DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)

Last update: Dec 26, 2022

Overview

Evaluation, Training, Demo, and Inference of DeFMO

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)

Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys

Qualitative results: https://www.youtube.com/watch?v=pmAynZvaaQ4

Pre-trained models

The pre-trained DeFMO model as reported in the paper is available here: https://polybox.ethz.ch/index.php/s/M06QR8jHog9GAcF. Put them into ./saved_models sub-folder.

Inference

For generating video temporal super-resolution:

python run.py --video example/falling_pen.avi

For generating temporal super-resolution of a single frame with the given background:

python run.py --im example/im.png --bgr example/bgr.png

Evaluation

After downloading the pre-trained models and downloading the evaluation datasets, you can run

python eval_dataset.py

Synthetic dataset generation

For the dataset generation, please download:

ShapeNetCore.v2 dataset: https://www.shapenet.org/.
Textures from the DTD dataset: https://www.robots.ox.ac.uk/~vgg/data/dtd/. The exact split used in DeFMO is from the "Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool" model and can be downloaded here: https://polybox.ethz.ch/index.php/s/9Abv3QRm0ZgPzhK.
Backgrounds for the training dataset from the VOT dataset: https://www.votchallenge.net/vot2018/dataset.html.
Backgrounds for the testing dataset from the Sports1M dataset: https://cs.stanford.edu/people/karpathy/deepvideo/.
Blender 2.79b with Python enabled.

Then, insert your paths in renderer/settings.py file. To generate the dataset, run in renderer sub-folder:

python run_render.py

Note that the full training dataset with 50 object categories, 1000 objects per category, and 24 timestamps takes up to 1 TB of storage memory. Due to this and also the ShapeNet licence, we cannot make the pre-generated dataset public - please generate it by yourself using the steps above.

Training

Set up all paths in main_settings.py and run

python train.py

Evaluation on real-world datasets

All evaluation datasets can be found at http://cmp.felk.cvut.cz/fmo/. We provide a download_datasets.sh script to download the Falling Objects, the TbD-3D, and the TbD datasets.

Reference

If you use this repository, please cite the following publication ( https://arxiv.org/abs/2012.00595 ):

@inproceedings{defmo,
  author = {Denys Rozumnyi and Martin R. Oswald and Vittorio Ferrari and Jiri Matas and Marc Pollefeys},
  title = {DeFMO: Deblurring and Shape Recovery of Fast Moving Objects},
  booktitle = {CVPR},
  address = {Nashville, Tennessee, USA},
  month = jun,
  year = {2021}
}

Comments

Question about training set

Hi, thanks for your generous sharing.

I have a question about training set generating in your work. I generated a training set following your codes. Its size is about 100GB, far less than 1TB. Is there anything wrong?

Thanks.

opened by fan-hd 11
Apply your model on custom longer video clips

Hi thank you for releasing your code,

Can your model be applied on custom videos about high speed train crossing? Video clips last from 3 to 10 seconds, my idea was to preprocess them with your code in order to keep the same frame rate and have a better video quality for later object detection. This is an example frame from original video clip:

I tried to run your code on a video about 6 seconds and the result was a longer video (about 13min) with a lower level of detail, probably I'm doing something wrong. This is an example frame from output video clip:

How can I correctly reconstruct the quality of single frames usin all the information contained in the video?

opened by fabiozappo 4
Question about comparison with Jin et al.'s work (CVPR2018)

Hi, thank you for your interesting work! I have a question about the comparison of methods in your work. When making comparisons, did you retrain Jin et al.'s model ("Learning to Extract a Video Sequence from a Single Motion-Blurred Image" from CVPR 2018), or did you just use their pre-trained checkpoints? I couldn't find the training code on their github page.

opened by zzh-tech 2
Padding in Time-Consistency Loss

Hi,

Congratulations!

I found that "padding = tuple(side // 10 for side in sh[:2]) + (0,)" for normalized cross-correlation. Does it only implement padding to the height axis, since the padding tuple will be of size (4//10, H//10, 0)?

Thanks a lot.

opened by JLiu-Edinburgh 1
run on google colab!

I'm confused! and need to run the code on google colab or more explanation about how to implement that code in vscode or something else .if it know someone please help me

opened by ganikas 3

Releases(v1.0)

v1.0(Feb 7, 2021)

The first release of the method reported in the arxiv paper.
Source code(tar.gz)
Source code(zip)

Owner

Denys Rozumnyi

PhD student at ETH Zurich.

GitHub Repository https://github.com/rozumden/DeFMO

A platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.

Wilderness Scavenger: 3D Open-World FPS Game AI Challenge This is a platform for intelligent agent learning based on a 3D open-world FPS game develope

46 Nov 24, 2022

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

1.7k Dec 30, 2022

PURE: End-to-End Relation Extraction

PURE: End-to-End Relation Extraction This repository contains (PyTorch) code and pre-trained models for PURE (the Princeton University Relation Extrac

657 Jan 09, 2023

Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"

BoxeR By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-A

111 Dec 07, 2022

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)" which introduces a new class of deep generative models that gene

43 Jan 03, 2023

Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning".

ERICA Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive L

75 Nov 02, 2022

The Most Efficient Temporal Difference Learning Framework for 2048

moporgic/TDL2048+ TDL2048+ is a highly optimized temporal difference (TD) learning framework for 2048. Features Many common methods related to 2048 ar

5 Nov 23, 2022

Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

IIC2233 - Programación Avanzada Evaluación Las evaluaciones serán efectuadas por medio de actividades prácticas en clases y tareas. Se calculará la no

47 Sep 06, 2022

A module for solving and visualizing Schrödinger equation.

qmsolve This is an attempt at making a solid, easy to use solver, capable of solving and visualize the Schrödinger equation for multiple particles, an

506 Dec 28, 2022

PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

PEPit: Performance Estimation in Python This open source Python library provides a generic way to use PEP framework in Python. Performance estimation

53 Nov 16, 2022

Unifying Global-Local Representations in Salient Object Detection with Transformer

GLSTR (Global-Local Saliency Transformer) This is the official implementation of paper "Unifying Global-Local Representations in Salient Object Detect

11 Aug 24, 2022

Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan

6 Jan 06, 2023

Privacy-Preserving Portrait Matting [ACM MM-21]

Privacy-Preserving Portrait Matting [ACM MM-21] This is the official repository of the paper Privacy-Preserving Portrait Matting. Jizhizi Li∗, Sihan M

212 Dec 27, 2022

graph-theoretic framework for robust pairwise data association

CLIPPER: A Graph-Theoretic Framework for Robust Data Association Data association is a fundamental problem in robotics and autonomy. CLIPPER provides

118 Dec 28, 2022

Diverse Image Generation via Self-Conditioned GANs

Diverse Image Generation via Self-Conditioned GANs Project | Paper Diverse Image Generation via Self-Conditioned GANs Steven Liu, Tongzhou Wang, David

147 Dec 03, 2022

This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

398 Dec 30, 2022

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)

Related tags

Overview

Evaluation, Training, Demo, and Inference of DeFMO

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)

Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys

Qualitative results: https://www.youtube.com/watch?v=pmAynZvaaQ4

Pre-trained models

Inference

Evaluation

Synthetic dataset generation

Training

Evaluation on real-world datasets

Reference

Comments

Question about training set

Apply your model on custom longer video clips

Question about comparison with Jin et al.'s work (CVPR2018)

Padding in Time-Consistency Loss

run on google colab!

Releases(v1.0)

v1.0(Feb 7, 2021)

Owner

Denys Rozumnyi

A platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

PURE: End-to-End Relation Extraction

Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"

Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning".

The Most Efficient Temporal Difference Learning Framework for 2048

Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

A module for solving and visualizing Schrödinger equation.

PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

Unifying Global-Local Representations in Salient Object Detection with Transformer

Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Privacy-Preserving Portrait Matting [ACM MM-21]

graph-theoretic framework for robust pairwise data association

Diverse Image Generation via Self-Conditioned GANs

This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Code for the SIGIR 2022 paper "Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion"

This repo contains the code required to train the multivariate time-series Transformer.

Machine learning and Deep learning models, deploy on telegram (the best social media)

A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV