4 offres stage trouvées dans Cesson-Sévigné

Summary Research-oriented internship on multi-user Quality of Experience (QoE) for XR application, the intern will survey the state of the art, identify and formalize QoE metrics specific to multi-user XR, and propose/enhance a composite QoE metric with corresponding instrumentation. The intern will design and run controlled experiments to collect metrics across concurrent users, analyze correlations with perceived experience, and iterate on the model. The work may culminate in planning and conducting user studies in the Rennes lab. Expected outcomes: a literature review, a measurement framework/prototype, practical recommendations for QoE-aware adaptation in multi-user XR, participation in a scientific paper, and potential disclosures. Responsibilities State of the art in QoE in XR, and Multi-User scenarios Propose or refine a composite QoE metric and map each factor to measurable KPIs and thresholds Design and implement instrumentation to capture interaction signals across...

Summary Short paragraph about the role, accountabilities, and any business until or department specific information The goal of the project is to explore video coding improvement using an additional reference picture generated with a deep neural network (Deep Learning) also known as Deep Reference Frame (DRF). Since a large part of network traffic consists of video content, image compression has become a key challenge to reduce the volume of transmitted data. The project context is VVC video compression. VVC is the latest video compression standard developed by the ISO/IEC and ITU-T standardization bodies. Recently, it has been proposed to use deep learning based image generation (DRF: Deep Reference Frame) to predict certain frames and reduce the bitrate of transmitted video content. Indeed, temporal frame interpolation using Deep Learning techniques is advancing rapidly. Responsibilities List primary responsibilities State-of-art of DRF and analysis of a particular...

Summary The InterDigital Immersive Video Lab is focused on advancing avatar-based communication. In this context, accurate and detailed human 3D data is essential for reliably estimating features such as facial expressions, body posture, skin texture, lighting conditions, background segmentation, hair segmentation, and more. InterDigital has developed a proprietary framework for generating synthetic 3D facial image datasets. The next step is to enhance this framework to support the creation of 4D video sequences featuring full-body characters. This internship will contribute to the development and refinement of this extended capability. Responsibilities The goal of the internship will be to: Study state of the art on face & body analysis models and synthetic data generation Update the framework to full body with body animation Upgrade the framework to output video sequences Create and/or set up avatar elements (clothes, accessories, jewels etc.) Generate video...

Summary Multi-Modal Large Language Models (MLLMs) are increasingly capable of processing and reasoning over diverse input modalities such as text, images, audio, and video. However, running such models in real-time or resource-constrained environments poses significant challenges in terms of bandwidth and compute requirements. Distributing the processing of models between client and server (a "distributed computing" approach) is a promising solution. While traditional distributed inferencing has been applied successfully to DNN models, extending this paradigm to MLLMs is a novel and impactful use case. This internship aims to demonstrate the feasibility of distributed MLLMs inference approach, where MLLM components are distributed across two endpoints, coordinated through an agentic orchestration. Responsibilities The internship will be involved in the following tasks: Survey the recent advances in MLLMs, Select representative models, and a representative agentic...