). 6818–6820. 1 indexed citations
12.
Zhang, Feihu, et al.. (2021). Reinforcement Learning Path Planning Method with Error Estimation. Energies. 15(1). 247–247. 11 indexed citations
13.
Wang, Can, et al.. (2021). Deep Portrait Lighting Enhancement with 3D Guidance. Computer Graphics Forum. 40(4). 177–188. 1 indexed citations
14.
Wang, Shangfei, et al.. (2021). Dual Learning for Joint Facial Landmark Detection and Action Unit Recognition. IEEE Transactions on Affective Computing. 14(2). 1404–1416. 5 indexed citations
15.
Hong, Shenda, et al.. (2020). Gated temporal convolutional neural network and expert features for diagnosing and explaining physiological time series: A case study on heart rates. Computer Methods and Programs in Biomedicine. 200. 105847–105847. 6 indexed citations
16.
Wang, Shangfei, et al.. (2020). Exploiting Multi-Emotion Relations at Feature and Label Levels for Emotion Tagging. 2955–2963. 3 indexed citations
17.
Wang, Can, E. de Jong, Willem Woudstra, et al.. (2019). Bacterial Density and Biofilm Structure Determined by Optical Coherence Tomography. Scientific Reports. 9(1). 9794–9794. 53 indexed citations
18.
19.
Wang, Can, et al.. (2016). Research on algorithm of attribute reduction based on concept with introducer. 1318–1323. 1 indexed citations
20.
Wang, Can. (2001). An algorithm for two-dimensional PIV images. Journal of Hydrodynamics. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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