Mathis Petrovich
Impact in
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- Human Pose and Action Recognition
- Video Analysis and Summarization
- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
- Advanced Vision and Imaging
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- Human Motion and Animation
Papers in
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- Human Motion and Animation 4
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- Human Pose and Action Recognition 4
- Video Analysis and Summarization 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Gül Varol (4 shared papers)Michael J. Black (1 shared paper)Michael J. Black (2 shared papers)Makoto Yamada (1 shared paper)Héctor Climente-González (1 shared paper)Dinesh Singh (1 shared paper)Eiryo Kawakami (1 shared paper)Umar Iqbal (1 shared paper)
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (2 papers)arXiv (Cornell University) (1 paper)
In The Last Decade
Mathis Petrovich
5 papers receiving 361 citations
Mathis Petrovich's Hit Papers
Peers
Comparison fields: 5 of 40
- Computer Vision and Pattern Recognition 304
- Control and Systems Engineering 254
- Human-Computer Interaction 38
- Computational Mechanics 61
- Signal Processing 28
Countries citing papers authored by Mathis Petrovich
This map shows the geographic impact of Mathis Petrovich'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 Mathis Petrovich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathis Petrovich more than expected).
Fields of papers citing papers by Mathis Petrovich
This network shows the impact of papers produced by Mathis Petrovich. 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 Mathis Petrovich. The network helps show where Mathis Petrovich may publish in the future.
Co-authors
The 11 scholars most cited alongside Mathis Petrovich, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Action-Conditioned 3D Human Motion Synthesis with Transformer VAE Hit paper breakdown → | 2021 | 281 |
| 2 | 2022 | 60 | |
| 3 | 2023 | 17 | |
| 4 | 2024 | 13 | |
| 5 | 2024 | 3 |
About Mathis Petrovich
Mathis Petrovich is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Molecular Biology, Artificial Intelligence and Human-Computer Interaction, having authored 5 papers that have together received 374 indexed citations. Recurring topics across this work include Human Motion and Animation (4 papers), Human Pose and Action Recognition (4 papers), Hand Gesture Recognition Systems (1 paper), Video Analysis and Summarization (1 paper), Gene expression and cancer classification (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Neural Networks and Applications (1 paper) and Machine Learning in Bioinformatics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (304 citations), Control and Systems Engineering (254 citations), Human-Computer Interaction (38 citations), Computational Mechanics (61 citations) and Signal Processing (28 citations). Mathis Petrovich has collaborated with scholars based in France, Germany and Japan. Frequent co-authors include Gül Varol, Michael J. Black, Michael J. Black, Makoto Yamada, Héctor Climente-González, Dinesh Singh, Eiryo Kawakami, Umar Iqbal, Xue Bin Peng and Davis Rempe. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), HAL (Le Centre pour la Communication Scientifique Directe) and arXiv (Cornell University).
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.