Urgent! PhD Position F/M Physically-Grounded Video Generation Job Opening In Paris – Now Hiring INRIA
Contexte et atouts du poste
The Phd will be done at Inria in the Willow research team.
Mission confiée
Short Overview of the PhD Project:
This PhD thesis aims to enhance the physical consistency of current video generation
models by exploring various techniques to inject physics awareness into them.
PhD Project Description:
The motivation for this PhD thesis is to address a critical limitation in current video
generation models: their lack of consistency with the laws of physics.
Although these models
are increasingly adept at generating high-quality content that can almost perfectly match
real-world scenes, their capabilities to effectively model the underlying laws governing
dynamic interactions remain limited [1,2,3,4,6].
Simple scenarios, such as object freefall, are
sufficient to demonstrate these limitations [3].
Improving these capabilities is a fundamental
step towards building more robust models that can function as true world simulators.
Proposed Research Directions:
Different approaches have been explored to overcome the aforementioned limitations.
Some
works integrate 3D geometry and dynamics awareness as critical elements for generating
physically plausible videos [7].
Another interesting approach is model-based simulation
guidance, where physics engine simulations are used as an intermediate step to guide the
video generation process [4].
Furthermore, we consider post-training techniques to be
particularly promising.
In [3], the authors present a two-stage post-training pipeline
consisting of self-supervised fine-tuning on high-quality data and an Object Reward
Optimization (ORO) phase.
In [5], a novel framework called VideoREPA is proposed, which
distills physics understanding from video foundational models into text-to-video generation
models by aligning token-level relations.
Building on this, a primary direction for our research is the use of reasoning-capable models,
such as Large Language Models (LLMs) or Vision-Language Models (VLMs), to create
physically grounded scene descriptions that can guide the video generation process.
We
hypothesize that this could be a direct way to transfer the reasoning capabilities of
understanding models to generative ones.
Different settings and formats for this guidance,
from free-form text to more structured inputs, will be explored.
Moreover, we aim to investigate post-training techniques based on physics-informed reward
methods, such as those presented in [3].
Given that this work focuses on the specific case of
object freefall, a logical first step is to extend this approach to more complex and diverse
physical scenarios.
During the PhD thesis, the initial research directions will be adapted based on the evolution
of the field and the insights obtained during experimentation.
Evaluation and Benchmarking:
Recent benchmarks such as VideoPhy-2 [1], Phy-World [2], and PISA [3] are valuable
resources for measuring our contributions.
However, a key part of this project will also
involve identifying the limitations of current benchmarks.
Consequently, designing novel
tasks and evaluation strategies to better assess physical plausibility presents an additional
opportunity for contribution for this PhD project.
References:
[1] VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video
Generation
H.
Bansal, C.
Peng, Y.
Bitton, R.
Goldenberg, A.
Grover, K.
W.
Chang
[2] How Far is Video Generation from World Model: A Physical Law Perspective
B.
Kang, Y.
Yue, R.
Lu, Z.
Lin, Y.
Zhao, K.
Wang, G.
Huang, J.
Feng
[3] PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by
Watching Stuff Drop
C.
Li, O.
Michel, X.
Pan, S.
Liu, M.
Roberts, S.
Xie
[4] PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
S.
Liu, Z.
Ren, S.
Gupta, S.
Wang
[5] VideoREPA: Learning Physics for Video Generation through Relational Alignment with
Foundation Models
X.
Zhang, J.
Liao, S.
Zhang, F.
Meng, X.
Wan, J.
Yan, Y.
Cheng
[6] MOTIONCRAsFT: Physics-based Zero-Shot Video Generation
L.
S.
Aira, A.
Montanaro, E.
Aiello, D.
Valsesia, E.
Magli
[7] Towards Physical Understanding in Video Generation: A 3D Point Regularization
Approach
Y.
Chen, J.
Cao, A.
Kag, V.
Goel, S.
Korolev, C.
Jiang, S.
Tulyakov, J.
Ren
Principales activités
Main activities:
Analyse and implement related work.
Design novel innovative solutions.
Write progress reports and papers.
Present work at conferences.
Compétences
Technical skills and level required : programming skills are required.
Languages : English and possibly French.
Relational skills : Good communication skills.
Avantages
✨ Smart • Intelligent • Private • Secure
Practice for Any Interview Q&A (AI Enabled)
Predict interview Q&A (AI Supported)
Mock interview trainer (AI Supported)
Ace behavioral interviews (AI Powered)
Record interview questions (Confidential)
Master your interviews
Track your answers (Confidential)
Schedule your applications (Confidential)
Create perfect cover letters (AI Supported)
Analyze your resume (NLP Supported)
ATS compatibility check (AI Supported)
Optimize your applications (AI Supported)
O*NET Supported
O*NET Supported
O*NET Supported
O*NET Supported
O*NET Supported
European Union Recommended
Institution Recommended
Institution Recommended
Researcher Recommended
IT Savvy Recommended
Trades Recommended
O*NET Supported
Artist Recommended
Researchers Recommended
Create your account
Access your account
Create your professional profile
Preview your profile
Your saved opportunities
Reviews you've given
Companies you follow
Discover employers
O*NET Supported
Common questions answered
Help for job seekers
How matching works
Customized job suggestions
Fast application process
Manage alert settings
Understanding alerts
How we match resumes
Professional branding guide
Increase your visibility
Get verified status
Learn about our AI
How ATS ranks you
AI-powered matching
Join thousands of professionals who've advanced their careers with our platform
Unlock Your PhD Position Potential: Insight & Career Growth Guide
Real-time PhD Position Jobs Trends in Paris, France (Graphical Representation)
Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph below. This graph displays the job market trends for PhD Position in Paris, France using a bar chart to represent the number of jobs available and a trend line to illustrate the trend over time. Specifically, the graph shows 157 jobs in France and 45 jobs in Paris. This comprehensive analysis highlights market share and opportunities for professionals in PhD Position roles. These dynamic trends provide a better understanding of the job market landscape in these regions.
Great news! INRIA is currently hiring and seeking a PhD Position F/M Physically Grounded Video Generation to join their team. Feel free to download the job details.
Wait no longer! Are you also interested in exploring similar jobs? Search now: PhD Position F/M Physically Grounded Video Generation Jobs Paris.
An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at INRIA adheres to the cultural norms as outlined by Expertini.
The fundamental ethical values are:The average salary range for a PhD Position F/M Physically Grounded Video Generation Jobs France varies, but the pay scale is rated "Standard" in Paris. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.
Key qualifications for PhD Position F/M Physically Grounded Video Generation typically include Computer Occupations and a list of qualifications and expertise as mentioned in the job specification. Be sure to check the specific job listing for detailed requirements and qualifications.
To improve your chances of getting hired for PhD Position F/M Physically Grounded Video Generation, consider enhancing your skills. Check your CV/Résumé Score with our free Resume Scoring Tool. We have an in-built Resume Scoring tool that gives you the matching score for each job based on your CV/Résumé once it is uploaded. This can help you align your CV/Résumé according to the job requirements and enhance your skills if needed.
Here are some tips to help you prepare for and ace your job interview:
Before the Interview:To prepare for your PhD Position F/M Physically Grounded Video Generation interview at INRIA, research the company, understand the job requirements, and practice common interview questions.
Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the INRIA's products or services and be prepared to discuss how you can contribute to their success.
By following these tips, you can increase your chances of making a positive impression and landing the job!
Setting up job alerts for PhD Position F/M Physically Grounded Video Generation is easy with France Jobs Expertini. Simply visit our job alerts page here, enter your preferred job title and location, and choose how often you want to receive notifications. You'll get the latest job openings sent directly to your email for FREE!
