Post-Doctorant Postdoc Position Editing And Conditional Generation With Text-To-Video Generation Models H/F - INRIA

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.Post-Doctorant F/H Postdoc Position: Editing and Conditional Generation with Text-to-Video Generation Models

Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

A propos du centre ou de la direction fonctionnelle

Le Centre Inria de l'Université Grenoble Alpes, regroupe un peu moins de 600 personnes réparties au sein de 22 équipes de recherche et 7 services support à la recherche.

Son effectif est distribué sur 3 campus à Grenoble, en lien étroit avec les laboratoires et les établissements de recherche et d'enseignement supérieur (Université Grenoble Alpes, CNRS, CEA, INRAE, ...), mais aussi avec les acteurs économiques du territoire.

Présent dans les domaines du calcul et grands systèmes distribués, logiciels sûrs et systèmes embarqués, la modélisation de l'environnement à différentes échelles et la science des données et intelligence artificielle, le Centre Inria de l'Université Grenoble Alpe participe au meilleur niveau à la vie scientifique internationale par les résultats obtenus et les collaborations tant en Europe que dans le reste du monde.

Contexte et atouts du poste

Titre : Editing and Conditional Generation with Text-to-Video Generation Models

Supervision : Dr Stéphane Lathuilière (INRIA-UGA)

Funding : BPI contract

Contexte :Background and Motivation
Recent advancements in generative AI, and in particular diffusion models [1,2], have significantly enhanced the capabilities of text-to-video (T2V) models[3,4], allowing users to produce richly varied and imaginative scenes from natural language descriptions. These systems demonstrate strong scene diversity and flexibility, making them attractive for applications in entertainment, simulation, and human-computer interaction. However, a persistent limitation lies in their inability to enforce fine-grained conditioning. For example, while a T2V model can generate a person walking in a park, it cannot ensure that the person is wearing a specific garment or that the garment adapts convincingly to body shape, pose, and interaction with the environment. In contrast, virtual try-on (VTON) systems are highly specialized in clothing transfer tasks~[5], excelling at fine-grained conditioning of garments on target individuals. They can adapt clothing to morphology, pose, and texture details with remarkable realism. Yet, they lack the scene diversity and broader contextual awareness that T2V models offer. Current VTON approaches generally operate in isolation, focusing on clothing alignment rather than situating the dressed person within dynamic, complex environments. Bridging these two paradigms offers a powerful opportunity: to synthesize realistic humans dressed in controllable garments, embedded within richly described environments, and interacting with objects and other people. This integration could transform applications in e-commerce (immersive virtual try-on experiences), creative industries (fashion films, digital avatars), and simulation (training data for human-AI interaction).

Mission confiée

Research Objectives :
The primary mission of the Postdoctoral Research Fellow will be to advance the state-of-the-art in controllable and editable Text-to-Video (T2V) generation. The successful candidate will design, implement, and evaluate novel deep generative models and methodologies that address the current limitations of existing T2V systems. A core focus will be on achieving fine-grained conditional generation, allowing users to specify complex temporal, spatial, and stylistic constraints, as well as enabling intuitive and high-fidelity post-generation editing of the video content. The research will aim to produce models that are not only photorealistic but also exhibit high semantic fidelity, temporal coherence, and practical usability in creative and industrial applications.

Principales activités

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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.

Compétences

Compétences techniques et niveau requis :We are seeking a motivated PhD candidate with a strong background in one or more the following areas :

- speech processing, computer vision, machine learning,
- solid programmming skills
- interest in connecting AI with human cognition Prior experience with LLM, SpeechLMs, RL algorithms, or robotic platforms is a plus, but not mandatory

Langues : Anglais

Avantages

- Restauration subventionnée
- Transports publics remboursés partiellement
- Congés: 7 semaines de congés annuels + 10 jours de RTT (base temps plein) + possibilité d'autorisations d'absence exceptionnelle (ex : enfants malades, déménagement)
- Possibilité de télétravail et aménagement du temps de travail
- Équipements professionnels à disposition (visioconférence, prêts de matériels informatiques, etc.)
- Prestations sociales, culturelles et sportives (Association de gestion des oeuvres sociales d'Inria)
- Accès à la formation professionnelle
- Sécurité sociale