Pose Description Generation Model

:warning: In what follows, command lines are assumed to be launched from ./src/text2pose.

:warning: The evaluation of this model relies partly on a text-to-pose retrieval model and a text-conditionned pose generation model, see section Extra setup, below.

Model overview

• Input: 3D human pose;
• Output: pose description.

:crystal_ball: Demo

To generate text descriptions based on a pretrained model and example 3D human poses, run the following:

streamlit run generative_caption/demo_generative_caption.py -- --model_paths </path/to/model.pth>

:bulb: Tips: Specify several model paths to compare models together.

Extra setup

At the beginning of the bash script, indicate the shortnames of the trained models used for evaluation:

• fid: text-to-pose retrieval model (info),
• pose_generative_model: text-to-pose generative model (info),
• textret_model: text-to-pose retrieval model, eg. the same as for fid (info).

Indicate the paths to the models corresponding to each of these shortnames in shortname_2_model_path.txt.

:bullettrain_front: Train

:memo: Modify the variables at the top of the bash script to specify the desired model & training options.

Then use the following command:

bash generative_caption/script_generative_caption.sh 'train' <training phase: pretrain|finetune> <seed number>

Note for the finetuning step: In the script, pretrained defines the nickname of the pretrained model. The mapping between nicknames and actual model paths is given by shortname_2_model_path.txt. This means that if you train a model and intend to use its weights to train another, you should first write its path in shortname_2_model_path.txt, give it a nickname, and write this nickname in front of the pretrained argument in the script. The nickname will appear in the path of the finetuned model.

:dart: Evaluate

Use the following command:

bash generative_caption/script_generative_caption.sh 'eval'  </path/to/model.pth>