Emre Aksan

1.2k total citations
13 papers, 591 citations indexed

About

Emre Aksan is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Emre Aksan has authored 13 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 7 papers in Control and Systems Engineering and 3 papers in Artificial Intelligence. Recurrent topics in Emre Aksan's work include Human Motion and Animation (6 papers), Human Pose and Action Recognition (6 papers) and Neural dynamics and brain function (2 papers). Emre Aksan is often cited by papers focused on Human Motion and Animation (6 papers), Human Pose and Action Recognition (6 papers) and Neural dynamics and brain function (2 papers). Emre Aksan collaborates with scholars based in Switzerland, United States and United Kingdom. Emre Aksan's co-authors include Otmar Hilliges, Manuel Kaufmann, Peng Cao, Michael J. Black, Partha Ghosh, Yinghao Huang, Gerard Pons‐Moll, Jie Song, Fabrizio Pece and Muhammed Kocabas and has published in prestigious journals such as ACM Transactions on Graphics, IEEE Robotics and Automation Letters and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Emre Aksan

13 papers receiving 571 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emre Aksan Switzerland	10	449	279	138	103	92	13	591
Manuel Kaufmann Germany	8	380 0.8×	207 0.7×	107 0.8×	104 1.0×	50 0.5×	19	509
Panna Felsen United States	4	860 1.9×	341 1.2×	144 1.0×	70 0.7×	164 1.8×	6	958
Srinath Sridhar Germany	3	713 1.6×	213 0.8×	129 0.9×	278 2.7×	88 1.0×	5	846
Umar Iqbal United States	11	616 1.4×	264 0.9×	111 0.8×	177 1.7×	94 1.0×	20	755
Sebastian Starke United Kingdom	13	651 1.4×	669 2.4×	136 1.0×	93 0.9×	88 1.0×	22	885
Lin Gui China	3	461 1.0×	78 0.3×	74 0.5×	90 0.9×	54 0.6×	4	530
Jack M. Wang Canada	12	537 1.2×	567 2.0×	325 2.4×	68 0.7×	93 1.0×	13	892
Michalis Raptis United States	10	649 1.4×	126 0.5×	159 1.2×	154 1.5×	233 2.5×	12	719
Julieta Martínez Canada	4	829 1.8×	136 0.5×	210 1.5×	250 2.4×	143 1.6×	7	905
Yuliang Xiu Germany	9	529 1.2×	104 0.4×	144 1.0×	77 0.7×	74 0.8×	13	709

Countries citing papers authored by Emre Aksan

Since Specialization

Citations

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

Fields of papers citing papers by Emre Aksan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emre Aksan

This figure shows the co-authorship network connecting the top 25 collaborators of Emre Aksan. A scholar is included among the top collaborators of Emre Aksan 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 Emre Aksan. Emre Aksan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Karunratanakul, Korrawe, Konpat Preechakul, Emre Aksan, et al.. (2024). Optimizing Diffusion Noise Can Serve As Universal Motion Priors. 1334–1345. 9 indexed citations

2.

Kocabas, Muhammed, et al.. (2024). Physically Plausible Full-Body Hand-Object Interaction Synthesis. 464–473. 10 indexed citations

3.

Aksan, Emre, et al.. (2023). STCN: Stochastic Temporal Convolutional Networks. Repository for Publications and Research Data (ETH Zurich). 1 indexed citations

4.

Kocabas, Muhammed, et al.. (2022). D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 20545–20554. 40 indexed citations

5.

Wang, Xi, et al.. (2022). Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors. 353–362. 12 indexed citations

6.

Aksan, Emre, Manuel Kaufmann, Peng Cao, & Otmar Hilliges. (2021). A Spatio-temporal Transformer for 3D Human Motion Prediction. 565–574. 135 indexed citations

7.

Jendele, Lukáš, et al.. (2021). Learning Functionally Decomposed Hierarchies for Continuous Control Tasks With Path Planning. IEEE Robotics and Automation Letters. 6(2). 3623–3630. 11 indexed citations

8.

Aksan, Emre, Peng Cao, Manuel Kaufmann, & Otmar Hilliges. (2020). Attention, please: A Spatio-temporal Transformer for 3D Human Motion Prediction. arXiv (Cornell University). 13 indexed citations

9.

Aksan, Emre, Fabrizio Pece, & Otmar Hilliges. (2018). DeepWriting. 1–14. 38 indexed citations

10.

Huang, Yinghao, Manuel Kaufmann, Emre Aksan, et al.. (2018). Deep inertial poser. ACM Transactions on Graphics. 37(6). 1–15. 192 indexed citations

11.

Ghosh, Partha, Jie Song, Emre Aksan, & Otmar Hilliges. (2017). Learning Human Motion Models for Long-Term Predictions. 458–466. 126 indexed citations

12.

Aksan, Emre, et al.. (2014). Functional networks of anatomic brain regions. 53–60. 1 indexed citations

13.

Fırat, Orhan, et al.. (2014). Large Scale Functional Connectivity for Brain Decoding. 3 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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