Mathis Petrovich

1.0k total citations · 1 hit paper
5 papers, 338 citations indexed

About

Mathis Petrovich is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Mathis Petrovich has authored 5 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Control and Systems Engineering, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Mathis Petrovich's work include Human Pose and Action Recognition (4 papers), Human Motion and Animation (4 papers) and Machine Learning in Bioinformatics (1 paper). Mathis Petrovich is often cited by papers focused on Human Pose and Action Recognition (4 papers), Human Motion and Animation (4 papers) and Machine Learning in Bioinformatics (1 paper). Mathis Petrovich collaborates with scholars based in France, Germany and United States. Mathis Petrovich's co-authors include Gül Varol, Michael J. Black, Michael J. Black, Eiryo Kawakami, Dinesh Singh, Makoto Yamada, Héctor Climente-González, Umar Iqbal, Xue Bin Peng and Davis Rempe and has published in prestigious journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), HAL (Le Centre pour la Communication Scientifique Directe) and arXiv (Cornell University).

In The Last Decade

Mathis Petrovich

5 papers receiving 324 citations

Hit Papers

align trajectories

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.

Action-Conditioned 3D Human Motion Synthesis with Transformer VAE

2021 259 citations Mathis Petrovich, Michael J. Black et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mathis Petrovich France	4	283	232	57	47	35	5	338
Shihao Zou Canada	7	372 1.3×	293 1.3×	69 1.2×	72 1.5×	41 1.2×	15	459
Xuefei Zhe China	9	233 0.8×	64 0.3×	64 1.1×	46 1.0×	57 1.6×	14	293
Hung Yu Ling Canada	4	192 0.7×	178 0.8×	33 0.6×	33 0.7×	8 0.2×	6	250
C. Karen Liu United States	5	451 1.6×	468 2.0×	48 0.8×	46 1.0×	20 0.6×	6	534
Deepak Gopinath United States	5	177 0.6×	180 0.8×	30 0.5×	33 0.7×	10 0.3×	8	223
Xiangyang Ji China	7	161 0.6×	40 0.2×	23 0.4×	15 0.3×	59 1.7×	18	194
Iván Huerta Spain	10	229 0.8×	32 0.1×	15 0.3×	24 0.5×	22 0.6×	14	268
Bharat Lal Bhatnagar Germany	6	276 1.0×	94 0.4×	198 3.5×	21 0.4×	26 0.7×	11	343
Konpat Preechakul Thailand	4	152 0.5×	54 0.2×	24 0.4×	63 1.3×	8 0.2×	4	211
Artsiom Sanakoyeu Germany	8	282 1.0×	39 0.2×	26 0.5×	66 1.4×	15 0.4×	13	324

Countries citing papers authored by Mathis Petrovich

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Mathis Petrovich

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

All Works

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5 of 5 papers shown

1.

Petrovich, Mathis, et al.. (2024). A Cross-Dataset Study for Text-based 3D Human Motion Retrieval. 1932–1940. 3 indexed citations

2.

Petrovich, Mathis, Or Litany, Umar Iqbal, et al.. (2024). Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation. SPIRE - Sciences Po Institutional REpository. 1911–1921. 8 indexed citations

3.

Singh, Dinesh, Héctor Climente-González, Mathis Petrovich, Eiryo Kawakami, & Makoto Yamada. (2023). FsNet: Feature Selection Network on High-dimensional Biological Data. arXiv (Cornell University). 1–9. 16 indexed citations

4.

Petrovich, Mathis, et al.. (2022). TEACH: Temporal Action Composition for 3D Humans. HAL (Le Centre pour la Communication Scientifique Directe). 414–423. 52 indexed citations

5.

Petrovich, Mathis, Michael J. Black, & Gül Varol. (2021). Action-Conditioned 3D Human Motion Synthesis with Transformer VAE. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 10965–10975. 259 indexed citations breakdown →

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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