Shubham Tulsiani

4.7k total citations · 2 hit papers
36 papers, 1.6k citations indexed

About

Shubham Tulsiani is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Computational Mechanics. According to data from OpenAlex, Shubham Tulsiani has authored 36 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 12 papers in Control and Systems Engineering and 7 papers in Computational Mechanics. Recurrent topics in Shubham Tulsiani's work include Advanced Vision and Imaging (13 papers), Human Pose and Action Recognition (11 papers) and Robot Manipulation and Learning (10 papers). Shubham Tulsiani is often cited by papers focused on Advanced Vision and Imaging (13 papers), Human Pose and Action Recognition (11 papers) and Robot Manipulation and Learning (10 papers). Shubham Tulsiani collaborates with scholars based in United States, Israel and India. Shubham Tulsiani's co-authors include Jitendra Malik, João Carreira, Christian Häne, Abhishek Kar, Leonidas Guibas, Abhinav Gupta, Justin Johnson, David Novotný, Steve Branson and Christoph Lassner and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition Letters and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Shubham Tulsiani

32 papers receiving 1.6k citations

Hit Papers

align trajectories sort by overperformance

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.

Accelerating 3D deep learning with PyTorch3D

2020 334 citations Justin Johnson, Nikhila Ravi et al. profile →
SparseFusion: Distilling View-Conditioned Diffusion for 3D Reconstruction

2023 89 citations Shubham Tulsiani et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shubham Tulsiani United States	17	1.2k	696	442	321	276	36	1.6k
Mathieu Aubry France	14	1.1k 0.9×	603 0.9×	303 0.7×	279 0.9×	299 1.1×	30	1.6k
Tianjia Shao China	19	847 0.7×	435 0.6×	244 0.6×	148 0.5×	240 0.9×	53	1.1k
Lingjie Liu United States	22	1.1k 0.9×	666 1.0×	645 1.5×	120 0.4×	149 0.5×	57	1.6k
Sida Peng China	19	1.6k 1.3×	758 1.1×	605 1.4×	560 1.7×	628 2.3×	63	2.1k
Leslie Ikemoto United States	8	966 0.8×	339 0.5×	266 0.6×	233 0.7×	268 1.0×	10	1.4k
Ersin Yumer United States	17	1.4k 1.2×	712 1.0×	631 1.4×	116 0.4×	81 0.3×	30	1.9k
Sameh Khamis United States	14	1.5k 1.3×	680 1.0×	629 1.4×	192 0.6×	264 1.0×	23	1.8k
Daniel Ritchie United States	14	574 0.5×	475 0.7×	361 0.8×	92 0.3×	134 0.5×	36	1.1k
Siddhartha Chaudhuri United States	24	1.4k 1.2×	1.6k 2.2×	1.0k 2.3×	162 0.5×	198 0.7×	39	2.4k
Matthias NieBner Germany	14	1.8k 1.4×	699 1.0×	419 0.9×	743 2.3×	117 0.4×	17	2.3k

Countries citing papers authored by Shubham Tulsiani

Since Specialization

Citations

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

Fields of papers citing papers by Shubham Tulsiani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shubham Tulsiani

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

Tulsiani, Shubham, et al.. (2025). SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation. 628–639.

2.

Ramanan, Deva, et al.. (2025). AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis. 21674–21684.

3.

Zhao, Qitao, et al.. (2025). DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion. 6317–6326. 1 indexed citations

4.

Jampani, Varun, et al.. (2024). MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation. 9698–9707. 1 indexed citations

5.

Bharadhwaj, Homanga, et al.. (2024). Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans. 6904–6911. 7 indexed citations

6.

Bharadhwaj, Homanga, et al.. (2024). RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking. 4788–4795. 33 indexed citations

7.

Ye, Yufei, Abhinav Gupta, Kris Kitani, & Shubham Tulsiani. (2024). G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis. 1911–1920. 7 indexed citations

8.

Ye, Yufei, Abhinav Gupta, & Shubham Tulsiani. (2022). What's in your hands? 3D Reconstruction of Generic Objects in Hands. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3885–3895. 41 indexed citations

9.

Kanazawa, Angjoo, et al.. (2022). Monocular Dynamic View Synthesis: A Reality Check. 33768–33780.

10.

Ye, Yufei, Shubham Tulsiani, & Abhinav Gupta. (2021). Shelf-Supervised Mesh Prediction in the Wild. 8839–8848. 43 indexed citations

11.

Tulsiani, Shubham, et al.. (2020). See, Hear, Explore: Curiosity via Audio-Visual Association. Neural Information Processing Systems. 33. 14961–14972. 2 indexed citations

12.

Gandhi, Dhiraj, et al.. (2020). Visual Imitation Made Easy. 1992–2005. 1 indexed citations

13.

Tulsiani, Shubham, et al.. (2020). Discovering Motor Programs by Recomposing Demonstrations. International Conference on Learning Representations. 9 indexed citations

14.

Johnson, Justin, Nikhila Ravi, Jeremy Reizenstein, et al.. (2020). Accelerating 3D deep learning with PyTorch3D. 1–1. 334 indexed citations breakdown →

15.

Häne, Christian, Shubham Tulsiani, & Jitendra Malik. (2017). Hierarchical Surface Prediction for 3D Object Reconstruction. 412–420. 183 indexed citations

16.

Tulsiani, Shubham, Hao Su, Leonidas Guibas, Alexei A. Efros, & Jitendra Malik. (2017). Learning Shape Abstractions by Assembling Volumetric Primitives. 1466–1474. 184 indexed citations

17.

Malik, Jitendra, Pablo Arbeláez, João Carreira, et al.. (2016). The three R’s of computer vision: Recognition, reconstruction and reorganization. Pattern Recognition Letters. 72. 4–14. 22 indexed citations

18.

Kar, Abhishek, Shubham Tulsiani, João Carreira, & Jitendra Malik. (2015). Amodal Completion and Size Constancy in Natural Scenes. 127–135. 23 indexed citations

19.

Kar, Abhishek, Shubham Tulsiani, João Carreira, & Jitendra Malik. (2015). Category-specific object reconstruction from a single image. 1966–1974. 150 indexed citations

20.

Yessenov, Kuat, Shubham Tulsiani, Aditya Krishna Menon, et al.. (2013). A colorful approach to text processing by example. 495–504. 33 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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