The Mission

The Graphics Replicability Stamp Initiative (GRSI) is an independent group of volunteers who help the community by enabling sharing of code and data as a community resource for non-commercial use. The volunteers check the submitted code and certify its replicability, awarding a replicability stamp, which is an additional recognition for authors of accepted papers who are willing to provide a complete implementation of their algorithm, to replicate the results presented in their paper. The replicability stamp is not meant to be a measure of the scientific quality of the paper or of the usefulness of presented algorithms. Rather, it is meant to be an endorsement of the replicability of the results presented in the paper and the recognition of the service provided to the community by releasing the code. Submissions for the replicability stamp will be considered only *after* the paper has been fully accepted. Submissions that are awarded the replicability stamp will receive additional exposure by being listed on this website. The purpose of this stamp is to promote reproducibility of research results and to allow scientists and practitioners to immediately benefit from state-of-the-art research results, without spending months re-implementing the proposed algorithms and trying to find the right parameter values. We also hope that it will indirectly foster scientific progress, since it will allow researchers to reliably compare with and to build upon existing techniques, knowing that they are using exactly the same implementation. This is an initiative supported by a growing list of publishers, journals and conferences.

Application Process

Any paper accepted in or after 2017 at one the following journals is eligible to apply for the stamp: The application process is lightweight, please refer to the website of the journals for their application form. You will have to provide general information on the submission, a representative image, a link to a public git repository with the source code, and instructions on how to compile and replicate the results. The code should compile on a vanilla installation of one of the major operating systems (Linux, MacOSX, or Windows), have a license that allows non-commercial usage, and only depend on libraries that are free for academic or research purposes. The code quality will not be evaluated, the purpose of the stamp is only to simplify replicability of the results: in its simplest form, the code should reproduce the data used to generate every result figure shown in the paper.

Replicable Papers

MeshCNN: A Network With an Edge

Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or
ACM Transactions on Graphics (TOG)


On Bubble Rings and Ink Chandeliers

Marcel Padilla, Albert Chern, Felix Knöppel, Ulrich Pinkall, Peter Schröder
ACM Transactions on Graphics (TOG)


Combining Voxel and Normal Predictions for Multi-View 3D Sketching

Johanna Delanoy, David Coeurjolly, Jacques-Olivier Lachaud, Adrien Bousseau
Elsevier Computers & Graphics (C&G)


An Implicit Frictional Contact Solver for Adaptive Cloth Simulation

Jie Li, Gilles Daviet, Rahul Narain, Florence Bertails-Descoubes, Matthew Overby, George Brown, Laurence Boissieux
ACM Transactions on Graphics (TOG)


Progressive Parameterizations

Ligang Liu, Chunyang Ye, Ruiqi Ni, Xiao-Ming Fu
ACM Transactions on Graphics (TOG)


T-junctions in spline surfaces

Kestutis Karciauskas, Daniele Panozzo, Jorg Peters (F# code by Martin Sarov)
ACM Transactions on Graphics (TOG)


A Multi-Scale Model for Simulating Liquid-Hair Interactions

Yun (Raymond) Fei, Henrique Teles Maia, Christopher Batty, Changxi Zheng, Eitan Grinspun
ACM Transactions on Graphics (TOG)


Hole Detection of a Planar Point Set: An Empty Disk Approach

​​Subhasree Methirumangalath, Shyam Sundar Kannan, Amal Dev Parakkat, and Ramanathan Muthuganapathy
Elsevier Computers & Graphics (SMI 2017)


The 2D Shape Structure Dataset: A user annotated open Access Database

Axel Carlier, Kathryn Leonard, Stefanie Hahmann, Geraldine Morin, Misha Collins
Elsevier Computers & Graphics (SMI 2016)


Modeling and Analysis of Origami Structures with Smooth Folds

Edwin A. Peraza Hernandez, Darren J. Hartl, Ergun Akleman, Dimitris C. Lagoudas
Elsevier Computer-Aided Design (SPM 2016)


Non-Rigid Puzzles

Or Litany, Emanuele Rodola, Alex Bronstein, Michael Bronstein, Daniel Cremers
Wiley Computer Graphics Forum (SGP 2016)



The Replicability committee is a independent group of volunteers from the graphics community who want to help the community by enabling sharing of code and data as a community resource for non-commercial use.

General Chair

Daniele Panozzo (New York University)

Associate Chairs

Marc Alexa (TU Berlin) - ACM TOG,
Leila De Floriani (University of Maryland, College Park) - IEEE TVCG,
Min Chen (University of Oxford) - Wiley CGF,
Joaquim Jorge (Instituto Superior Tecnico Lisboa) - Elsevier C&G,
Konrad Polthier (FU Berlin) - Elsevier CAGD

Conference Chairs

Marco Attene (IMATI-GE / CNR) - SMI,
Mario Botsch (University of Bielefeld) - SPM,

Advisory Board

Leif Kobbelt (RWTH Aachen), Holly Rushmeier (Yale University), Wenzel Jakob (EPFL), Sylvain Paris (Adobe Research), Kun Zhou (Zhejiang University), Juliana Freire (New York University), Marco Attene (IMATI-GE/CNR), Kavita Bala (Cornell).

Program Committee

Alec Jacobson (University of Toronto), Carlos Scheidegger (University of Arizona), Christian Schuller (ETHZ), Christoph Garth (University of Kaiserslautern), David Bommes (RWTH Aachen University), Dmitriy Smirnov (MIT), Dmitry Sokolov (INRIA), Francis Williams (NYU), James Zhou (Adobe Research), Jeremie Dumas (NYU), Julian Panetta (EPFL), Julien Tierny (CNRS), Jun-Yan Zhu (MIT), Justin Solomon (MIT), Kenshi Takayama (NII), Mario Botsch (University of Bielefeld), Miika Aittala (Aalto University), Nico Pietroni (ISTI CNR), Noam Aigerman (ETHZ), Oliver Glauser (ETHZ), Roi Poranne (ETHZ), Ryan Schmidt (Autodesk), Sebastian Koch (TU Berlin), Shahar Kovalsky (Yale University), Teseo Schneider (NYU), Vijay Natarajan (Indian Institute of Science), Vladimir Kim (Adobe Research), Xifeng Gao (NYU), Yotam Gingold (George Mason University), Yu Wang (MIT), Zhongshi Jiang (NYU).