in2IN: Leveraging individual Information to Generate Human INteractions
🕹️ Usage
Input: The model gets as input the textual description of the overall interaction and the two individual descriptions from the interactants
Output (2,T,N,3): the model returns an array with the coordinates of the N joints of each interactant during a motion of T timesteps of duration,
from transformers import AutoModel
model = AutoModel.from_pretrained("pabloruizponce/in2IN", trust_remote_code=True)
model(textI, texti1, texti2)
📚 Citation
If you find our work helpful, please cite:
@InProceedings{Ruiz-Ponce_2024_CVPR,
author = {Ruiz-Ponce, Pablo and Barquero, German and Palmero, Cristina and Escalera, Sergio and Garc{\'\i}a-Rodr{\'\i}guez, Jos\'e},
title = {in2IN: Leveraging Individual Information to Generate Human INteractions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2024},
pages = {1941-1951}
}