Phd Position F/m Phd Position Computer Vision

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria Université Côte d’Azur center counts 36 research teams as well as 7 support departments. The center's staff (about 500 people including 320 Inria employees) is made up of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrative staff. 1/3 of the staff are civil servants, the others are contractual agents. The majority of the center’s research teams are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Four teams are based in Montpellier and two teams are hosted in Bologna in Italy and Athens. The Center is a founding member of Université Côte d'Azur and partner of the I-site MUSE supported by the University of Montpellier.

**Contexte et atouts du poste**:
**Team**

The STARS research team combines advanced theory with cutting edge practice focusing on cognitive vision systems.

**Mission confiée**:
The Inria STARS team is seeking for a Ph.D. researcher with strong background in computer vision, deep learning and machine learning.

**Principales activités**:
Context:
Generative adversarial networks (GANs) [1] have witnessed increased interest from academia and industry, due to exceptional capacity in generating highly realistic _images_ [2, 3, 4, 5, 6, 7]. _Videos_ signify more complex data, due to the additional temporal dimension. While some research works showed early results in video generation [8-11], there are many open questions in the field.
- Model architecture

The thesis firstly will investigate, how to design model architecture for generator and discriminator in generative models. We will explore traditional model architectures such as CNN and RNN, as well as Transformer-based generators. Our objective will be to explore whether we can design a unified model architecture that generalizes over categories, such as human bodies and faces. We will study how to connect different architectures, in order to create such a general system for cross-category generation.
- 3D-aware generation

Learning 3D-aware models from 2D data has become a popular research topic in image generation. In this thesis, we will go one step further in this direction to explore novel view synthesis in video generation. We intend to combine jointly state-of-the-art novel view synthesis techniques with video generation, aiming at creating 3D-aware video generation. Our idea is to explore implicit representation (e.g., NeRF), explicit representation (e.g., 3D representation), as well as hybrid (implicit-explicit) representation in video generation models. One objective will be to design an efficient and effective representation for novel-view synthesis in video generation.
- Generalizability

Finally, we will aim to design a universal model which is able to generate videos across categories. Most of current models focus on generating single category (e.g., faces, sky ). Currently, there is no models, which are able to generate complex multi-category videos (e.g. Kinetics-600). We plan to increase the complexity of video generative models and design a large-scale video GAN. The objective is to study whether big generative models are able to capture the distribution of complex video datasets and create semantic meaningful videos.

[1] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, 2014, pp. 2672-2680.
[2] T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 4401-4410.
[3] C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang et al., “Photo-realistic single image super
- resolution using a generative adversarial network.” in CVPR, 2017.
[4] L. Ma, Q. Sun, S. Georgoulis, L. Van Gool, B. Schiele, and M. Fritz, “Disentangled person image generation,” in CVPR, 2018.
[5] T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization for generative adversarial networks,” in ICLR, 2018.
[6] T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, and X. He, “Attngan : Fine-grained text to image generation with attentional generative adversarial networks,” in CVPR, 2018.
[7] B. Zhao, L. Meng, W. Yin, and L. Sigal, “Image generation from layout,” in CVPR, 2019.

[8] C. Vondrick, H. Pirsiavash, and A. Torralba, “Generating videos with scene dynamics,” in NIPS, 2016.
[9] M. Saito, E. Matsumoto, and S. Saito, “Temporal generative adversarial nets with singular value clipping,” in Proceedings of the IEEE International Conference on Computer Vision, 20