Andreas Lehrmann

1.1k total citations · 1 hit paper
8 papers, 562 citations indexed

About

Andreas Lehrmann is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design. According to data from OpenAlex, Andreas Lehrmann has authored 8 papers receiving a total of 562 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 2 papers in Computer Graphics and Computer-Aided Design. Recurrent topics in Andreas Lehrmann's work include Advanced Vision and Imaging (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Computer Graphics and Visualization Techniques (2 papers). Andreas Lehrmann is often cited by papers focused on Advanced Vision and Imaging (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Computer Graphics and Visualization Techniques (2 papers). Andreas Lehrmann collaborates with scholars based in Germany, United Kingdom and United States. Andreas Lehrmann's co-authors include Gabriel Schwartz, Yaser Sheikh, Stephen Lombardi, Tomas Simon, Jason Saragih, Sebastian Nowozin, Peter Gehler, Leonid Sigal, Polina Zablotskaia and Kay Nieselt and has published in prestigious journals such as ACM Transactions on Graphics, Data Mining and Knowledge Discovery and arXiv (Cornell University).

In The Last Decade

Andreas Lehrmann

8 papers receiving 544 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.

Neural volumes

2019 418 citations Stephen Lombardi, Tomas Simon et al. ACM Transactions on Graphics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Andreas Lehrmann Germany	4	492	277	231	78	56	8	562
Qing Shuai China	12	734 1.5×	367 1.3×	515 2.2×	103 1.3×	54 1.0×	27	867
Fanbo Xiang United States	5	448 0.9×	296 1.1×	203 0.9×	43 0.6×	31 0.6×	9	541
Timur Bagautdinov United States	10	477 1.0×	139 0.5×	199 0.9×	56 0.7×	34 0.6×	19	589
Ryota Natsume Japan	3	679 1.4×	317 1.1×	549 2.4×	67 0.9×	36 0.6×	3	788
Kripasindhu Sarkar Germany	9	311 0.6×	129 0.5×	207 0.9×	85 1.1×	19 0.3×	22	382
Michael Zollhoefer United States	8	450 0.9×	256 0.9×	284 1.2×	77 1.0×	12 0.2×	13	560
Yuanqing Zhang China	7	430 0.9×	253 0.9×	300 1.3×	56 0.7×	12 0.2×	13	538
Matheus Gadelha United States	7	314 0.6×	160 0.6×	233 1.0×	27 0.3×	25 0.4×	21	434
Kwan-Yee Lin China	12	502 1.0×	76 0.3×	144 0.6×	65 0.8×	37 0.7×	18	598
Tanner Schmidt United States	9	382 0.8×	124 0.4×	108 0.5×	126 1.6×	30 0.5×	11	496

Countries citing papers authored by Andreas Lehrmann

Since Specialization

Citations

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

Fields of papers citing papers by Andreas Lehrmann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andreas Lehrmann

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Zablotskaia, Polina, et al.. (2021). PROVIDE: A Probabilistic Framework for Unsupervised Video Decomposition. Uncertainty in Artificial Intelligence. 2 indexed citations

2.

Matthews, Iain, et al.. (2019). Structural Decompositions for End-to-End Relighting.. arXiv (Cornell University). 1 indexed citations

3.

Lombardi, Stephen, Tomas Simon, Jason Saragih, et al.. (2019). Neural volumes. ACM Transactions on Graphics. 38(4). 1–14. 418 indexed citations breakdown →

4.

Mori, Greg, et al.. (2018). Variational Autoencoders with Jointly Optimized Latent Dependency Structure. International Conference on Learning Representations. 1 indexed citations

5.

Lehrmann, Andreas & Leonid Sigal. (2017). Non-parametric Structured Output Networks. Neural Information Processing Systems. 30. 4214–4224. 2 indexed citations

6.

Lehrmann, Andreas, Peter Gehler, & Sebastian Nowozin. (2014). Efficient Nonlinear Markov Models for Human Motion. 1314–1321. 107 indexed citations

7.

Lehrmann, Andreas, Peter Gehler, & Sebastian Nowozin. (2013). A Non-parametric Bayesian Network Prior of Human Pose. 1281–1288. 27 indexed citations

8.

Lehrmann, Andreas, et al.. (2012). Visualizing dimensionality reduction of systems biology data. Data Mining and Knowledge Discovery. 27(1). 146–165. 4 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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