James Tompkin

3.0k total citations · 1 hit paper
72 papers, 1.4k citations indexed

About

James Tompkin is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Human-Computer Interaction. According to data from OpenAlex, James Tompkin has authored 72 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Computer Vision and Pattern Recognition, 17 papers in Computer Graphics and Computer-Aided Design and 9 papers in Human-Computer Interaction. Recurrent topics in James Tompkin's work include Advanced Vision and Imaging (31 papers), Computer Graphics and Visualization Techniques (17 papers) and Video Analysis and Summarization (11 papers). James Tompkin is often cited by papers focused on Advanced Vision and Imaging (31 papers), Computer Graphics and Visualization Techniques (17 papers) and Video Analysis and Summarization (11 papers). James Tompkin collaborates with scholars based in United States, United Kingdom and Germany. James Tompkin's co-authors include Hanspeter Pfister, Christian Theobalt, Jan Kautz, Kwang In Kim, Numair Khan, Nicolas Bonneel, Sylvain Paris, Deqing Sun, Kalyan Sunkavalli and Srinath Sridhar and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and International Journal of Computer Vision.

In The Last Decade

James Tompkin

64 papers receiving 1.4k citations

Hit Papers

Neural Fields in Visual Computing and Beyond 2022 2026 2023 2024 2022 50 100 150 200 250
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. Neural Fields in Visual Computing and Beyond
    2022 275 citations Yiheng Xie, Towaki Takikawa et al. Computer Graphics Forum profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Tompkin United States 21 981 287 225 188 171 72 1.4k
Hanqiu Sun Hong Kong 21 1.2k 1.2× 424 1.5× 343 1.5× 152 0.8× 109 0.6× 148 1.7k
Vincent Sitzmann United States 12 965 1.0× 279 1.0× 299 1.3× 212 1.1× 107 0.6× 23 1.8k
Amit H. Bermano Israel 19 1.2k 1.2× 363 1.3× 269 1.2× 105 0.6× 98 0.6× 54 1.6k
Stephen DiVerdi United States 22 1.1k 1.2× 375 1.3× 329 1.5× 494 2.6× 62 0.4× 62 1.5k
Mike J. Chantler United Kingdom 20 621 0.6× 265 0.9× 164 0.7× 74 0.4× 85 0.5× 141 1.3k
Franc Solina Slovenia 20 1.3k 1.3× 124 0.4× 241 1.1× 182 1.0× 154 0.9× 87 1.9k
Sean Fanello United States 20 1.1k 1.1× 365 1.3× 399 1.8× 272 1.4× 130 0.8× 45 1.4k
Xiaowu Chen China 28 1.6k 1.6× 277 1.0× 406 1.8× 143 0.8× 78 0.5× 106 2.2k
Chongyang Ma China 26 1.8k 1.8× 632 2.2× 623 2.8× 184 1.0× 198 1.2× 74 2.3k
Christian Richardt United Kingdom 18 1.5k 1.5× 477 1.7× 304 1.4× 169 0.9× 115 0.7× 60 1.7k

Countries citing papers authored by James Tompkin

Since Specialization
Citations

This map shows the geographic impact of James Tompkin'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 James Tompkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Tompkin more than expected).

Fields of papers citing papers by James Tompkin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James Tompkin. 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 James Tompkin. The network helps show where James Tompkin may publish in the future.

Co-authorship network of co-authors of James Tompkin

This figure shows the co-authorship network connecting the top 25 collaborators of James Tompkin. A scholar is included among the top collaborators of James Tompkin 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 James Tompkin. James Tompkin 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.
Tompkin, James, Hujun Bao, Yujun Shen, et al.. (2025). Efficient Object Reconstruction with Differentiable Area Light Shading. 1–12.
2.
Peng, Yifan, et al.. (2024). Local Gaussian Density Mixtures for Unstructured Lumigraph Rendering. 1–11. 1 indexed citations
3.
Meuleman, Andréas, et al.. (2024). OmniSDF: Scene Reconstruction Using Omnidirectional Signed Distance Functions and Adaptive Binoctrees. 20227–20236. 2 indexed citations
4.
Zhang, Qian, et al.. (2024). Learning physically based material and lighting decompositions for face editing. Computational Visual Media. 10(2). 295–308.
5.
Yang, Fumeng, Yuxin Ma, Lane Harrison, James Tompkin, & David H. Laidlaw. (2023). How Can Deep Neural Networks Aid Visualization Perception Research? Three Studies on Correlation Judgments in Scatterplots. 1–17. 7 indexed citations
6.
Xie, Yiheng, Towaki Takikawa, Shunsuke Saito, et al.. (2022). Neural Fields in Visual Computing and Beyond. Computer Graphics Forum. 41(2). 641–676. 275 indexed citations breakdown →
7.
Saffo, David, Nicholas A. Bond, Cody Dunne, et al.. (2021). Scalable Scalable Vector Graphics: Automatic Translation of Interactive SVGs to a Multithread VDOM for Fast Rendering. IEEE Transactions on Visualization and Computer Graphics. 28(9). 3219–3234. 8 indexed citations
8.
Jang, Won-Dong, Denis Schapiro, Donglai Wei, et al.. (2020). Channel Embedding for Informative Protein Identification from Highly Multiplexed Images. Lecture notes in computer science. 12265. 3–13. 2 indexed citations
9.
Dunne, Cody, et al.. (2019). Scalable Scalable Vector Graphics. OSF Preprints (OSF Preprints). 1 indexed citations
10.
Tompkin, James, et al.. (2019). Real-time Virtual Object Insertion for Moving 360° Videos. Pure (University of Bath). 1–9. 9 indexed citations
11.
12.
Papoutsaki, Alexandra, et al.. (2018). The eye of the typer. 1–9. 23 indexed citations
13.
Bharaj, Gaurav, David I. W. Levin, James Tompkin, et al.. (2015). Computational design of metallophone contact sounds. ACM Transactions on Graphics. 34(6). 1–13. 42 indexed citations
14.
Rhodin, Helge, James Tompkin, Kwang In Kim, et al.. (2015). Generalizing wave gestures from sparse examples for real-time character control. ACM Transactions on Graphics. 34(6). 1–12. 24 indexed citations
15.
Bharaj, Gaurav, Stelian Coros, Bernhard Thomaszewski, et al.. (2015). Computational design of walking automata. 93–100. 20 indexed citations
16.
Kwon, Younghee, et al.. (2015). Efficient Learning of Image Super-Resolution and Compression Artifact Removal with Semi-Local Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(9). 1792–1805. 40 indexed citations
17.
Tompkin, James, James McCann, Hanspeter Pfister, et al.. (2015). Joint 5D Pen Input for Light Field Displays. 637–647. 8 indexed citations
18.
Tompkin, James, et al.. (2012). Interactive viewpoint video textures. 11–17. 4 indexed citations
19.
Tompkin, James, Kwang In Kim, Jan Kautz, & Christian Theobalt. (2012). Videoscapes. ACM Transactions on Graphics. 31(4). 1–12. 27 indexed citations
20.
Tompkin, James, Fabrizio Pece, Kartic Subr, & Jan Kautz. (2011). Towards Moment Imagery: Automatic Cinemagraphs. 87–93. 30 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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