Dan Casas

5.5k total citations · 3 hit papers
45 papers, 2.9k citations indexed

About

Dan Casas is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Computational Mechanics. According to data from OpenAlex, Dan Casas has authored 45 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computer Vision and Pattern Recognition, 23 papers in Control and Systems Engineering and 19 papers in Computational Mechanics. Recurrent topics in Dan Casas's work include Human Motion and Animation (21 papers), Human Pose and Action Recognition (20 papers) and 3D Shape Modeling and Analysis (19 papers). Dan Casas is often cited by papers focused on Human Motion and Animation (21 papers), Human Pose and Action Recognition (20 papers) and 3D Shape Modeling and Analysis (19 papers). Dan Casas collaborates with scholars based in Spain, Germany and United States. Dan Casas's co-authors include Christian Theobalt, Oleksandr Sotnychenko, Dushyant Mehta, Helge Rhodin, Weipeng Xu, Miguel Á. Otaduy, Hans‐Peter Seidel, Igor Santesteban, Mohammad Shafiei and Pascal Fua and has published in prestigious journals such as ACM Transactions on Graphics, International Journal of Computer Vision and IEEE Transactions on Cybernetics.

In The Last Decade

Dan Casas

44 papers receiving 2.8k 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.

VNect

2017 690 citations Dushyant Mehta, Oleksandr Sotnychenko et al. ACM Transactions on Graphics profile →
Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision

2017 609 citations Dushyant Mehta, Helge Rhodin et al. profile →
GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB

2018 317 citations Oleksandr Sotnychenko, Dushyant Mehta et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dan Casas Spain	21	2.3k	838	762	738	395	45	2.9k
Javier Romero Germany	15	2.3k 1.0×	769 0.9×	752 1.0×	692 0.9×	202 0.5×	44	2.9k
Weipeng Xu Germany	22	3.2k 1.4×	1.1k 1.3×	607 0.8×	740 1.0×	441 1.1×	53	3.7k
Naureen Mahmood Germany	7	2.0k 0.9×	1.4k 1.6×	246 0.3×	642 0.9×	473 1.2×	12	2.6k
Matthew Loper Germany	12	2.4k 1.0×	1.6k 1.9×	257 0.3×	673 0.9×	590 1.5×	18	3.0k
Dushyant Mehta Germany	12	2.0k 0.9×	358 0.4×	649 0.9×	474 0.6×	69 0.2×	13	2.2k
Oleksandr Sotnychenko Germany	9	1.9k 0.8×	297 0.4×	734 1.0×	493 0.7×	38 0.1×	10	2.1k
Hanbyul Joo South Korea	13	1.3k 0.6×	603 0.7×	190 0.2×	280 0.4×	262 0.7×	29	1.6k
Gerard Pons‐Moll Germany	29	5.2k 2.2×	3.2k 3.8×	558 0.7×	1.3k 1.7×	1.6k 3.9×	61	6.4k
Georgios Pavlakos United States	15	1.7k 0.7×	447 0.5×	354 0.5×	365 0.5×	66 0.2×	27	1.9k
Jinxiang Chai United States	36	3.7k 1.6×	577 0.7×	390 0.5×	2.3k 3.2×	364 0.9×	56	4.2k

Countries citing papers authored by Dan Casas

Since Specialization

Citations

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

Fields of papers citing papers by Dan Casas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Casas

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Casas. A scholar is included among the top collaborators of Dan Casas 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 Dan Casas. Dan Casas 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:

20 of 20 papers shown

1.

Casas, Dan, et al.. (2025). Detecting anomalies in dense 3D crowds. Computers & Graphics. 130. 104267–104267. 2 indexed citations

2.

Casas, Dan, et al.. (2025). Accurate hand contact detection from RGB images via image-to-image translation. Computers & Graphics. 128. 104200–104200. 1 indexed citations

3.

Olivier, Anne‐Hélène, et al.. (2024). Resolving Collisions in Dense 3D Crowd Animations. ACM Transactions on Graphics. 43(5). 1–14. 1 indexed citations

4.

Casas, Dan, et al.. (2024). TexTile: A Differentiable Metric for Texture Tileability. 4439–4449. 3 indexed citations

5.

Casas, Dan, et al.. (2023). How Will It Drape Like? Capturing Fabric Mechanics from Depth Images. Computer Graphics Forum. 42(2). 149–160. 8 indexed citations

6.

Casas, Dan, et al.. (2023). Learning Contact Deformations with General Collider Descriptors. 1–10.

7.

Casas, Dan, et al.. (2022). PERGAMO: Personalized 3D Garments from Monocular Video. Computer Graphics Forum. 41(8). 293–304. 10 indexed citations

8.

Casas, Dan, et al.. (2022). Contact-centric deformation learning. ACM Transactions on Graphics. 41(4). 1–11. 11 indexed citations

9.

Garcés, Elena, et al.. (2022). A Survey on Intrinsic Images: Delving Deep into Lambert and Beyond. International Journal of Computer Vision. 130(3). 836–868. 26 indexed citations

10.

Casas, Dan, et al.. (2021). Learning contact corrections for handle-based subspace dynamics. ACM Transactions on Graphics. 40(4). 1–12. 24 indexed citations

11.

Santesteban, Igor, et al.. (2020). Fully Convolutional Graph Neural Networks for Parametric Virtual Try‐On. Computer Graphics Forum. 39(8). 145–156. 47 indexed citations

12.

Casas, Dan, et al.. (2017). Multi-view Performance Capture of Surface Details. International Journal of Computer Vision. 124(1). 96–113. 13 indexed citations

13.

Mehta, Dushyant, Helge Rhodin, Dan Casas, et al.. (2017). Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision. 506–516. 609 indexed citations breakdown →

14.

Mehta, Dushyant, Helge Rhodin, Dan Casas, et al.. (2016). Monocular 3D Human Pose Estimation Using Transfer Learning and Improved CNN Supervision. arXiv (Cornell University). 17 indexed citations

15.

Rhodin, Helge, Christian Richardt, Dan Casas, et al.. (2016). EgoCap. ACM Transactions on Graphics. 35(6). 1–11. 96 indexed citations

16.

Casas, Dan, et al.. (2016). Model-Based Outdoor Performance Capture. 166–175. 28 indexed citations

17.

Casas, Dan, Christian Richardt, John Collomosse, Christian Theobalt, & Adrian Hilton. (2015). 4D Model Flow: Precomputed Appearance Alignment for Real-time 4D Video Interpolation. Computer Graphics Forum. 34(7). 173–182. 13 indexed citations

18.

Casas, Dan, Marco Volino, John Collomosse, & Adrian Hilton. (2014). 4D video textures for interactive character appearance. Computer Graphics Forum. 33(2). 371–380. 57 indexed citations

19.

Casas, Dan, et al.. (2013). Animation Control of Surface Motion Capture. IEEE Transactions on Cybernetics. 43(6). 1532–1545. 8 indexed citations

20.

Casas, Dan, et al.. (2012). Interactive Animation of 4D Performance Capture. IEEE Transactions on Visualization and Computer Graphics. 19(5). 762–773. 21 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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