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Connectomics2024-1-ExcitatoryNeurons

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Acknowledgements[cc4ac8]

We give special thanks to the Imagen Video team for their collaboration and for providing their system to do super resolution. To our artist friends Irina Blok and Alonso Martinez for extensive creative exploration of the system and for using Phenaki to generate some of the videos showcased here. We also want to thank Niki Parmar for initial discussions. Special thanks to Gabriel Bender and Thang Luong for reviewing the paper and providing constructive feedback. We appreciate the efforts of Kevin Murphy and David Fleet for advising the project and providing feedback throughout. We are grateful to Evan Rapoport, Douglas Eck and Zoubin Ghahramani for supporting this work in a variety of ways. Tim Salimans and Chitwan Saharia helped us with brainstorming and coming up with shared benchmarks. Jason Baldridge was instrumental for bouncing ideas. Alex Rizkowsky was very helpful in keeping things organized, while Erica Moreira and Victor Gomes ensured smooth resourcing for the project. Sarah Laszlo and Kathy Meier-Hellstern have greatly helped us incorporate important responsible AI practices into this project, which we are immensely grateful for. Finally, Blake Hechtman and Anselm Levskaya were generous in helping us debug a number of JAX issues.

Credit for Phenakistoscope asset:

Creator: Muybridge, Eadweard, 1830-1904, artist
Title: The zoopraxiscope* - a couple waltzing (No. 35., title from item.)
Edits made: Extended background and converted file format to mp4

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Connectomics2024-2-InhibitoryNeurons

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Connectomics2024-1-ExcitatoryNeurons

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November 21, 2023

Open sourcing Project Guideline: A platform for computer vision accessibility technology
  • Human-Computer Interaction and Visualization ·
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  • Open Source Models & Datasets

November 21, 2023

Open sourcing Project Guideline: A platform for computer vision accessibility technology
  • Human-Computer Interaction and Visualization ·
  • Machine Perception ·
  • Open Source Models & Datasets

November 21, 2023

Open sourcing Project Guideline: A platform for computer vision accessibility technology
  • Human-Computer Interaction and Visualization ·
  • Machine Perception ·
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November 21, 2023

Open sourcing Project Guideline: A platform for computer vision accessibility technology
  • Human-Computer Interaction and Visualization ·
  • Machine Perception ·
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November 21, 2023

Open sourcing Project Guideline: A platform for computer vision accessibility technology
  • Human-Computer Interaction and Visualization ·
  • Machine Perception ·
  • Open Source Models & Datasets

November 21, 2023

Open sourcing Project Guideline: A platform for computer vision accessibility technology
  • Human-Computer Interaction and Visualization ·
  • Machine Perception ·
  • Open Source Models & Datasets

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De-rendering the World’s Revolutionary Artefacts
Elliott Wu
Ameesh Makadia
Jiajun Wu
Noah Snavely
Richard Tucker
Angjoo Kanazawa
Computer Vision and Pattern Recognition (CVPR) (2021)
Preview abstract Recent works have shown exciting results in unsupervised image de-rendering—learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR (Revolutionary Artefact De-rendering And Re-rendering), that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world's revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting. View details
De-rendering the World’s Revolutionary Artefacts
Elliott Wu
Ameesh Makadia
Jiajun Wu
Noah Snavely
Richard Tucker
Angjoo Kanazawa
Computer Vision and Pattern Recognition (CVPR) (2021)
Preview abstract Recent works have shown exciting results in unsupervised image de-rendering—learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR (Revolutionary Artefact De-rendering And Re-rendering), that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world's revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting. View details
De-rendering the World’s Revolutionary Artefacts
Elliott Wu
Ameesh Makadia
Jiajun Wu
Noah Snavely
Richard Tucker
Angjoo Kanazawa
Computer Vision and Pattern Recognition (CVPR) (2021)
Preview abstract Recent works have shown exciting results in unsupervised image de-rendering—learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR (Revolutionary Artefact De-rendering And Re-rendering), that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world's revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting. View details

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