Publiée 5 août 2026
Post-Doctoral Research Visit F/M Characterization of motion anomalies in videos: Application to the detection of AI-generated videos
Inria
Rennes, Hauts-de-France 60420, France
CDI
A propos du centre ou de la direction fonctionnelle
The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Contexte et atouts du poste
Funding: Funding for this postdoc position has not yet been secured. The selection of a postdoc candidate is a prerequisite. This is because the funding sources being sought require that the application be submitted by the postdoc fellow. The duration of the postdoc position may range from 12 to 24 months, depending on the funding secured.
Mission confiée
Subject: A video is more than just a succession of images because it incorporates the fundamental temporal dimension of motion. Motion indeed carries intrinsic information in videos. This characteristic is essential to the analysis. Additionally, motion properties become more explicit over time. Motion is fully represented by the velocity field computed between two images at every time instant of the video. In the long term, the consecutive velocity fields can be viewed as a multivariate time series. The first objective of the postdoc is to create parsimonious time series through learning that can adequately represent motion content and are semantically suitable for generic tasks such as detection, recognition, and classification. More specifically, we will explore anomaly detection in videos, a challenge shared by many applications, albeit in various forms. We will focus on the characterization of motion anomalies based on learned time series. Motion anomalies can manifest themselves in three main ways: deviations from normal behavior or context (e.g., a vehicle driving the wrong way on a highway), sudden divergences (e.g., a vehicle leaving the road, panic in a crowd), or non-natural motion (e.g., presumably in AI-generated videos). This last category will lead us to address the detection of AI-generated videos, whose rapid rise and growing ability to mimic reality raise major societal and economic issues. While the detection of AI-generated still images has been the subject of much research and even challenges, there are still relatively few methods designed specifically for videos. Methods developed for detecting AI-generated images are not effective for videos because they fail to recognize an essential video characteristic: its temporal dimension. This postdoc will build on our recent work, particularly on salient trajectory detection, long-term unsupervised motion segmentation, and automatic detection of AI-generated content.
Key words: Motion in image sequences, time series, learning, anomaly characterization, detection of AI-generated videos
References
- L. Maczyta, P. Bouthemy, and O. Le Meur. Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder, IEEE Trans. on Circuits and Systems for Video Technology, 32(4):1724 - 1738, April 2022.
- E. Meunier and P. Bouthemy. Segmenting the motion components of a video: A long-term unsupervised model, IEEE Transactions on Pattern Analysis and Machine Intelligence, 48(1):500-511, January 2026.
- G. Charbel, N. Kindji, E. Fromont, L. M. Rojas-Barahona, and T. Urvoy. Robust detection of synthetic tabular data under schema variability, 40th Annual AAAI Conf. on Artificial Intelligence (AAAI'2026), Singapore, January 2026.
Avantages
Rémunération
Monthly gross salary from 2 788 euros.
The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Contexte et atouts du poste
Funding: Funding for this postdoc position has not yet been secured. The selection of a postdoc candidate is a prerequisite. This is because the funding sources being sought require that the application be submitted by the postdoc fellow. The duration of the postdoc position may range from 12 to 24 months, depending on the funding secured.
Mission confiée
Subject: A video is more than just a succession of images because it incorporates the fundamental temporal dimension of motion. Motion indeed carries intrinsic information in videos. This characteristic is essential to the analysis. Additionally, motion properties become more explicit over time. Motion is fully represented by the velocity field computed between two images at every time instant of the video. In the long term, the consecutive velocity fields can be viewed as a multivariate time series. The first objective of the postdoc is to create parsimonious time series through learning that can adequately represent motion content and are semantically suitable for generic tasks such as detection, recognition, and classification. More specifically, we will explore anomaly detection in videos, a challenge shared by many applications, albeit in various forms. We will focus on the characterization of motion anomalies based on learned time series. Motion anomalies can manifest themselves in three main ways: deviations from normal behavior or context (e.g., a vehicle driving the wrong way on a highway), sudden divergences (e.g., a vehicle leaving the road, panic in a crowd), or non-natural motion (e.g., presumably in AI-generated videos). This last category will lead us to address the detection of AI-generated videos, whose rapid rise and growing ability to mimic reality raise major societal and economic issues. While the detection of AI-generated still images has been the subject of much research and even challenges, there are still relatively few methods designed specifically for videos. Methods developed for detecting AI-generated images are not effective for videos because they fail to recognize an essential video characteristic: its temporal dimension. This postdoc will build on our recent work, particularly on salient trajectory detection, long-term unsupervised motion segmentation, and automatic detection of AI-generated content.
Key words: Motion in image sequences, time series, learning, anomaly characterization, detection of AI-generated videos
References
- L. Maczyta, P. Bouthemy, and O. Le Meur. Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder, IEEE Trans. on Circuits and Systems for Video Technology, 32(4):1724 - 1738, April 2022.
- E. Meunier and P. Bouthemy. Segmenting the motion components of a video: A long-term unsupervised model, IEEE Transactions on Pattern Analysis and Machine Intelligence, 48(1):500-511, January 2026.
- G. Charbel, N. Kindji, E. Fromont, L. M. Rojas-Barahona, and T. Urvoy. Robust detection of synthetic tabular data under schema variability, 40th Annual AAAI Conf. on Artificial Intelligence (AAAI'2026), Singapore, January 2026.
Avantages
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
Rémunération
Monthly gross salary from 2 788 euros.
