Differences

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--- curriculum [2025/11/06 13:26] – [Period 2] respai-vic
+++ curriculum [2026/04/10 14:34] (current) – [Period 1] respai-vic
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   * Statistics in Action (Recommended option, APM_52066_EP, Zacharie Naulet, EP & INRAE)
   * Advanced Deep Learning (CSC_52087_EP, Michalis Vazirgiannis, Vicky Kalogeiton, Johannes Lutzeyer, EP)
-  * Graph Representation Learning (CSC_52072_EP, Johannes Lutzeyer and Michalis Vazigiannis, EP)
+  * Graph Machine and Deep Learning for Generative AI (CSC_52072_EP, Johannes Lutzeyer and Michalis Vazigiannis, EP)
   * Emerging Topics in Machine Learning (APM_52188_EP, Rémi Flamary, EP)
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 **CSC_53433_EP - Creative & Generative models in Computer Graphics (24h, 2 ECTS),  Marie-Paule Cani (EP), Julien Pettré (Inria)** (contact: marie-paule.cani@polytechnique.edu)
-> This course presents the techniques for content creation in 3D Computer Graphics, from 3D shapes to animated landscapes and character motion, with a focus on their link with AI. We first introduce user-centered Creative AI (also called Expressive modeling methods), i.e. smart 3D models - either based on knowledge or trained from examples, designed to help users  create/generate the 3D shapes their have in mind, and their organisation into complex, static or animated scenes. Second, we study the use of AI in Character Animation, from early motion planning and control methods to deep reinforcement learning solutions for generating the gaits of physically-based characters. These methods result into 3D agents able to navigate alone or within crowds, and to interact with their environment. The lab sessions are held on Unity, based on C#.
+> This course presents the techniques for content creation in 3D Computer Graphics, from 3D shapes to animated landscapes and character motion, with a focus on their link with AI. We first introduce user-centered Creative AI (also called Expressive modeling methods), i.e. smart 3D models - either based on knowledge or trained from examples, designed to help users  create/generate the 3D shapes their have in mind, and their organisation into complex, static or animated scenes. Second, we study the use of AI in Character Animation, from early motion planning and control methods to deep reinforcement learning solutions for generating the gaits of physically-based characters. These methods result into 3D agents able to navigate alone or within crowds, and to interact with their environment. The lab sessions are held on Unity, based on C#, taking the form of a project where students are asked to combine expressive and learning-based methods to create, populate and animate a 3D virtual world of their choice.