Training and Evaluation Code for Neural Volumes

Overview

Neural Volumes

This repository contains training and evaluation code for the paper Neural Volumes. The method learns a 3D volumetric representation of objects & scenes that can be rendered and animated from only calibrated multi-view video.

Neural Volumes

Citing Neural Volumes

If you use Neural Volumes in your research, please cite the paper:

@article{Lombardi:2019,
 author = {Stephen Lombardi and Tomas Simon and Jason Saragih and Gabriel Schwartz and Andreas Lehrmann and Yaser Sheikh},
 title = {Neural Volumes: Learning Dynamic Renderable Volumes from Images},
 journal = {ACM Trans. Graph.},
 issue_date = {July 2019},
 volume = {38},
 number = {4},
 month = jul,
 year = {2019},
 issn = {0730-0301},
 pages = {65:1--65:14},
 articleno = {65},
 numpages = {14},
 url = {http://doi.acm.org/10.1145/3306346.3323020},
 doi = {10.1145/3306346.3323020},
 acmid = {3323020},
 publisher = {ACM},
 address = {New York, NY, USA},
}

File Organization

The root directory contains several subdirectories and files:

data/ --- custom PyTorch Dataset classes for loading included data
eval/ --- utilities for evaluation
experiments/ --- location of input data and training and evaluation output
models/ --- PyTorch modules for Neural Volumes
render.py --- main evaluation script
train.py --- main training script

Requirements

  • Python (3.6+)
    • PyTorch (1.2+)
    • NumPy
    • Pillow
    • Matplotlib
  • ffmpeg (in PATH, needed to render videos)

How to Use

There are two main scripts in the root directory: train.py and render.py. The scripts take a configuration file for the experiment that defines the dataset used and the options for the model (e.g., the type of decoder that is used).

A sample set of input data is provided in the v0.1 release and can be downloaded here and extracted into the root directory of the repository. experiments/dryice1/data contains the input images and camera calibration data, and experiments/dryice1/experiment1 contains an example experiment configuration file (experiments/dryice1/experiment1/config.py).

To train the model:

python train.py experiments/dryice1/experiment1/config.py

To render a video of a trained model:

python render.py experiments/dryice1/experiment1/config.py Render

License

See the LICENSE file for details.

Comments
  • Training with our own data

    Training with our own data

    Hi,
    I have a few questions on how the data should be formatted and the data format of the provided dryice1.

    • The model expects world space coordinate in meters? i.e if my extrinsics are already in meters do I still need the world_scale=1/256. in config.py file?
    • The extrinsics are in world2cam and the rotation convention is like opencv? i.e, y-down,z-forward and x-right, assuming identity for pose.txt file?
    • how long do I need to train for about 200 frames? And in the config.py file it seems you are skipping some frames? This is ok to do for my own sequence as well?
    • in the KRT file, I see that there's 5 parameters above the RT matrix. This is the distortion correction in opencv format? But it is not used yes?
    • I did not visualize your cameras, so I am not sure how they are distributed. Is it gonna be a problem if I use 50 cameras equally distributed in a half-hemisphere and the subject is already at world origin and 3.5 meters from every cameras? My question is do I need to filter the training cameras so that the back side of subject that is not seen by input 3 cameras is excluded?
    • How do I choose the input cameras? I have a visualization of the cameras . Which camera config should I use? Is this more a question of which testing camera poses I intend to have, i.e narrower the testing cameras' range of view, the closer input training cameras can be? Config_0 is more orthogonal and Config_1 sees less of the backside.
    opened by zawlin 32
  • Some questions about coordination transformation

    Some questions about coordination transformation

    Hello, Thanks for releasing your code. I am impressed by your work. Now I hope to run your code with my our dataset. I have two questions.

    Firstly, I see the pose.txt is used in the code to put the objects in the center. If I use my own data, will the file still work?

    Secondly, I see the code set the raypos is among -1 and 1. Is it the matrix in this pose file that narrows the range to -1 to 1? My own dataset' range is different.

    Thirdly, does the code limit the scope of the template? Does it have to be between 0-255?

    Thanks a lot in advance!

    opened by maobenz 3
  • Location of the volume

    Location of the volume

    Hi there,

    I wonder whether the origin of the volume is (0,0,0)?

    I'm testing the method on a public dataset (http://people.csail.mit.edu/drdaniel/mesh_animation), and I know exactly where (0,0,0) is in the images. But the volume seems to float around the scene. This is the first preview for training process: prog_000001

    Each camera is pointing to the opposite side of the scene, so I expect the same for the volume location in images. But for some reason, they are on the same side in the images. Can you help?

    Thank you.

    opened by lochuynh1989 3
  • Any plan to release all data that presented in the paper?

    Any plan to release all data that presented in the paper?

    Hi @stephenlombardi ,

    Thanks for sharing this great work. I was wondering do you have any plan to release all the data that you used in the paper (apart from the dryice)?

    Best, Zirui

    opened by ziruiw-dev 2
  • Block-wise initialization scheme

    Block-wise initialization scheme

    Hi, is there any paper describing the used block-wise weight initialization scheme?

    https://github.com/facebookresearch/neuralvolumes/blob/8c5fad49b2b05b4b2e79917ee87299e7c1676d59/models/utils.py#L73

    opened by denkorzh 2
  • Is there a way to render a 3D file from this?

    Is there a way to render a 3D file from this?

    Hello, I was wondering if there is a way to export an .obj/,fbx file along with corresponding materials from this? If not, do you have any suggestions as to how to go about that if I were to try extend the code to incorporate that functionality?

    opened by arlorostirolla 1
  • How Can I train and render a Person Image

    How Can I train and render a Person Image

    Hi my name is Luan I am trying to render a Person Image but I am not being able to run can you create and for me a folder with the Setting setup to use a person image? Thank you.

    opened by LuanDalOrto 1
  • code for hybrid rendering (section 6.2) doesn't exist?

    code for hybrid rendering (section 6.2) doesn't exist?

    Hello,

    First of all, thank you for releasing the code for your seminal work. I really think neural volumes is one of the works that popularized differentiable rendering and inspired future works such as neural radiance fields.

    My question is whether this codebase includes the code for the hybrid rendering method outlined in section 6.2 of the paper. I'm trying to fit Neural Volumes to multi-view video of a full-body human being, similar to the 5th subfigure in Fig. 1 of the main paper, but after reading it more carefully it seems as though I would need to use hybrid rendering to be able to render the fine details of the human being.

    Could you

    1. confirm the existence of hybrid rendering in this codebase AND
    2. whether or not hybrid rendering was used to render the full-bodied human being in Fig. 1 of the main paper.

    Thank you in advance.

    opened by andrewsonga 1
  • Misaligned views in rendering

    Misaligned views in rendering

    Hi,

    I am working on MIT dataset to test the network. When I specify a camera to render, it looks fine throughout timeline. However, while rendering the rotating video, the cameras are misaligned as shown in attached screenshot. All cameras look like clustered at the center and views are spread around within the range cameras cover. Is it possible to be any error in KRT or configuration?

    Any suggestion is welcome. issue_MIT_5_cams

    opened by CorneliusHsiao 1
Releases(v0.1)
Owner
Meta Research
Meta Research
Alphabetical Letter Recognition

DecisionTrees-Image-Classification Alphabetical Letter Recognition In these demo we are using "Decision Trees" Our database is composed by Learning Im

Mohammed Firass 4 Nov 30, 2021
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
[ICLR2021oral] Rethinking Architecture Selection in Differentiable NAS

DARTS-PT Code accompanying the paper ICLR'2021: Rethinking Architecture Selection in Differentiable NAS Ruochen Wang, Minhao Cheng, Xiangning Chen, Xi

Ruochen Wang 86 Dec 27, 2022
Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters

[Unofficial code-base] NeRF--: Neural Radiance Fields Without Known Camera Parameters [ Project | Paper | Official code base ] ⬅️ Thanks the original

Jianfei Guo 239 Dec 22, 2022
RL and distillation in CARLA using a factorized world model

World on Rails Learning to drive from a world on rails Dian Chen, Vladlen Koltun, Philipp Krähenbühl, arXiv techical report (arXiv 2105.00636) This re

Dian Chen 131 Dec 16, 2022
Colab notebook and additional materials for Python-driven analysis of redlining data in Philadelphia

RedliningExploration The Google Colaboratory file contained in this repository contains work inspired by a project on educational inequality in the Ph

Benjamin Warren 1 Jan 20, 2022
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

ELECTRA Introduction ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using

Google Research 2.1k Dec 28, 2022
PyTorch implementation of Deformable Convolution

Deformable Convolutional Networks in PyTorch This repo is an implementation of Deformable Convolution. Ported from author's MXNet implementation. Buil

411 Dec 16, 2022
A general 3D Object Detection codebase in PyTorch.

Det3D is the first 3D Object Detection toolbox which provides off the box implementations of many 3D object detection algorithms such as PointPillars, SECOND, PIXOR, etc, as well as state-of-the-art

Benjin Zhu 1.4k Jan 05, 2023
This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (EMNLP 2020)

Towards Persona-Based Empathetic Conversational Models (PEC) This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (E

Zhong Peixiang 35 Nov 17, 2022
Implementation of the algorithm shown in the article "Modelo de Predicción de Éxito de Canciones Basado en Descriptores de Audio"

Success Predictor Implementation of the algorithm shown in the article "Modelo de Predicción de Éxito de Canciones Basado en Descriptores de Audio". B

Rodrigo Nazar Meier 4 Mar 17, 2022
A library for implementing Decentralized Graph Neural Network algorithms.

decentralized-gnn A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes. De

Multimedia Knowledge and Social Analytics Lab 5 Nov 07, 2022
A privacy-focused, intelligent security camera system.

Self-Hosted Home Security Camera System A privacy-focused, intelligent security camera system. Features: Multi-camera support w/ minimal configuration

Scott Barnes 175 Jan 01, 2023
[ICCV21] Official implementation of the "Social NCE: Contrastive Learning of Socially-aware Motion Representations" in PyTorch.

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

VITA lab at EPFL 125 Dec 23, 2022
OpenMMLab Pose Estimation Toolbox and Benchmark.

Introduction English | 简体中文 MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project. The master b

OpenMMLab 2.8k Dec 31, 2022
PyMatting: A Python Library for Alpha Matting

Given an input image and a hand-drawn trimap (top row), alpha matting estimates the alpha channel of a foreground object which can then be composed onto a different background (bottom row).

PyMatting 1.4k Dec 30, 2022
[ICCV 2021] Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

Amplitude-Phase Recombination (ICCV'21) Official PyTorch implementation of "Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neur

Guangyao Chen 53 Oct 05, 2022
TensorFlow CNN for fast style transfer

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! It takes 100ms on a 2015 Titan X to style t

1 Dec 14, 2021
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Paddle-PANet 目录 结果对比 论文介绍 快速安装 结果对比 CTW1500 Method Backbone Fine

7 Aug 08, 2022
Code from the paper "High-Performance Brain-to-Text Communication via Handwriting"

High-Performance Brain-to-Text Communication via Handwriting Overview This repo is associated with this manuscript, preprint and dataset. The code can

Francis R. Willett 306 Jan 03, 2023