1. 

   Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

   Feng Liu, Luan Tran, Xiaoming Liu

   Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the ...

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   Keywords: Generic Object 3D Reconstruction, 3D Shape Reconstruction, Semantic Segmentation

2. 

   3D Face Modeling from Diverse Raw Scan Data

   Feng Liu, Luan Tran, Xiaoming Liu

   Traditional 3D face models learn a latent representation of faces using linear subspaces from no more than 300 training scans of a single database. The main roadblock of building a large-scale face model from diverse 3D databases lies in the lack of dense correspondence among raw scans. To address these ...

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   Keywords: 3D Face Modeling, Face Reconstruction, 3D Shape Reconstruction

3. 

   Gait Recognition via Disentangled Representation Learning

   Ziyuan Zhang, Luan Tran, Xi Yin, Yousef Atoum, Xiaoming Liu, Jian Wan, Nanxin Wang

   Gait, the walking pattern of individuals, is one of the most important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To ...

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   Keywords: Biometrics, Gait Recognition, Database

4. 

   Towards Interpretable Face Recognition

   Bangjie Yin, Luan Tran, Haoxiang Li, Xiaohui Shen, Xiaoming Liu

   Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions. Recent works further push the interpretability in the network learning ...

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   Keywords: Face Recognition, Biometrics

5. 

   MSU-AVIS dataset: Fusing Face and Voice Modalities for Biometric Recognition in Indoor Surveillance Videos

   Anurag Chowdhury, Yousef Atoum, Luan Tran, Xiaoming Liu, Arun Ross

   Indoor video surveillance systems often use the face modality to establish the identity of a person of interest. However, the face image may not offer sufficient discriminatory information in many scenarios due to substantial variations in pose, illumination, expression, resolution and distance between the subject and the camera.

   In such ...

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   Keywords: Application, Biometrics, Face Recognition, Surveillance, Multi-modality

6. 

   Nonlinear 3D Face Morphable Model

   Luan Tran, Xiaoming Liu

   As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, including model fitting, image synthesis, etc. Conventional 3DMM is learned from a collection of wellcontrolled 2D face images with associated 3D face scans, and represented by two sets of ...

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   Keywords: Face Alignment, Face Reconstruction, 3D Face Modeling, 3D Shape Reconstruction

7. 

   Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild

   Luan Tran, Kihyuk Sohn, Xiang Yu, Xiaoming Liu, Manmohan Chandraker

   Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features and input pixels. We propose that advantages may be derived by jointly investigating the two, in the form of different level insights that lead ...

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   Keywords: Domain Adaptation

8. 

   Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

   Luan Tran, Xi Yin, Xiaoming Liu

   The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks ...

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   Keywords: Face Recognition, Surveillance, Biometrics, Face Synthesis

9. 

   Learning to Fuse Information with Missing Modalities

   Luan Tran, Xiaoming Liu, Jiayu Zhou, Rong Jin

   One of the key GEOINT capabilities is to be able to automatically recognize a large array of objects from visual data. Depending on the resolution of imagery, objects may range from specific locations or scenes, road, building, forest to vehicle, human, etc. This is clearly a technically challenging problem for ...

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   Keywords: Data Imputation, Multi-modality

2021

• Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
  Feng Liu, Luan Tran, Xiaoming Liu
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2021), Nashville, TN, Jun. 2021
  Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code | Video
@inproceedings{ fully-understanding-generic-objects-modeling-segmentation-and-reconstruction,
  author = { Feng Liu and Luan Tran and Xiaoming Liu },
  title = { Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Nashville, TN },
  month = { June },
  year = { 2021 },
}

2020

• On Learning Disentangled Representations for Gait Recognition
  Ziyuan Zhang, Luan Tran, Feng Liu, Xiaoming Liu
  IEEE Transactions on Pattern Analysis and Machine Intelligence, , May. 2020 (in press)
  Bibtex | PDF | arXiv | Project Webpage | Code
@article{ on-learning-disentangled-representations-for-gait-recognition,
  author = { Ziyuan Zhang and Luan Tran and Feng Liu and Xiaoming Liu },
  title = { On Learning Disentangled Representations for Gait Recognition },
  journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence },
  month = { May },
  year = { 2020 },
}

2019

• On Learning 3D Face Morphable Model from In-the-wild Images
  Luan Tran, Xiaoming Liu
  IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, No. 1, pp.157-171, , Jun. 2019
  Bibtex | PDF | arXiv | Project Webpage | Code
@article{ on-learning-3d-face-morphable-model-from-in-the-wild-images,
  author = { Luan Tran and Xiaoming Liu },
  title = { On Learning 3D Face Morphable Model from In-the-wild Images },
  journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence },
  volume = { 43 },
  number = { 1 },
  month = { June },
  year = { 2019 },
  pages = { 157--171 },
}
• 3D Face Modeling from Diverse Raw Scan Data
  Feng Liu, Luan Tran, Xiaoming Liu
  In Proceeding of International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, Oct. 2019 (Oral presentation)
  Bibtex | PDF | arXiv | Supplemental | Poster | Project Webpage | Code | Video
@inproceedings{ 3d-face-modeling-from-diverse-raw-scan-data,
  author = { Feng Liu and Luan Tran and Xiaoming Liu },
  title = { 3D Face Modeling from Diverse Raw Scan Data },
  booktitle = { In Proceeding of International Conference on Computer Vision },
  address = { Seoul, South Korea },
  month = { October },
  year = { 2019 },
}
• Towards Interpretable Face Recognition
  Bangjie Yin*, Luan Tran*, Haoxiang Li, Xiaohui Shen, Xiaoming Liu
  In Proceeding of International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, Oct. 2019 (Oral presentation)
  Bibtex | PDF | arXiv | Project Webpage | Code | Video
@inproceedings{ towards-interpretable-face-recognition,
  author = { Bangjie Yin* and Luan Tran* and Haoxiang Li and Xiaohui Shen and Xiaoming Liu },
  title = { Towards Interpretable Face Recognition },
  booktitle = { In Proceeding of International Conference on Computer Vision },
  address = { Seoul, South Korea },
  month = { October },
  year = { 2019 },
}
• Gait Recognition via Disentangled Representation Learning
  Ziyuan Zhang, Luan Tran, Xi Yin, Yousef Atoum, Jian Wan, Nanxin Wang, Xiaoming Liu
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, Jun. 2019 (Oral presentation)
  Bibtex | PDF | arXiv | Project Webpage | Code
@inproceedings{ gait-recognition-via-disentangled-representation-learning,
  author = { Ziyuan Zhang and Luan Tran and Xi Yin and Yousef Atoum and Jian Wan and Nanxin Wang and Xiaoming Liu },
  title = { Gait Recognition via Disentangled Representation Learning },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Long Beach, CA },
  month = { June },
  year = { 2019 },
}
• Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
  Luan Tran, Kihyuk Sohn, Xiang Yu, Xiaoming Liu, Manmohan Chandraker
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, Jun. 2019
  Bibtex | PDF | arXiv | Supplemental | Poster | Project Webpage
@inproceedings{ gotta-adapt-em-all-joint-pixel-and-feature-level-domain-adaptation-for-recognition-in-the-wild,
  author = { Luan Tran and Kihyuk Sohn and Xiang Yu and Xiaoming Liu and Manmohan Chandraker },
  title = { Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Long Beach, CA },
  month = { June },
  year = { 2019 },
}
• Towards High-fidelity Nonlinear 3D Face Morphable Model
  Luan Tran, Feng Liu, Xiaoming Liu
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, Jun. 2019
  Bibtex | PDF | arXiv | Poster | Project Webpage | Code
@inproceedings{ towards-high-fidelity-nonlinear-3d-face-morphable-model,
  author = { Luan Tran and Feng Liu and Xiaoming Liu },
  title = { Towards High-fidelity Nonlinear 3D Face Morphable Model },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Long Beach, CA },
  month = { June },
  year = { 2019 },
}

2018

• Representation Learning by Rotating Your Faces
  Luan Tran, Xi Yin, Xiaoming Liu
  IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 12, pp.3007-3021, , Sep. 2018
  Bibtex | PDF | arXiv | Project Webpage | Code
@article{ representation-learning-by-rotating-your-faces,
  author = { Luan Tran and Xi Yin and Xiaoming Liu },
  title = { Representation Learning by Rotating Your Faces },
  journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence },
  volume = { 41 },
  number = { 12 },
  month = { September },
  year = { 2018 },
  pages = { 3007--3021 },
}
• Nonlinear 3D Face Morphable Model
  Luan Tran, Xiaoming Liu
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, Jun. 2018 (Spotlight)
  Bibtex | PDF | arXiv | Poster | Project Webpage | Code
@inproceedings{ nonlinear-3d-face-morphable-model,
  author = { Luan Tran and Xiaoming Liu },
  title = { Nonlinear 3D Face Morphable Model },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Salt Lake City, UT },
  month = { June },
  year = { 2018 },
}
• MSU-AVIS dataset: Fusing Face and Voice Modalities for Biometric Recognition in Indoor Surveillance Videos
  Anurag Chowdhury, Yousef Atoum, Luan Tran, Xiaoming Liu, Arun Ross
  In Proceeding of International Conference on Pattern Recognition (ICPR 2018), Beijing, China, Aug. 2018
  Bibtex | PDF | Poster | Project Webpage
@inproceedings{ msu-avis-dataset-fusing-face-and-voice-modalities-for-biometric-recognition-in-indoor-surveillance-videos,
  author = { Anurag Chowdhury and Yousef Atoum and Luan Tran and Xiaoming Liu and Arun Ross },
  title = { MSU-AVIS dataset: Fusing Face and Voice Modalities for Biometric Recognition in Indoor Surveillance Videos },
  booktitle = { In Proceeding of International Conference on Pattern Recognition },
  address = { Beijing, China },
  month = { August },
  year = { 2018 },
}

2017

• Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
  Luan Tran, Xi Yin, Xiaoming Liu
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, Jul. 2017 (Oral presentation)
  Bibtex | PDF | Project Webpage | Code
@inproceedings{ disentangled-representation-learning-gan-for-pose-invariant-face-recognition,
  author = { Luan Tran and Xi Yin and Xiaoming Liu },
  title = { Disentangled Representation Learning GAN for Pose-Invariant Face Recognition },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Honolulu, HI },
  month = { July },
  year = { 2017 },
}
• Missing Modalities Imputation via Cascaded Residual Autoencoder
  Luan Tran, Xiaoming Liu, Jiayu Zhou, Rong Jin
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, Jul. 2017
  Bibtex | PDF | Project Webpage | Code
@inproceedings{ missing-modalities-imputation-via-cascaded-residual-autoencoder,
  author = { Luan Tran and Xiaoming Liu and Jiayu Zhou and Rong Jin },
  title = { Missing Modalities Imputation via Cascaded Residual Autoencoder },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Honolulu, HI },
  month = { July },
  year = { 2017 },
}