[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting

Overview

[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting

Open 3DPhotoInpainting in Colab

[Paper] [Project Website] [Google Colab]

We propose a method for converting a single RGB-D input image into a 3D photo, i.e., a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that iteratively synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. The resulting 3D photos can be efficiently rendered with motion parallax using standard graphics engines. We validate the effectiveness of our method on a wide range of challenging everyday scenes and show fewer artifacts when compared with the state-of-the-arts.

3D Photography using Context-aware Layered Depth Inpainting
Meng-Li Shih, Shih-Yang Su, Johannes Kopf, and Jia-Bin Huang
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

Prerequisites

  • Linux (tested on Ubuntu 18.04.4 LTS)
  • Anaconda
  • Python 3.7 (tested on 3.7.4)
  • PyTorch 1.4.0 (tested on 1.4.0 for execution)

and the Python dependencies listed in requirements.txt

  • To get started, please run the following commands:
    conda create -n 3DP python=3.7 anaconda
    conda activate 3DP
    pip install -r requirements.txt
    conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit==10.1.243 -c pytorch
  • Next, please download the model weight using the following command:
    chmod +x download.sh
    ./download.sh

Quick start

Please follow the instructions in this section. This should allow to execute our results. For more detailed instructions, please refer to DOCUMENTATION.md.

Execute

  1. Put .jpg files (e.g., test.jpg) into the image folder.
    • E.g., image/moon.jpg
  2. Run the following command
    python main.py --config argument.yml
    • Note: The 3D photo generation process usually takes about 2-3 minutes depending on the available computing resources.
  3. The results are stored in the following directories:
    • Corresponding depth map estimated by MiDaS
      • E.g. depth/moon.npy, depth/moon.png
      • User could edit depth/moon.png manually.
        • Remember to set the following two flags as listed below if user wants to use manually edited depth/moon.png as input for 3D Photo.
          • depth_format: '.png'
          • require_midas: False
    • Inpainted 3D mesh (Optional: User need to switch on the flag save_ply)
      • E.g. mesh/moon.ply
    • Rendered videos with zoom-in motion
      • E.g. video/moon_zoom-in.mp4
    • Rendered videos with swing motion
      • E.g. video/moon_swing.mp4
    • Rendered videos with circle motion
      • E.g. video/moon_circle.mp4
    • Rendered videos with dolly zoom-in effect
      • E.g. video/moon_dolly-zoom-in.mp4
      • Note: We assume that the object of focus is located at the center of the image.
  4. (Optional) If you want to change the default configuration. Please read DOCUMENTATION.md and modified argument.yml.

License

This work is licensed under MIT License. See LICENSE for details.

If you find our code/models useful, please consider citing our paper:

@inproceedings{Shih3DP20,
  author = {Shih, Meng-Li and Su, Shih-Yang and Kopf, Johannes and Huang, Jia-Bin},
  title = {3D Photography using Context-aware Layered Depth Inpainting},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}
}

Acknowledgments

Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

TableauBits 3 May 29, 2022
Combinatorially Hard Games where the levels are procedurally generated

puzzlegen Implementation of two procedurally simulated environments with gym interfaces. IceSlider: the agent needs to reach and stop on the pink squa

Autonomous Learning Group 3 Jun 26, 2022
DiSECt: Differentiable Simulator for Robotic Cutting

DiSECt: Differentiable Simulator for Robotic Cutting Website | Paper | Dataset | Video | Blog post DiSECt is a simulator for the cutting of deformable

NVIDIA Research Projects 73 Oct 29, 2022
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

2D-TAN (Optimized) Introduction This is an optimized re-implementation repository for AAAI'2020 paper: Learning 2D Temporal Localization Networks for

Joya Chen 112 Dec 31, 2022
HashNeRF-pytorch - Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives

HashNeRF-pytorch Instant-NGP recently introduced a Multi-resolution Hash Encodin

Yash Sanjay Bhalgat 616 Jan 06, 2023
Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21)

AdvRush Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21) Environmental Set-up Python == 3.6.12, PyTorch =

11 Dec 10, 2022
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

This repository is the official PyTorch implementation of Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

hippopmonkey 4 Dec 11, 2022
NaturalProofs: Mathematical Theorem Proving in Natural Language

NaturalProofs: Mathematical Theorem Proving in Natural Language NaturalProofs: Mathematical Theorem Proving in Natural Language Sean Welleck, Jiacheng

Sean Welleck 83 Jan 05, 2023
LRBoost is a scikit-learn compatible approach to performing linear residual based stacking/boosting.

LRBoost is a sckit-learn compatible package for linear residual boosting. LRBoost combines a linear estimator and a non-linear estimator to leverage t

Andrew Patton 5 Nov 23, 2022
This repo holds codes of the ICCV21 paper: Visual Alignment Constraint for Continuous Sign Language Recognition.

VAC_CSLR This repo holds codes of the paper: Visual Alignment Constraint for Continuous Sign Language Recognition.(ICCV 2021) [paper] Prerequisites Th

Yuecong Min 64 Dec 19, 2022
This repository contains an implementation of ConvMixer for the ICLR 2022 submission "Patches Are All You Need?".

Patches Are All You Need? 🤷 This repository contains an implementation of ConvMixer for the ICLR 2022 submission "Patches Are All You Need?". Code ov

ICLR 2022 Author 934 Dec 30, 2022
Contrastive Learning for Compact Single Image Dehazing, CVPR2021

AECR-Net Contrastive Learning for Compact Single Image Dehazing, CVPR2021. Official Pytorch based implementation. Paper arxiv Pytorch Version TODO: mo

glassy 253 Jan 01, 2023
Python implementation of Bayesian optimization over permutation spaces.

Bayesian Optimization over Permutation Spaces This repository contains the source code and the resources related to the paper "Bayesian Optimization o

Aryan Deshwal 9 Dec 23, 2022
This repository contains a pytorch implementation of "StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision".

StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision | Project Page | Paper | This repository contains a pytorch implementation of "St

87 Dec 09, 2022
[WWW 2022] Zero-Shot Stance Detection via Contrastive Learning

PT-HCL for Zero-Shot Stance Detection The code of this repository is constantly being updated... Please look forward to it! Introduction This reposito

Akuchi 12 Dec 21, 2022
Reading list for research topics in Masked Image Modeling

awesome-MIM Reading list for research topics in Masked Image Modeling(MIM). We list the most popular methods for MIM, if I missed something, please su

ligang 231 Dec 07, 2022
Algorithms for outlier, adversarial and drift detection

Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline d

Seldon 1.6k Dec 31, 2022
Custom Implementation of Non-Deep Networks

ParNet Custom Implementation of Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Official Repository https

Pritama Kumar Nayak 20 May 27, 2022
Code for the paper "Improving Vision-and-Language Navigation with Image-Text Pairs from the Web" (ECCV 2020)

Improving Vision-and-Language Navigation with Image-Text Pairs from the Web Arjun Majumdar, Ayush Shrivastava, Stefan Lee, Peter Anderson, Devi Parikh

Arjun Majumdar 44 Dec 14, 2022
The Pytorch implementation for "Video-Text Pre-training with Learned Regions"

Region_Learner The Pytorch implementation for "Video-Text Pre-training with Learned Regions" (arxiv) We are still cleaning up the code further and pre

Rui Yan 0 Mar 20, 2022