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2 offres trouvées dans Rennes

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InterDigital
03 janv., 2026
STAGE, Stage
InternFull Body 4D Synthetic Datasets H/F
InterDigital 48.1336951,-1.6190308
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...
Qualifications Master - computer graphics, 3D modeling, rendering, Maya/Houdini/Blender
Durée (Mois):
6
InterDigital
03 janv., 2026
STAGE, Stage
InternDistributed Inference For Multi-Modal Large Language Models Mllms With Agentic ai Orchestration H/F
InterDigital 48.1336951,-1.6190308
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...
Qualifications Education: Master's student in Computer Science, Artificial Intelligence, Data Science, or related field. Skills: Background in AI/ML, particularly large language models. Knowledge of multi-modal systems (text, vision, speech). Proficiency in Python programming and ML frameworks (PyTorch). Ability to conduct research and prototype efficiently. Nice to have: familiarity with distributed systems, networking, bandwidth concepts, ONNX framework
Durée (Mois):
6
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