COINS

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 106)

code release of paper "Compositional Human-Scene Interaction Synthesis with Semantic Control"

COINS 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Compositional Human-Scene Interaction Synthesis with Semantic Control (COINS)
This repository contains the implementation of our paper Compositional Human-Scene Interaction Synthesis with Semantic Control and the PROX-S dataset expansion.

You can find more information on our [project page](https://zkf1997.github.io/COINS/index.html).
![teaser](https://raw.githubusercontent.com/zkf1997/COINS/main/images/COINS_teaser.svg)

## Installation
This implementation is tested on the following platform:
```
Python 3.7, PyTorch 1.11.0 with CUDA 11.3 and cuDNN 8.2.0, PyTorch3D 0.6.2, Ubuntu 20.04
``` 
We recommend to manage the dependencies using conda.
Please first install CUDA and ensure NVCC works. 
You can then create a conda environment using provided yml file as following:
```
conda env create -n COINS -f environment.yml
```
External data files:
* To use the SMPL-X body models, please download the weights from [SMPL-X website](https://smpl-x.is.tue.mpg.de/index.html) and set `smplx_model_folder` in [config](configuration/config.py).
* Please download [POSA](https://posa.is.tue.mpg.de/index.html) and extract the `mesh_ds` for body mesh downsampling. Please set `mesh_ds_folder` in [config](configuration/config.py) accordingly.

(Optional): If you want off-screen image rendering from SSH session or in a headless server, please install `osmesa` and set OpenGL at the start of scripts as following:
```
os.environ['PYOPENGL_PLATFORM'] = 'osmesa' 
```
## PROX-S dataset
![PROX-S](https://raw.githubusercontent.com/zkf1997/COINS/main/images/PROX-S.png)
The PROX-S dataset is a human-scene interaction dataset annotated on top of [PROX](https://prox.is.tue.mpg.de/index.html) and [PROX-E](https://github.com/yz-cnsdqz/PSI-release), which contains:
* scene instance segmentation
* per-frame interaction semantic labels and SMPL-X body estimation

You can download PROX-S expansion [here](https://drive.google.com/drive/folders/1nV_S_m0Yl8p3sOaCLpz5IIZxoL4_TAtE?usp=sharing).
Please also download `scenes`, `cam2world`, `sdf`, and `body_segments` from [PROX](https://prox.is.tue.mpg.de/index.html), `scenes_semantics` from [PROX-E](https://github.com/yz-cnsdqz/PSI-release), and set the paths in [config](configuration/config.py).

You can render scene segmentation and log object instances by:
```
cd data; python scene.py
```
Regarding interaction data with semantic labels, we provide a script [load_interaction](data/load_interaction.py) to load interaction data of specific action or action-object pair, and visualize the interaction with object instances.

## Pre-trained Models
We provide the pre-trained models for BodyVAE and PelvisVae [here](https://drive.google.com/drive/folders/1nV_S_m0Yl8p3sOaCLpz5IIZxoL4_TAtE?usp=sharing).
Please set `checkpoint_folder` in [config](configuration/config.py).

## Interaction synthesis in PROX scenes
For synthesizing interactions with semantic control, please run [two_stage_sample](interaction/two_stage_sample.py) as following:
```
cd interaction
# sample interactions
python two_stage_sample.py --exp_name test --lr_posa 0.01 --max_step_body 100  --weight_penetration 100 --weight_pose 10 --weight_init 0.01  --weight_contact_semantic 1 --num_sample 8 --num_try 1  --visualize 1 --full_scene 1 --interaction 'sit on-chair' --scene_name 'MPH16'
python two_stage_sample.py --exp_name test --lr_posa 0.01 --max_step_body 100  --weight_penetration 100 --weight_pose 10 --weight_init 0.01  --weight_contact_semantic 1 --num_sample 8 --num_try 1  --visualize 1 --full_scene 1 --interaction 'sit on-chair+touch-table' --scene_name 'MPH16'
# compositional interaction synthesis using models trained only on atomic data
python two_stage_sample.py --exp_name test --lr_posa 0.01 --max_step_body 100 --weight_penetration 100 --weight_pose 10 --weight_init 0.01 --weight_contact_semantic 1 --num_sample 8 --num_try 1 --visualize 1 --full_scene 1 --interaction 'sit on-chair+touch-table' --scene_name 'MPH16' --composition 1 --transform_checkpoint 'pelvis_atomic.ckpt' --interaction_checkpoint 'body_atomic.ckpt' 
```
The synthesized results can be found in `./results/two_stage`.
Currently, the script supports choosing PROX scenes by `--scene_name` and interaction by `--interaction` in the format of `action1-object1[+action2-object2]`. The script iterates over all instances of specified category in the input scene and generates interactions for each action-instance pair.

You may freely manipulate the weights.

## Training Generative Models
We use [PyTorch Lightning](https://www.pytorchlightning.ai/) for model training. Please refer to its documentation if you want to customize trainer features such as logging, checkpoint, resume training, etc.

To train BodyVAE, please run:
```
cd interaction; python interaction_trainer.py --expr_name two_contact --model InteractionVAE --weight_kl 1 --used_interaction 'all' --robust_kl 1 --batch_size 8 --latent_dim 128 --num_obj_points 8192 --num_obj_keypoints 256 --use_pointnet2 1 --body_type mesh --template_type tpose --use_annealing 1 --latent_usage memory --second_stage 2 --use_contact_feature 1 --weight_contact_rec 1 --weight_contact_dist 1 --weight_normal 0.1 --weight_edge_length 0.2 --relative_length 1 --data_overwrite 0 --include_motion 1 --weight_normal_consistency 0.05 --use_regressor 1 --contact_scene_thresh 0.01 --contact_semantic_thresh 0.05 
```

To train PelvisVAE, please run:
```
cd interaction; python transform_trainer.py --expr_name floor_all --model InteractionVAE --weight_kl 1 --weight_pelvis 3 --weight_orient 1 --weight_dist 1 --weight_coord 1 --weight_penetration 3  --used_interaction 'all' --use_annealing 0 --robust_kl 1 --batch_size 8 --use_augment 1 --thresh_penetration 0.25 --second_stage 10 --num_obj_keypoints 256 --num_obj_points 8192 --num_layers 2 --embedding_dim 64 --latent_dim 6 --use_prox_single 1 --include_motion 1 --use_annotate 0 --data_overwrite 0 --use_pointnet2 1 --use_floor_height 1
```
The trained models with logs can be found in `./results/interaction` and `./results/transform`. You can set `--debug 1` to render samples for inspection during training.

## Baselines
<details>
<summary>In case you want to run the baselines</summary>

### PiGraph-X
The code for PiGraph-X can be found in the folder [pigraph](./pigraph). 
To synthesize interactions using PiGraph-X, you can run:
```
cd pigraph; python synthesize.py --use_penetration 0 --composition 0 --visualize 1 --gender neutral --num_results 8 --num_skeletons 8 --num_translations 32 --num_rotations 12 --interaction 'sit on-chair' --scene_name 'MPH16' --save_dir pigraph_normal
```
You can refer to [synthesize.sh](./pigraph/synthesize.sh) for large-scale synthesis for evaluation.

### POSA-I
The POSA-I method consists of the following three steps:
#### Train generative model for body with contact features
Please see [body_trainer.py](interaction/body_trainer.py)
#### Sample bodies with contact feature
Please see [sample_body_feature.py](interaction/sample_body_feature.py).
#### Place bodies into scenes using POSA
Please first download the [POSA](https://posa.is.tue.mpg.de/index.html) code and data files. Then merge the [POSA](./POSA) folder with the POSA code from original author.
Please check the instructions in orginal POSA repo and then refer to [synthesize.py](POSA/synthesize.py) for synthesis.

The code of POSA-I is currently distributed in [inteaction](./interaction) and [POSA](./POSA), as well as the origial POSA repo. It is currently kind of messy and will potentially be restructured.
</details>

## Evaluation
The [evaluation](evaluation/) folder contains the scripts for evaluation.
* [load_results.py](evaluation/load_results.py) loading interaction results from different methods. 
* [render_results.py](evaluation/render_results.py) renders interactions in multi-views 
* [eval_results.py](evaluation/eval_results.py) evaluates the physical plausibility, semantic contact, and diversity metrics of generated results.

## License
We employ MIT license for this repository, with the exceptions of codes borrowed or modified from other works:
* [mpcat40.tsv](./configuration/mpcat40.tsv) from [Matterport](https://github.com/niessner/Matterport/blob/master/metadata/mpcat40.tsv).
* [smplx_vert_segmentation.json](./configuration/smplx_vert_segmentation.json) from [Meshcapade](#https://github.com/Meshcapade/wiki/blob/main/assets/SMPL_body_segmentation/smplx/smplx_vert_segmentation.json).
* [chamfer_distance.py](interaction/chamfer_distance.py) from [Pytorch3D](https://pytorch3d.org/). 
* [loss.py](interaction/loss.py) from [Pose2Mesh](https://github.com/hongsukchoi/Pose2Mesh_RELEASE/blob/master/lib/core/loss.py).
* [mesh.py](interaction/mesh.py), [posa_utils](interaction/posa_utils.py), [viz_utils](interaction/viz_util.py) from [POSA](https://posa.is.tue.mpg.de/index.html).
* [eulerangles.py](interaction/eulerangles.py) from [transforms3d](https://github.com/matthew-brett/transforms3d/blob/master/transforms3d/euler.py).
* [pointnet2.py](interaction/pointnet2.py) from [pointnet_pointnet2](https://github.com/yanx27/Pointnet_Pointnet2_pytorch/blob/master/models/pointnet2_utils.py).
* [smplx_regressor.py](interaction/smplx_regressor.py) from [GraphCMR](https://github.com/nkolot/GraphCMR).
* [transformer.py](interaction/transformer.py) from [pytorch](https://pytorch.org/docs/stable/_modules/torch/nn/modules/transformer.html#Transformer).

We sincerely thank the authors for releasing the codes and please check their respective licenses for these parts.

COINS

COINS 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

COINS

COINS 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README