Jungdam Won

1.1k total citations
27 papers, 604 citations indexed

About

Jungdam Won is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Jungdam Won has authored 27 papers receiving a total of 604 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Control and Systems Engineering, 22 papers in Computer Vision and Pattern Recognition and 7 papers in Computational Mechanics. Recurrent topics in Jungdam Won's work include Human Motion and Animation (24 papers), Human Pose and Action Recognition (21 papers) and Video Analysis and Summarization (9 papers). Jungdam Won is often cited by papers focused on Human Motion and Animation (24 papers), Human Pose and Action Recognition (21 papers) and Video Analysis and Summarization (9 papers). Jungdam Won collaborates with scholars based in South Korea, United States and Israel. Jungdam Won's co-authors include Jessica K. Hodgins, Jehee Lee, Deepak Gopinath, Jae-Dong Lee, Jehee Lee, Jong-Ho Park, Carol O’Sullivan, Kyungho Lee, Yuting Ye and Junyong Noh and has published in prestigious journals such as ACM Transactions on Graphics, Pattern Recognition and Computer Graphics Forum.

In The Last Decade

Jungdam Won

27 papers receiving 590 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jungdam Won South Korea	13	449	430	92	81	72	27	604
Myung Geol Choi South Korea	9	285 0.6×	312 0.7×	15 0.2×	40 0.5×	64 0.9×	23	418
Yunrong Guo Canada	8	173 0.4×	186 0.4×	67 0.7×	71 0.9×	19 0.3×	10	334
James M. Bern Switzerland	10	203 0.5×	67 0.2×	239 2.6×	36 0.4×	39 0.5×	15	401
Claudia Pérez-D’Arpino United States	8	187 0.4×	165 0.4×	48 0.5×	110 1.4×	10 0.1×	14	370
Umar Iqbal United States	11	264 0.6×	616 1.4×	111 1.2×	94 1.2×	98 1.4×	20	755
C. Karen Liu United States	9	403 0.9×	380 0.9×	63 0.7×	39 0.5×	46 0.6×	11	466
Philippe Beaudoin Canada	12	475 1.1×	490 1.1×	95 1.0×	65 0.8×	189 2.6×	18	729
Sebastian Höfer Germany	10	223 0.5×	216 0.5×	88 1.0×	90 1.1×	28 0.4×	21	447
H. Noborio Japan	11	159 0.4×	290 0.7×	52 0.6×	17 0.2×	32 0.4×	61	385
Oussama Kanoun France	10	528 1.2×	320 0.7×	334 3.6×	32 0.4×	17 0.2×	14	664

Countries citing papers authored by Jungdam Won

Since Specialization

Citations

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

Fields of papers citing papers by Jungdam Won

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jungdam Won

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

Lee, Jehee, et al.. (2025). MAGNET: Muscle Activation Generation Networks for Diverse Human Movement. 1–11. 1 indexed citations

2.

Won, Jungdam, et al.. (2025). Versatile Physics‐based Character Control with Hybrid Latent Representation. Computer Graphics Forum. 44(2). 1 indexed citations

3.

Hodgins, Jessica K., et al.. (2024). Strategy and Skill Learning for Physics-based Table Tennis Animation. Seoul National University Open Repository (Seoul National University). 1–11. 3 indexed citations

4.

Won, Jungdam, et al.. (2023). PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors. 1–10. 13 indexed citations

5.

Zhang, Yunbo, Deepak Gopinath, Yuting Ye, et al.. (2023). Simulation and Retargeting of Complex Multi-Character Interactions. 1–11. 12 indexed citations

6.

Won, Jungdam, et al.. (2023). ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters. Seoul National University Open Repository (Seoul National University). 1–11. 9 indexed citations

7.

Lee, Jehee, et al.. (2023). SAME: Skeleton-Agnostic Motion Embedding for Character Animation. Seoul National University Open Repository (Seoul National University). 1–11. 11 indexed citations

8.

Urbanek, Jack, et al.. (2023). Breaking The Limits of Text-conditioned 3D Motion Synthesis with Elaborative Descriptions. 2306–2316. 6 indexed citations

9.

Won, Jungdam, et al.. (2023). FastMimic: Model-Based Motion Imitation for Agile, Diverse and Generalizable Quadrupedal Locomotion. Robotics. 12(3). 90–90. 4 indexed citations

10.

Kim, Ji-Hoon, et al.. (2022). Conditional motion in-betweening. Pattern Recognition. 132. 108894–108894. 19 indexed citations

11.

Won, Jungdam, Deepak Gopinath, & Jessica K. Hodgins. (2022). Physics-based character controllers using conditional VAEs. ACM Transactions on Graphics. 41(4). 1–12. 56 indexed citations

12.

Gopinath, Deepak, Hanbyul Joo, & Jungdam Won. (2022). Motion In-betweening for Physically Simulated Characters. 1–2. 5 indexed citations

13.

Won, Jungdam, Deepak Gopinath, & Jessica K. Hodgins. (2021). Control strategies for physically simulated characters performing two-player competitive sports. ACM Transactions on Graphics. 40(4). 1–11. 49 indexed citations

14.

Won, Jungdam, Deepak Gopinath, & Jessica K. Hodgins. (2021). Control strategies for physically simulated characters performing two-player competitive sports. ACM Transactions on Graphics. 40(4). 1–11. 4 indexed citations

15.

Won, Jungdam, Deepak Gopinath, & Jessica K. Hodgins. (2020). A scalable approach to control diverse behaviors for physically simulated characters. ACM Transactions on Graphics. 39(4). 94 indexed citations

16.

Won, Jungdam & Jehee Lee. (2019). Learning body shape variation in physics-based characters. ACM Transactions on Graphics. 38(6). 1–12. 60 indexed citations

17.

Won, Jungdam, et al.. (2019). SoftCon. ACM Transactions on Graphics. 38(6). 1–12. 49 indexed citations

18.

Won, Jungdam, et al.. (2017). How to train your dragon. ACM Transactions on Graphics. 36(6). 1–13. 36 indexed citations

19.

Won, Jungdam, Kyungho Lee, Carol O’Sullivan, Jessica K. Hodgins, & Jehee Lee. (2014). Generating and ranking diverse multi-character interactions. ACM Transactions on Graphics. 33(6). 1–12. 37 indexed citations

20.

Won, Jungdam, et al.. (2013). Data-driven control of flapping flight. ACM Transactions on Graphics. 32(5). 1–12. 32 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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