**Matheus Gadelha**

I am currently a Research Scientist at [Adobe Research](https://research.adobe.com/).
I received my PhD from [University of Massachusetts - Amherst](https://www.cics.umass.edu/)
while being supervised by [Prof. Rui Wang](https://people.cs.umass.edu/~ruiwang/) and
[Prof. Subhransu Maji](http://people.cs.umass.edu/~smaji/).
I am interested in Computer Graphics, Vision and their intersections with Machine Learning.
My work is focused on models and representations of tridimensional data for
both discriminative and generative models with a particular interest in learning them directly from images.
Check my Research Statement below for a more detailed overview.
Prior to joining UMass, I've obtained my bachelor's and master's degrees in Computer Science
from [Federal University of Rio Grande do Norte](https://ufrn.br/en),
while working with [Prof. Selan dos Santos](https://sigaa.ufrn.br/sigaa/public/docente/portal.jsf?siape=2497950) and [Prof. Bruno Motta de Carvalho](https://sigaa.ufrn.br/sigaa/public/docente/portal.jsf?siape=2177445).
**Internships for PhD students: ** If you are interested in related areas to the ones I've mentioned above (or anything
related to my previous research), don't be shy and send me an e-mail with your CV and a short
description of the problems you are interested in working on. We are always looking for talented
interns to join us at Adobe Research.
**Open research mentoring: **
I used to hold a career mentoring session where I had the chance to chat with many
inspiring students.
Most of them were considering pursuing a research career and applying to grad school.
Unfortunately, I was bit frustrated by it, since by the time applications are being considered,
there is not much I can do to help those students beyond some very superficial advice in grad school applications.
Thus, inspired by my colleague [Li-Yi Wei](https://www.liyiwei.org/), I decided to have
an open research mentorship program.
*tl;dr*: if you are interested in doing research in computer graphics/vision, we
can work together so that you can have some experience with research in my field and further
develop the skills and credentials required to have a successful research.
If you want to know more, please access [this](research_mentoring.html) page.
[[CV](cv.pdf)]
[[Research Statement](ResearchStatement.pdf)]
[[twitter](https://twitter.com/gadelha_m)]
[[email](mailto:matheusabrantesgadelha@gmail.com)]
[[scholar](https://scholar.google.com/citations?user=VhqmvXsAAAAJ&hl=en)]
(#) Work Experience
 **Adobe**. Research Scientist at San Jose, CA. _June 2021 - Now_.
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 **Google (Perception)**. Research Intern in ~~Mountain View, CA~~ Amherst, MA. _Summer 2020_.
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 **Adobe**. Research Intern at San Jose, CA. _Summer 2019_.
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 **Amazon**. Applied Scientist Intern at Pasadena, CA. _Summer 2018_.
(#) Research

[ArXiv]
**Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models**
_Shengqu Cai, Duygu Ceylan, **Matheus Gadelha**, Chun-Hao Huang, Tuanfeng Y. Wang, Gordon Wetzstein_
[[Pre-print](https://primecai.github.io/generative_rendering/assets/pdf/low_res.pdf)]
[[Project Page](https://primecai.github.io/generative_rendering/index)]
-----

[ICCV 2023]
**3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets**
_Ta-Ying Cheng, **Matheus Gadelha**, Soren Pirk, Thibault Groueix, Radomir Mech, Andrew Markham, Niki Trigoni_
[[Pre-print - coming soon]()]
-----

[TVCG]
**ANISE: Assembly-based Neural Implicit Surface rEconstruction**
_Dmitry Petrov, **Matheus Gadelha**, Radomir Mech, Evangelos Kalogerakis_
[[Pre-print](https://arxiv.org/pdf/2205.13682.pdf)]
[[Project Page](https://lodurality.github.io/ANISE/)]
-----

[ICCV 2023 Workshop]
**Accidental Turntables: Learning 3D Pose by Watching Objects Turn**
_Zezhou Cheng, **Matheus Gadelha**, Subhransu Maji_
[[Pre-print](https://arxiv.org/pdf/2212.06300.pdf)]
[[Project Page](https://people.cs.umass.edu/~zezhoucheng/acci-turn/)]
-----

[ECCV 2022 Workshop]
**Recovering Detail in 3D Shapes Using Disparity Maps**
_Marissa Ramirez de Chanlatte, **Matheus Gadelha**, Thibault Groueix, Radomir Mech_
[[Pre-print](https://arxiv.org/pdf/2207.00182.pdf)]
-----

[SGP 2022]
**PrimFit: Learning to Fit Primitives Improves Few Shot Learning on Point Clouds**
_Gopal Sharma, Bidya Dash, **Matheus Gadelha**, Aruni RoyChowdhury, Marios Loizou, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller, Rui Wang and Subhransu Maji_
[[Pre-print](https://arxiv.org/pdf/2112.13942.pdf)]
-----

[CVPR 2022]
**PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos**
_Yiming Xie, **Matheus Gadelha**, Fengting Yang, Xiaowei Zhou, Huaizu Jiang_
[[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_PlanarRecon_Real-Time_3D_Plane_Detection_and_Reconstruction_From_Posed_Monocular_CVPR_2022_paper.pdf)]
[[Supplemental](https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_PlanarRecon_Real-Time_3D_CVPR_2022_supplemental.pdf)]
-----

[IEEE AIVR 2021]
**Trace Match & Merge: Long-TermField-Of-View Prediction for AR Applications**
_Adam Viola`*`, Sahil Sharma`*`,Pankaj Bishnoi`*`, **Matheus Gadelha**, Stefano Petrangeli, Haoliang Wang, Viswanathan Swaminathan_
**Best paper candidate at [IEEE AIVR](https://ieee-aivr.cs.nthu.edu.tw/program)**.
[[Paper](AIVR_2021___AR_FOV_prediction.pdf)]
-----

[IJCV 2020]
**Inferring 3D Shapes from Image Collections using Adversarial Networks**
_**Matheus Gadelha**, Aartika Rai, Rui Wang, Subhransu Maji_
[[Journal](https://link.springer.com/article/10.1007/s11263-020-01335-w)]
[[Pre-print](https://arxiv.org/abs/1906.04910)]
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[ECCV 2020]
**Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions**
_**Matheus Gadelha**`*`, Aruni RoyChowdhury`*`, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller, Rui Wang, Subhransu Maji_
[[Paper](https://arxiv.org/pdf/2003.13834.pdf)]
[[Project Page](selfsupacd/index.html)]
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**Deep Manifold Prior**
_**Matheus Gadelha**, Rui Wang, Subhransu Maji_
**Best poster honorable mention at [NECV](http://visual.cs.brown.edu/workshops/necv2019/)**.
[[Paper](https://arxiv.org/pdf/2004.04242.pdf)]
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[CVPR 2020]
**Learning Generative Models of Shape Handles**
_**Matheus Gadelha**, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji_
[[Project Page](shapehandles/index.html)]
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[ICCV 2019]
**Shape Reconstruction with Differentiable Projections and Deep Priors **
_**Matheus Gadelha**, Rui Wang, Subhransu Maji_
[[Paper](shaperec/dsp_paper.pdf)]
[[Project Page](shaperec/index.html)]
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[CVPR 2019]
**A Bayesian Perspective on the Deep Image Prior **
_Zezhou Cheng, **Matheus Gadelha**, Subhransu Maji, Daniel Sheldon_
[[Paper](https://arxiv.org/abs/1904.07457)]
[[Project Page](https://people.cs.umass.edu/~zezhoucheng/gp-dip/)]
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[ECCV 2018 - 3DRMS Workshop]
**A Deeper Look at 3D Shape Classifiers **
_Jong-Chyi Su, **Matheus Gadelha**, Rui Wang, Subhransu Maji_
[[Paper](https://arxiv.org/abs/1809.02560)]
[[Project Page](https://people.cs.umass.edu/~jcsu/papers/shape_recog/)]
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[ECCV 2018]
**Multiresolution Tree Networks for Point Cloud Processing **
_**Matheus Gadelha**, Rui Wang, Subhransu Maji_
[[Paper](https://arxiv.org/abs/1807.03520)]
[[Project Page](mrt/index.html)]
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[3DV 2017]
**Unsupervised 3D Shape Induction from 2D Views of Multiple Objects **
_**Matheus Gadelha**, Subhransu Maji, Rui Wang_
[[Paper](https://arxiv.org/pdf/1612.05872.pdf)]
[[Project Page](prgan/index.html)]
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[3DV 2017]
**3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks **
_Zhaoliang Lun, **Matheus Gadelha**, Evangelos Kalogerakis, Subhransu Maji, Rui Wang_
[[ArXiv](https://arxiv.org/abs/1707.06375)]
[[Project Page](https://people.cs.umass.edu/~zlun/papers/SketchModeling/)]
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[BMVC 2017]
**Shape Generation using Spatially Partitioned Point Clouds **
_**Matheus Gadelha**, Subhransu Maji, Rui Wang_
[[ArXiv](https://arxiv.org/abs/1707.06267)]
[[Project Page](sppc/index.html)]
(#) Reviewing
I worked/am working as a reviewer for the following venues:
* SIGGRAPH 2023
* CVPR 2018-2023 (**2021 outstanding reviewer**)
* ICCV 2019-2023
* ECCV 2018-2023
* ICLR 2021
* TPAMI (2018, 2021, 2023)
* Computer and Graphics 2018
* SIGGRAPH Asia 2018
* Pacific Graphics 2019
I worked/am working as AC for the following venues:
* WACV 2023
(#) Open Source
(##) Interactive Mesh Deformation w/ ARAP Surface Modeling

Implementation of the ARAP Surface Modeling paper.
It includes a software to deform any mesh, applying the computed transformation in real time.
Made using C++ with Eigen and OpenGL.
[[code](https://github.com/matheusgadelha/Pintar)]
(##) Rigid Body Simulation
Rigid body simulator coded in C++ with Eigen library and OpenGL.
The code includes a demo where you can load any model and interactively draw forces.
[[code](https://github.com/matheusgadelha/Pintar)]
(##) Subdivision Surface

Code in C++ to perform subdivision of a closed triangular mesh. Made from scratch, so no
libraries are required.
[[code](https://github.com/matheusgadelha/SubdivisionSurfaces)]
(##) Generic A* implementation and CSP solver
Template-based implementation for the A* algorithm and CSP solver written in C++.
It is a generic solver, where you can simply define the successor and heuristic function for each problem.
Includes examples on solving knight shortest path problem and the classic TSP (w/ Minimum Spanning Tree heuristic).
CSP solver includes an efficient sudoku solver example.
[[A* code](https://github.com/matheusgadelha/AStar)]
[[CSP code](https://github.com/matheusgadelha/CSPSolver)]