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Anastasis Stathopoulos I am a Research Scientist at FAIR, Meta based in New York City. I received my PhD in Computer Science from Rutgers University, where I was advised by Dimitris Metaxas. Before that, I graduated with a diploma (BS + MEng) in Electical & Computer Engineering (ECE) from the National Technical University of Athens. I have spent the summers of 2020 and 2021 at Amazon Prime Video working on video understanding. My research centers on computer vision, combining multimodal and generative techniques, to study how humans interact in social contexts.
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Selected Publications
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Score-Guided Diffusion for 3D Human Recovery
Anastasis Stathopoulos, Ligong Han, Dimitris Metaxas Computer Vision and Pattern Recognition (CVPR), 2024 [project page] [code] [arxiv] [bibtex]
@inproceedings{stathopoulos2024score,
title = {Score-Guided Diffusion for 3D Human Recovery},
author = {Stathopoulos, Anastasis and Han, Ligong and Metaxas, Dimitris},
booktitle = {CVPR},
year = {2024}
}
Solving inverse problems for 3D human pose and shape reconstruction with score guidance in the latent space of a diffusion model. |
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Learning Articulated Shape with Keypoint Pseudo-labels from Web Images
Anastasis Stathopoulos, Georgios Pavlakos, Ligong Han, Dimitris Metaxas Computer Vision and Pattern Recognition (CVPR), 2023 [project page] [code & data] [arxiv] [bibtex]
@inproceedings{stathopoulos2023learning,
title = {Learning Articulated Shape with Keypoint Pseudo-labels from Web Images},
author = {Stathopoulos, Anastasis and Pavlakos, Georgios and Han, Ligong and Metaxas, Dimitris},
booktitle = {CVPR},
year = {2023}
}
Training models for 3D animal recovery with minimal annotations using large-scale collections of web images. |
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Exploiting Unlabeled Data with Vision and Language Models for Object Detection
Shiyu Zhao, Zhixing Zhang, Samuel Shulter, Long Zhao, Vijay Kumar, Anastasis Stathopoulos, Manmonahan Chandraker, Dimitris Metaxas European Conference on Computer Vision (ECCV), 2022 [code & data] [arxiv] [bibtex]
@inproceedings{zhao2022exploiting,
title={Exploiting unlabeled data with vision and language models for object detection},
author={Zhao, Shiyu and Zhang, Zhixing and Schulter, Samuel and Zhao, Long and Vijay Kumar, BG and Stathopoulos, Anastasis and Chandraker, Manmohan and Metaxas, Dimitris N},
booktitle={ECCV},
year={2022},
}
Improving open-vocabulary and semi-supervised object detection with pseudo-labels from VLMs. |
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Dual Projection Generative Adversarial Networks for Conditional Image Generation
Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos, Yu Tian , Ruijiang Gao, Asim Kadav, Dimitris Metaxas International Conference on Computer Vision (ICCV), 2021 [code] [arxiv] [bibtex]
@inproceedings{han2021dual,
title={Dual Projection Generative Adversarial Networks for Conditional Image Generation},
author={Han, Ligong and Min, Martin Renqiang and Stathopoulos, Anastasis and Tian, Yu and Gao, Ruijiang and Kadav, Asim and Metaxas, Dimitris N},
booktitle={ICCV},
year={2021}
}
Improving class separability and sample quality by balancing data and label matching in cGANs. |
Credits to Jon Barron and Georgia Gkioxari