R&D Internship: Few-shot learning for motion inbetweening techniques in character animation
Inria
Rennes
R&D Internship: Few-shot learning for motion inbetweening techniques in character animation
Le descriptif de l’offre ci-dessous est en Anglais
Type de contrat : Convention de stage
Niveau de diplôme exigé : Bac + 4 ou équivalent
Stagiaire de la recherche
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.
This internship is part of a joint collaboration between the company Mercenaries Engineering which develops the Rumba animation tool, and the IRISA / Inria Center at Rennes University.
Motion in-betweening in animation is the task that consists in creating intermediate animations between two given keyframes while preserving the naturalness of the motion. This is a classical task in the creation pipeline of artists, and recent contributions in the field have been proposing ways to automatically generate these in-between motions using deep-learning techniques. 1] rely on conditioned Recurrent Transition Networks, extended with time-to-arrival information, to create in-between motions even with sparse keyframes for animated characters. Most approaches require large datasets to ensure natural motions, which may be sparse or hard to access when considering more cartoon-style animations.
The objective of this internship is to design a motion in-betweening technique for character animation inside the Rumba animation tool. Given a small dataset of existing animations (e.g. representing sample motions of a character available in a studio), our purpose is to design a tool capable of exploiting this sparse data to generate automatically in-between motions. Additional specification constraints (speed / feet contacts) may also be considered to improve the editing capacity of the technique.
The work will start by reproducing the results of classical motion in-betweening techniques such as [1][5][6]. We will then explore how existing datasets may be augmented with dedicated sparse data provided directly by users of the system, to partially retrain the model and generate results which are visually similar to the sparse data. Dedicated noise generators may also need to be designed to ensure that the keyframe constraints can be reached even with low amounts of data by using the delta-interpolator [5]. Results will be integrated in the Rumba animation tool with the support of the Rumba R&D team.
extend motion in-betweening techniques to handle sparse data.
~Python scripting, C/C++ (for Rumba integration)
Partial reimbursement of public transport costs
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