Code for Motion Representations for Articulated Animation paper

Overview

Motion Representations for Articulated Animation

This repository contains the source code for the CVPR'2021 paper Motion Representations for Articulated Animation by Aliaksandr Siarohin, Oliver Woodford, Jian Ren, Menglei Chai and Sergey Tulyakov.

For more qualitiative examples visit our project page.

Example animation

Here is an example of several images produced by our method. In the first column the driving video is shown. For the remaining columns the top image is animated by using motions extracted from the driving.

Screenshot

Installation

We support python3. To install the dependencies run:

pip install -r requirements.txt

YAML configs

There are several configuration files one for each dataset in the config folder named as config/dataset_name.yaml. See config/dataset.yaml to get the description of each parameter.

See description of the parameters in the config/vox256.yaml. We adjust the the configuration to run on 1 V100 GPU, training on 256x256 dataset takes approximatly 2 days.

Pre-trained checkpoints

Checkpoints can be found in checkpoints folder. Checkpoints are large, therefore we use git lsf to store them. Either use git lfs pull or download checkpoints manually from github.

Animation Demo

To run a demo, download a checkpoint and run the following command:

python demo.py  --config config/dataset_name.yaml --driving_video path/to/driving --source_image path/to/source --checkpoint path/to/checkpoint

The result will be stored in result.mp4. To use Animation via Disentaglemet add --mode avd, for standard animation add --mode standard instead.

Colab Demo

We prepared a demo runnable in google-colab, see: demo.ipynb.

Training

To train a model run:

CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --device_ids 0

The code will create a folder in the log directory (each run will create a time-stamped new folder). Checkpoints will be saved to this folder. To check the loss values during training see log.txt. You can also check training data reconstructions in the train-vis subfolder. Then to train Animation via disentaglement (AVD) use:

CUDA_VISIBLE_DEVICES=0 python run.py --checkpoint log/{folder}/cpk.pth --config config/dataset_name.yaml --device_ids 0 --mode train_avd

Where {folder} is the name of the folder created in the previous step. (Note: use backslash '' before space.) This will use the same folder where checkpoint was previously stored. It will create a new checkpoint containing all the previous models and the trained avd_network. You can monitor performance in log file and visualizations in train-vis folder.

Evaluation on video reconstruction

To evaluate the reconstruction performance run:

CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode reconstruction --checkpoint log/{folder}/cpk.pth

Where {folder} is the name of the folder created in the previous step. (Note: use backslash '' before space.) The reconstruction subfolder will be created in the checkpoint folder. The generated video will be stored to this folder, also generated videos will be stored in png subfolder in loss-less '.png' format for evaluation. Instructions for computing metrics from the paper can be found here.

TED dataset

For obtaining TED dataset run the following commands:

git clone https://github.com/AliaksandrSiarohin/video-preprocessing
cd video-preprocessing
python load_videos.py --metadata ../data/ted384-metadata.csv --format .mp4 --out_folder ../data/TED384-v2 --workers 8 --image_shape 384,384

Training on your own dataset

  1. Resize all the videos to the same size, e.g 256x256, the videos can be in '.gif', '.mp4' or folder with images. We recommend the latter, for each video make a separate folder with all the frames in '.png' format. This format is loss-less, and it has better i/o performance.

  2. Create a folder data/dataset_name with 2 subfolders train and test, put training videos in the train and testing in the test.

  3. Create a config file config/dataset_name.yaml. See description of the parameters in the config/vox256.yaml. Specify the dataset root in dataset_params specify by setting root_dir: data/dataset_name. Adjust other parameters as desired, such as the number of epochs for example. Specify id_sampling: False if you do not want to use id_sampling.

Additional notes

Citation:

@inproceedings{siarohin2021motion,
        author={Siarohin, Aliaksandr and Woodford, Oliver and Ren, Jian and Chai, Menglei and Tulyakov, Sergey},
        title={Motion Representations for Articulated Animation},
        booktitle = {CVPR},
        year = {2021}
}
Official PyTorch Implementation for InfoSwap: Information Bottleneck Disentanglement for Identity Swapping

InfoSwap: Information Bottleneck Disentanglement for Identity Swapping Code usage Please check out the user manual page. Paper Gege Gao, Huaibo Huang,

Grace Hešeri 56 Dec 20, 2022
PyTorch implementations of deep reinforcement learning algorithms and environments

Deep Reinforcement Learning Algorithms with PyTorch This repository contains PyTorch implementations of deep reinforcement learning algorithms and env

Petros Christodoulou 4.7k Jan 04, 2023
Subgraph Based Learning of Contextual Embedding

SLiCE Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks Dataset details: We use four public benchmark da

Pacific Northwest National Laboratory 27 Dec 01, 2022
Road Crack Detection Using Deep Learning Methods

Road-Crack-Detection-Using-Deep-Learning-Methods This is my Diploma Thesis ¨Road Crack Detection Using Deep Learning Methods¨ under the supervision of

Aggelos Katsaliros 3 May 03, 2022
PyElastica is the Python implementation of Elastica, an open-source software for the simulation of assemblies of slender, one-dimensional structures using Cosserat Rod theory.

PyElastica PyElastica is the python implementation of Elastica: an open-source project for simulating assemblies of slender, one-dimensional structure

Gazzola Lab 105 Jan 09, 2023
YOLOv5🚀 reproduction by Guo Quanhao using PaddlePaddle

YOLOv5-Paddle YOLOv5 🚀 reproduction by Guo Quanhao using PaddlePaddle 支持AutoBatch 支持AutoAnchor 支持GPU Memory 快速开始 使用AIStudio高性能环境快速构建YOLOv5训练(PaddlePa

QuanHao Guo 20 Nov 14, 2022
Simulation of moving particles under microscopic imaging

Simulation of moving particles under microscopic imaging Install scipy numpy scikit-image tiffile Run python simulation.py Read result https://imagej

Zehao Wang 2 Dec 14, 2021
An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

알고리즘 스터디 🔥 부스트캠프 웹모바일 6기 iOS 10조의 알고리즘 스터디 입니다. 개인적인 사정 등으로 S034, S055만 참가하였습니다. 스터디 목적 상진: 코테 합격 + 부캠끝나고 아침에 일어나기 위해 필요한 사이클 기완: 꾸준하게 자리에 앉아 공부하기 +

2 Jan 11, 2022
SplineConv implementation for Paddle.

SplineConv implementation for Paddle This module implements the SplineConv operators from Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Mül

北海若 3 Dec 29, 2021
Software Platform for solving and manipulating multiparametric programs in Python

PPOPT Python Parametric OPtimization Toolbox (PPOPT) is a software platform for solving and manipulating multiparametric programs in Python. This pack

10 Sep 13, 2022
Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

Yolov5 running on TorchServe (GPU compatible) ! This is a dockerfile to run TorchServe for Yolo v5 object detection model. (TorchServe (PyTorch librar

82 Nov 29, 2022
API for RL algorithm design & testing of BCA (Building Control Agent) HVAC on EnergyPlus building energy simulator by wrapping their EMS Python API

RL - EmsPy (work In Progress...) The EmsPy Python package was made to facilitate Reinforcement Learning (RL) algorithm research for developing and tes

20 Jan 05, 2023
Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation.

Distant Supervision for Scene Graph Generation Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation. Introduction The pape

THUNLP 23 Dec 31, 2022
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

English | 简体中文 Welcome to the PaddlePaddle GitHub. PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open

19.4k Jan 04, 2023
Pyramid Pooling Transformer for Scene Understanding

Pyramid Pooling Transformer for Scene Understanding Requirements: torch 1.6+ torchvision 0.7.0 timm==0.3.2 Validated on torch 1.6.0, torchvision 0.7.0

Yu-Huan Wu 119 Dec 29, 2022
VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation

VID-Fusion VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation Authors: Ziming Ding , Tiankai Yang, Kunyi Zhan

ZJU FAST Lab 86 Nov 18, 2022
Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations

Facebook Research 75 Dec 19, 2022
A blender add-on that automatically re-aligns wrong axis objects.

Auto Align A blender add-on that automatically re-aligns wrong axis objects. Usage There are three options available in the 3D Viewport Sidebar It

29 Nov 25, 2022
The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory

Simple-DMA a simple Dual Memory Architecture for classifications. based on the paper Dual-Memory Deep Learning Architectures for Lifelong Learning of

1 Jan 27, 2022
Simple Tensorflow implementation of Toward Spatially Unbiased Generative Models (ICCV 2021)

Spatial unbiased GANs — Simple TensorFlow Implementation [Paper] : Toward Spatially Unbiased Generative Models (ICCV 2021) Abstract Recent image gener

Junho Kim 16 Apr 15, 2022