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.
On the Continuity of Rotation Representations in Neural Networks
2019637 citationsConnelly Barnes, Jingwan Lu et al.profile →
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
2017297 citationsPatsorn Sangkloy, Jingwan Lu et al.profile →
Zero-shot Image-to-Image Translation
2023176 citationsKrishna Kumar Singh, Yijun Li et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Jingwan Lu'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 Jingwan Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingwan Lu more than expected).
This network shows the impact of papers produced by Jingwan Lu. 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 Jingwan Lu. The network helps show where Jingwan Lu may publish in the future.
Co-authorship network of co-authors of Jingwan Lu
This figure shows the co-authorship network connecting the top 25 collaborators of Jingwan Lu.
A scholar is included among the top collaborators of Jingwan Lu 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 Jingwan Lu. Jingwan Lu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lu, Jingwan, et al.. (2021). Pose with style. ACM Transactions on Graphics. 40(6). 1–11.65 indexed citations
10.
Park, Taesung, Jun-Yan Zhu, Oliver Wang, et al.. (2020). Swapping Autoencoder for Deep Image Manipulation. Neural Information Processing Systems. 33. 7198–7211.13 indexed citations
Fišer, Jakub, et al.. (2019). 例によるビデオのスタイリング【JST・京大機械翻訳】. ACM Transactions on Graphics. 38(4). 1–11.1 indexed citations
13.
Raj, Amit, Cusuh Ham, Connelly Barnes, et al.. (2019). Learning to Generate Textures on 3D Meshes. Computer Vision and Pattern Recognition. 32–38.3 indexed citations
14.
Jamriška, Ondřej, Michal Lukáč, Jakub Fišer, et al.. (2019). Stylizing video by example. ACM Transactions on Graphics. 38(4). 1–11.55 indexed citations
15.
Raj, Amit, Patsorn Sangkloy, Huiwen Chang, et al.. (2018). SwapNet: Garment Transfer in Single View Images. 666–682.18 indexed citations
Jamriška, Ondřej, Jakub Fišer, Paul Asente, et al.. (2015). LazyFluids. ACM Transactions on Graphics. 34(4). 1–10.39 indexed citations
20.
Lu, Jingwan, et al.. (2014). RealPigment. 21–30.14 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.