Matheus Gadelha

Matheus Gadelha

I am currently a Research Scientist at Adobe Research. I received my PhD from University of Massachusetts - Amherst while being supervised by Prof. Rui Wang and Prof. Subhransu Maji. 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.

Lately, I have been particularly interested in mechanisms to incorporate 3D capabilities into large generative models so we can properly control them and robustly create/understand 3D data. I am also broadly interested in techniques (not necessarily ML-based) that allow us to better manipulate and author 3D content.

Latest on my research:

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.

Academic Collaborations: If you are a professor or a student interested in working with me, feel free to send me an e-mail -- I am more than happy to discuss interesting research problems we could work on together (or even just to chat about research).

[CV] [twitter] [email] [scholar]



DMesh: A Differentiable Representation for General Meshes

Sanghyun Son, Matheus Gadelha, Yang Zhou, Zexiang Xu, Ming C. Lin, Yi Zhou


GEM3D: Generative Medial Abstractions for 3D Shape Synthesis

Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan, Vova Kim, Matheus Gadelha, Melinos Averkiou, Siddhartha Chaudhuri, Evangelos Kalogerakis

CVPR 2024

Learning Continuous 3D Words for Text-to-Image Generation

Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, Niki Trigoni

CVPR 2024

Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D

Karran Pandey, Paul Guerrero, Matheus Gadelha, Yannick Hold-Geoffroy, Karan Singh, Niloy Mitra

CVPR 2024

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

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


ANISE: Assembly-based Neural Implicit Surface rEconstruction

Dmitry Petrov, Matheus Gadelha, Radomir Mech, Evangelos Kalogerakis

ICCV 2023 Workshop

Accidental Turntables: Learning 3D Pose by Watching Objects Turn

Zezhou Cheng, Matheus Gadelha, Subhransu Maji

ECCV 2022 Workshop

Recovering Detail in 3D Shapes Using Disparity Maps

Marissa Ramirez de Chanlatte, Matheus Gadelha, Thibault Groueix, Radomir Mech

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

CVPR 2022

PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos

Yiming Xie, Matheus Gadelha, Fengting Yang, Xiaowei Zhou, Huaizu Jiang


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.

IJCV 2020

Inferring 3D Shapes from Image Collections using Adversarial Networks

Matheus Gadelha, Aartika Rai, Rui Wang, Subhransu Maji

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


Deep Manifold Prior

Matheus Gadelha, Rui Wang, Subhransu Maji

Best poster honorable mention at NECV.

CVPR 2020

Learning Generative Models of Shape Handles

Matheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji

ICCV 2019

**Shape Reconstruction with Differentiable Projections and Deep Priors **

Matheus Gadelha, Rui Wang, Subhransu Maji

CVPR 2019

**A Bayesian Perspective on the Deep Image Prior **

Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon

ECCV 2018 - 3DRMS Workshop

A Deeper Look at 3D Shape Classifiers

Jong-Chyi Su, Matheus Gadelha, Rui Wang, Subhransu Maji

ECCV 2018

Multiresolution Tree Networks for Point Cloud Processing

Matheus Gadelha, Rui Wang, Subhransu Maji

3DV 2017

Unsupervised 3D Shape Induction from 2D Views of Multiple Objects

Matheus Gadelha, Subhransu Maji, Rui Wang

3DV 2017

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis, Subhransu Maji, Rui Wang

BMVC 2017

Shape Generation using Spatially Partitioned Point Clouds

Matheus Gadelha, Subhransu Maji, Rui Wang



I worked/am working as a reviewer for the following venues:

I worked/am working as AC for the following venues:

Professional Experience


Research Scientist at San Jose, CA.

June 2021 - Now.

Google (Perception).

Research Intern in Amherst, MA.

Summer 2020.


Research Intern at San Jose, CA

Summer 2019.


Applied Scientist Intern at Pasadena, CA.

Summer 2018.

Federal University at Rio Grande do Norte.

Temporary Letcturer - Introduction to Algorithms and Numerical Methods.

June 2014 - June2015 .