DESCRIPTION :
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2. Main Tasks (Plain Text)
The Postdoctoral Research Fellow will be responsible for the following main tasks. They will engage in Model Design and Development by designing and implementing novel architectures (e.g., Diffusion Models, Transformers, VAEs) specifically tailored for high-resolution, temporally consistent, and controllable video generation. A key focus is to develop conditional generation techniques to guide the Text-to-Video process using various complex inputs beyond a simple text prompt, such as image references, motion skeletons, semantic masks, or detailed scene descriptions. They will extensively research Video Editing and Manipulation, developing methods for high-fidelity post-generation video editing, allowing for non-destructive modification of generated videos (e.g., object replacement, style transfer, background alteration) while maintaining strong temporal consistency. Furthermore, they will investigate in-context editing mechanisms that enable precise changes to specific
segments or objects within a generated video based on new text or image prompts. A core part of the role is Addressing Key T2V Challenges. This includes tackling the fundamental challenge of temporal coherence and consistency, ensuring that generated videos do not suffer from "flickering" or object identity changes across frames, and developing strategies to improve semantic fidelity, resolving issues where models misinterpret complex text prompts. They will also explore methods for efficient training and inference to manage the significant computational cost associated with high-resolution, long-duration video generation, and address the difficulties of data scarcity and bias through techniques like data augmentation or cross-modal transfer learning. Finally, they will perform Evaluation and Benchmarking, establishing rigorous quantitative and qualitative metrics to assess the quality, editability, and controllability of the developed models. The fellow is
expected to prioritize Dissemination and Collaboration, which involves documenting research findings and publishing high-quality papers in top-tier machine learning and computer vision venues, actively participating in departmental seminars, and contributing to collaborative projects.
Code d'emploi : Chargé de Recherches (h/f)
Domaine professionnel actuel : Scientifiques
Niveau de formation : Bac+8
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée déterminée (CDD)
Compétences : Intelligence Artificielle, Vision par Ordinateur, Machine Learning, Montage Vidéo, Transfer Learning, Large Language Models, Variational Autoencoders, Anglais, Recherche, Edition, Analyse Comparative (Benchmark), Conception et Réalisation en Robotique
Courriel :
stephane.lathuiliere@inria.fr
Téléphone :
0139635511
Type d'annonceur : Employeur direct
