Philippe Weinzaepfel

5.9k total citations
27 papers, 1000 citations indexed

About

Philippe Weinzaepfel is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Philippe Weinzaepfel has authored 27 papers receiving a total of 1000 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 9 papers in Aerospace Engineering and 8 papers in Control and Systems Engineering. Recurrent topics in Philippe Weinzaepfel's work include Advanced Image and Video Retrieval Techniques (10 papers), Human Pose and Action Recognition (9 papers) and Robotics and Sensor-Based Localization (9 papers). Philippe Weinzaepfel is often cited by papers focused on Advanced Image and Video Retrieval Techniques (10 papers), Human Pose and Action Recognition (9 papers) and Robotics and Sensor-Based Localization (9 papers). Philippe Weinzaepfel collaborates with scholars based in South Korea, France and Spain. Philippe Weinzaepfel's co-authors include Cordelia Schmid, Jérôme Revaud, Grégory Rogez, Karteek Alahari, Zaïd Harchaoui, Martin Humenberger, Hervé Jeǵou, Patrick Pérez, César Roberto de Souza and Vicky Kalogeiton and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Computer Vision and Image Understanding.

In The Last Decade

Philippe Weinzaepfel

22 papers receiving 967 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Philippe Weinzaepfel South Korea	12	886	267	217	91	68	27	1000
Weichao Qiu United States	10	480 0.5×	130 0.5×	202 0.9×	35 0.4×	23 0.3×	23	630
Tianshan Liu Hong Kong	16	445 0.5×	209 0.8×	84 0.4×	95 1.0×	44 0.6×	54	658
Laura Sevilla-Lara United States	6	817 0.9×	313 1.2×	116 0.5×	69 0.8×	18 0.3×	13	919
Cam-Hao Hua South Korea	17	414 0.5×	638 2.4×	250 1.2×	141 1.5×	74 1.1×	36	1.1k
Thanh-Toan Do Australia	19	608 0.7×	244 0.9×	163 0.8×	55 0.6×	27 0.4×	52	895
Matt Feiszli Israel	10	698 0.8×	465 1.7×	47 0.2×	91 1.0×	58 0.9×	20	942
Karteek Alahari France	13	704 0.8×	273 1.0×	55 0.3×	64 0.7×	45 0.7×	26	801
Fangneng Zhan Singapore	17	787 0.9×	170 0.6×	33 0.2×	47 0.5×	25 0.4×	29	941
Weijian Xu China	8	346 0.4×	144 0.5×	40 0.2×	59 0.6×	57 0.8×	28	481
Joo Kooi Tan Japan	11	591 0.7×	146 0.5×	74 0.3×	234 2.6×	132 1.9×	187	807

Countries citing papers authored by Philippe Weinzaepfel

Since Specialization

Citations

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

Fields of papers citing papers by Philippe Weinzaepfel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philippe Weinzaepfel

This figure shows the co-authorship network connecting the top 25 collaborators of Philippe Weinzaepfel. A scholar is included among the top collaborators of Philippe Weinzaepfel 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 Philippe Weinzaepfel. Philippe Weinzaepfel 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

Weinzaepfel, Philippe, et al.. (2025). Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors. 1071–1081.

Weinzaepfel, Philippe, et al.. (2025). MASt3R-SfM: A Fully-Integrated Solution for Unconstrained Structure-from-Motion. 1–10. 1 indexed citations

Leroy, Vincent, et al.. (2024). SHOWMe: Robust object-agnostic hand-object 3D reconstruction from RGB video. Computer Vision and Image Understanding. 247. 104073–104073. 4 indexed citations

Lucas, Thomas G., et al.. (2024). Purposer: Putting Human Motion Generation in Context. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 1310–1319.

Weinzaepfel, Philippe, et al.. (2024). PoseScript: Linking 3D Human Poses and Natural Language. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(7). 5146–5159.

Baradel, Fabien, et al.. (2024). Cross-View and Cross-Pose Completion for 3D Human Understanding. 1512–1523.

Revaud, Jérôme, et al.. (2024). SACReg: Scene-Agnostic Coordinate Regression for Visual Localization. 688–698. 5 indexed citations

Weinzaepfel, Philippe, Thomas G. Lucas, Vincent Leroy, et al.. (2023). CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. 17923–17934. 39 indexed citations

Leroy, Vincent, et al.. (2023). SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction. 1927–1936. 6 indexed citations

10.

Revaud, Jérôme, Vincent Leroy, Philippe Weinzaepfel, & Boris Chidlovskii. (2022). PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3916–3926. 8 indexed citations

11.

Chidlovskii, Boris, et al.. (2022). CroCo: Self-Supervised Pre-Training for 3D Vision Tasks by Cross-View Completion. 3502–3516.

12.

Du, Yuming, et al.. (2022). Multi-Finger Grasping Like Humans. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 1564–1570. 1 indexed citations

13.

Groueix, Thibault, et al.. (2022). PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(11). 1–17. 12 indexed citations

14.

Kalantidis, Yannis, et al.. (2020). Hard Negative Mixing for Contrastive Learning. Neural Information Processing Systems. 33. 21798–21809. 24 indexed citations

15.

Weinzaepfel, Philippe, et al.. (2020). SuperLoss: A Generic Loss for Robust Curriculum Learning. Neural Information Processing Systems. 33. 4308–4319. 24 indexed citations

16.

Revaud, Jérôme, et al.. (2019). R2D2: Reliable and Repeatable Detectors and Descriptors for Joint Sparse Keypoint Detection and Local Feature Extraction. arXiv (Cornell University). 3 indexed citations

17.

Revaud, Jérôme, César Roberto de Souza, Martin Humenberger, & Philippe Weinzaepfel. (2019). R2D2: Reliable and Repeatable Detector and Descriptor. Neural Information Processing Systems. 32. 12405–12415. 102 indexed citations

18.

Rogez, Grégory, Philippe Weinzaepfel, & Cordelia Schmid. (2019). LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(5). 1–1. 157 indexed citations

19.

Kalogeiton, Vicky, Philippe Weinzaepfel, Vittorio Ferrari, & Cordelia Schmid. (2017). International Conference on Computer Vision (ICCV 2017). 90 indexed citations

20.

Revaud, Jérôme, Philippe Weinzaepfel, Zaïd Harchaoui, & Cordelia Schmid. (2015). Deep Convolutional Matching. arXiv (Cornell University). 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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