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MultiAct: Long-Term 3D Human Motion Generation from Multiple Actions

Introduction

This repo is official PyTorch implementation of MultiAct: Long-Term 3D Human Motion Generation from Multiple Actions (AAAI 2023 Oral.).

Quick demo

  • Install PyTorch and Python >= 3.8.13. Run sh requirements.sh to install the python packages. You should slightly change torchgeometry kernel code following here.
  • Download the pre-trained model from here and unzip in ${ROOT}/output.
  • Prepare BABEL dataset following here.
  • Prepare SMPL-H body model following here.
  • Run python generate.py --env gen --gpu 0 --mode gen_short for the short-term generation.
  • Run python generate.py --env gen --gpu 0 --mode gen_long for the long-term generation.
  • Generated motions are stored in ${ROOT}/output/gen_release/vis/.

Preparation

BABEL dataset

  • Prepare BABEL dataset following here.
  • Unzip AMASS and babel_v1.0_release folder in dataset directory as below.
${ROOT}  
|-- dataset
|   |-- BABEL
|   |   |-- AMASS
|   |   |-- babel_v1.0_release

SMPLH body model

  • Prepare SMPL-H body model from here.
  • Place the human body 3D model files in human_models directory as below.
${ROOT}  
|-- human_models
|   |-- SMPLH_MALE.pkl
|   |-- SMPLH_FEMALE.pkl
|   |-- SMPLH_NEUTRAL.npz

Body visualizer

  • We use the body visualizer code released in this repo.
  • Running requirements.sh installs the body visualizer in ${ROOT}/body_visualizer/.

Running MultiAct

Train

  • Run python train.py --env train --gpu 0.
  • Running this code will override the downloaded checkpoints.

Test

  • Run python test.py --env test --gpu 0.
  • Note that the variation of the generation result depends on the random sampling of the latent vector from estimated prior Gaussian distribution. Thus, the evaluation result may be slightly different from the reported metric scores in our paper.
  • Evaluation result is stored in the log file in ${ROOT}/output/test_release/log/.

Short-term generation

  • Run python generate.py --env gen --gpu 0 --mode gen_short for the short-term generation.
  • Generated motions are stored in ${ROOT}/output/gen_release/vis/single_step_unseen.

Long-term generation

Generating long-term motion at once

  • Run python generate.py --env gen --gpu 0 --mode gen_long for the long-term generation.
  • Generated motions are stored in ${ROOT}/output/gen_release/vis/long_term/(exp_no)/(sample_no)/(step-by-step motion).

Generating long-term motion step-by-step via resuming from previous generation results

  • Modify environment file ${ROOT}/envs/gen.yaml to match your purpose.
  • Mark resume: True in environment file.
  • Specify resume_exp, resume_sample, and resume_step to determine which point to continue the generation.
  • Generated motions are stored in ${ROOT}/output/gen_release/vis/long_term/(next_exp_no)/(sample_no)/(step-by-step motion).

Reference

@InProceedings{Lee2023MultiAct,  
author = {Lee, Taeryung and Moon, Gyeongsik and Lee, Kyoung Mu},  
title = {MultiAct: Long-Term 3D Human Motion Generation from Multiple Action Labels},  
booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},  
year = {2023}  
}