Davis Rempe

598 total citations
11 papers, 194 citations indexed

About

Davis Rempe is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Control and Systems Engineering. According to data from OpenAlex, Davis Rempe has authored 11 papers receiving a total of 194 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 6 papers in Computational Mechanics and 5 papers in Control and Systems Engineering. Recurrent topics in Davis Rempe's work include 3D Shape Modeling and Analysis (6 papers), Human Pose and Action Recognition (5 papers) and Human Motion and Animation (3 papers). Davis Rempe is often cited by papers focused on 3D Shape Modeling and Analysis (6 papers), Human Pose and Action Recognition (5 papers) and Human Motion and Animation (3 papers). Davis Rempe collaborates with scholars based in United States, United Kingdom and Germany. Davis Rempe's co-authors include Or Litany, Leonidas Guibas, Sanja Fidler, Jonah Philion, Marco Pavone, Baishakhi Ray, Danfei Xu, Sushant Veer, Ziyuan Zhong and Yuxiao Chen and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Repository for Publications and Research Data (ETH Zurich) and Neural Information Processing Systems.

In The Last Decade

Davis Rempe

11 papers receiving 186 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Davis Rempe United States	6	85	71	68	49	23	11	194
Jinghao Miao United States	8	186 2.2×	86 1.2×	122 1.8×	30 0.6×	43 1.9×	16	270
Jonah Philion Canada	5	57 0.7×	24 0.3×	67 1.0×	30 0.6×	7 0.3×	5	143
Phillip Karle Germany	6	127 1.5×	48 0.7×	76 1.1×	41 0.8×	20 0.9×	12	204
Weijing Shi China	5	77 0.9×	23 0.3×	61 0.9×	17 0.3×	10 0.4×	13	162
Stefan Orf Germany	4	130 1.5×	46 0.6×	82 1.2×	24 0.5×	22 1.0×	13	214
Christoph Schöller Germany	6	159 1.9×	26 0.4×	117 1.7×	79 1.6×	33 1.4×	6	258
Yingjuan Tang China	6	159 1.9×	49 0.7×	79 1.2×	23 0.5×	34 1.5×	13	264
Apratim Bhattacharyya Germany	6	148 1.7×	23 0.3×	175 2.6×	84 1.7×	36 1.6×	11	262
Onay Urfalıoǧlu Germany	6	90 1.1×	34 0.5×	128 1.9×	74 1.5×	17 0.7×	16	248
Srikanth Malla United States	6	148 1.7×	21 0.3×	179 2.6×	68 1.4×	23 1.0×	7	280

Countries citing papers authored by Davis Rempe

Since Specialization

Citations

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

Fields of papers citing papers by Davis Rempe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Davis Rempe

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

All Works

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11 of 11 papers shown

Liu, Jiateng, et al.. (2024). CurveCloudNet: Processing Point Clouds with 1D Structure. abs/2206.12073. 27981–27991. 1 indexed citations

Rempe, Davis, et al.. (2024). NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis. 947–957. 7 indexed citations

Petrovich, Mathis, Or Litany, Umar Iqbal, et al.. (2024). Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation. SPIRE - Sciences Po Institutional REpository. 1911–1921. 8 indexed citations

Zhong, Ziyuan, Davis Rempe, Danfei Xu, et al.. (2023). Guided Conditional Diffusion for Controllable Traffic Simulation. 3560–3566. 48 indexed citations

Pan, Boxiao, Bokui Shen, Davis Rempe, et al.. (2023). COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos. 5239–5249. 3 indexed citations

Rempe, Davis, Zhengyi Luo, Xue Bin Peng, et al.. (2023). Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion. 13756–13766. 37 indexed citations

Rempe, Davis, Jonah Philion, Leonidas Guibas, Sanja Fidler, & Or Litany. (2022). Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 17284–17294. 75 indexed citations

Rempe, Davis, et al.. (2021). CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations. Repository for Publications and Research Data (ETH Zurich). 33(20). 13688–13701. 4 indexed citations

Rempe, Davis, Srinath Sridhar, He Wang, & Leonidas Guibas. (2020). Predicting the Physical Dynamics of Unseen 3D Objects. 2823–2832. 4 indexed citations

10.

Rempe, Davis, Srinath Sridhar, He Wang, & Leonidas Guibas. (2019). Learning Generalizable Final-State Dynamics of 3D Rigid Objects. Computer Vision and Pattern Recognition. 17–20. 2 indexed citations

11.

Sridhar, Srinath, et al.. (2019). Multiview Aggregation for Learning Category-Specific Shape Reconstruction. Neural Information Processing Systems. 32(20). 2351–2362. 5 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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