publications

2026

1. ECCV

   

   EmoteGPT: 3D Human Facial Expressions from Natural Language Descriptions

   Haoran Wang, Mohit Mendiratta, Christian Theobalt, and Adam Kortylewski

   In European Conference on Computer Vision, 2026
2. ECCV

   

   SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models

   Olaf Dünkel^*, Basavaraj Sunagad^*, Haoran Wang, David T. Hoffmann, Christian Theobalt, and Adam Kortylewski

   In European Conference on Computer Vision, 2026

2025

1. TPAMI

   

   ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception

   Christos Sakaridis^*, Haoran Wang^*, Ke Li, René Zurbrügg, Arpit Jadon, Wim Abbeloos, Daniel Olmeda Reino, Luc Van Gool, and Dengxin Dai

   IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025
2. CVPR

   

   PersonaHOI: Effortlessly Improving Face Personalization in Human-Object Interaction Generation

   Xinting Hu^*, Haoran Wang^*, Jan Eric Lenssen, and Bernt Schiele

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2025

2023

1. CVPR

   

   MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation

   Lukas Hoyer, Dengxin Dai, Haoran Wang, and Luc Van Gool

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

2021

1. IJCV

   

   Scale-Aware Domain Adaptive Faster R-CNN

   Yuhua Chen, Haoran Wang, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool

   International Journal of Computer Vision, Jul 2021

   Abs DOI

   Object detection typically assumes that training and test samples are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch may lead to a significant performance drop. In this work, we present Scale-aware Domain Adaptive Faster R-CNN, a model aiming at improving the cross-domain robustness of object detection. In particular, our model improves the traditional Faster R-CNN model by tackling the domain shift on two levels: (1) the image-level shift, such as image style, illumination, etc., and (2) the instance-level shift, such as object appearance, size, etc. The two domain adaptation modules are implemented by learning domain classifiers in an adversarial training manner. Moreover, we observe that the large variance in object scales often brings a crucial challenge to cross-domain object detection. Thus, we improve our model by explicitly incorporating the object scale into adversarial training. We evaluate our proposed model on multiple cross-domain scenarios, including object detection in adverse weather, learning from synthetic data, and cross-camera adaptation, where the proposed model outperforms baselines and competing methods by a significant margin. The promising results demonstrate the effectiveness of our proposed model for cross-domain object detection. The implementation of our model is available at .

2020

1. ECCV

   

   Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation

   Haoran Wang^*, Tong Shen^*, Wei Zhang, Ling-Yu Duan, and Tao Mei

   In European Conference on Computer Vision, 2020

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