Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

Last update: Nov 02, 2022

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Overview

AniFormer

This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu Sebe, Guoying Zhao.

Citation

If you use our code or paper, please consider citing:

@inproceedings{chen2021AniFormer,
  title={AniFormer: Data-driven 3D Animation withTransformer},
  author={Chen, Haoyu and Tang, Hao and Sebe, Nicu and Zhao, Guoying},
  booktitle={BMVC},
  year={2021}
}

Dependencies

Requirements:

python3.6
numpy
pytorch==1.1.0 and above
trimesh

Dataset preparation

Please download DFAUST dataset from DFAUST link for training the model.

Generate the driving sequence based on our script:

TBD

Training

The usage of our code is easy, just run the code below.

python train.py

Acknowledgement

Part of our code is based on

3D transfer: NPT，

Transformer framework: (https://github.com/lucidrains/vit-pytorch)

Many thanks!

License

MIT-2.0 License

Owner

A researcher in Oulu, Finland

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Related tags

Overview

AniFormer

Citation

Dependencies

Dataset preparation

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Acknowledgement

License

Owner

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Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

Related tags

Overview

AniFormer

Citation

Dependencies

Dataset preparation

Training

Acknowledgement

License

Owner

INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing

Code for "Human Pose Regression with Residual Log-likelihood Estimation", ICCV 2021 Oral

Multi-Output Gaussian Process Toolkit

​TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

Official PyTorch implementation of "RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on" (IJCAI-ECAI 2022)

MoViNets PyTorch implementation: Mobile Video Networks for Efficient Video Recognition;

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras

Final project code: Implementing MAE with downscaled encoders and datasets, for ESE546 FA21 at University of Pennsylvania

Code repo for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper.

Offline Reinforcement Learning with Implicit Q-Learning

Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios

Official Pytorch implementation of Meta Internal Learning

Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

SE3 Pose Interp - Interpolate camera pose or trajectory in SE3, pose interpolation, trajectory interpolation

Pytorch implementation of Implicit Behavior Cloning.

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Metric learning algorithms in Python

Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

Python based Advanced AI Assistant

TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.