Course Content
Introduction to the course (05:10)
Intro + PAACADEMY Updates
Lecture (01:17:28)
Frontiers in 3D AI
This session explores advances in 3D generative AI, highlighting new diffusion models for creating consistent 3D objects and scenes from 2D images or videos.
Luma AI (24:44)
Introduction to Luma AI
This session demonstrates how Luma Dream Machine uses AI to create animations and 3D reconstructions from images and text, unlocking new creative possibilities.
AI Reconstruction with Blender (36:46)
Exploring AI-Powered 3D Visualization
This session covers 3D reconstruction and visualization using tools like Luma AI and Blender, focusing on point clouds, meshes, and rendering techniques.
3D Model Generation Process (48:55)
Creating 3D Models from Videos
This session explains how to generate 3D models and point clouds—covering setup, dependency installation and managing output files.
Text to 3D scenes (25:43)
generating 3D scenes from text using a text to 3D scene generation model
This session covers generating and visualizing 3D scenes from text, including addressing issues with multi-view stitching.
3D Scene Creation Overview (43:40)
Generating 3D Scenes and Texturing
This session covers generating 3D scenes with a Colab notebook, importing and texturing meshes in Blender, and starting with Unreal Engine.
Unreal Engine Particle System Setup (37:12)
Creating a Basic Particle System
This session explains creating a basic particle system in Unreal Engine, including importing assets, configuring Niagara settings, and applying textures.
Unreal Engine System Setup (32:59)
Creating and Adjusting Particle Systems
This session explains how to set up a particle system in Unreal Engine, including particle sampling, force adjustments, and visualization techniques.
Mesh Particles Setup (34:30)
Setting Up Mesh Particles and Cameras
This session covers setting up mesh particles and animating cameras in Niagara and Blender to visualize and capture particle effects.
3D Scene Generation Overview (37:19)
Generating 3D Scenes from Image combination
This session covers generating 3D scenes by combining images from different camera angles to create the final scene.
Instructors
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Biography
Duration: | 8 Hours |
Instructor: | Daniel Escobar |
Difficulty: | Beginner |
Language: | English |
Certificate: |
Yes,
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Categories: | Artificial Intelligence and +1 |
Digital Members: | 89.25 EUR |
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Recent image and video generation advancements have revolutionized content creation, offering unprecedented possibilities. While we rapidly approach seamless interaction with scenes and videos through text inputs, 3D scene generation remains a fertile ground for exploration. Over the past two years, significant progress has been made in 3D and 4D object generation, mainly through multiview diffusion models. More recently, the application of consistent video generation as a proxy for 3D spatial representation has emerged, paving the way for comprehensive 3D scene generation.These developments present a unique opportunity to harness deep learning algorithms, allowing us to envision and create immersive 3D scenes, thereby unlocking a new dimension of creative expression.This workshop will explore the cutting-edge techniques of multiview diffusion models conditioned on camera paths for 3D scene generation. We will dive into the latest methods for representing 3D scenes and geometry and investigate how current research in 3D generative AI leverages pre-trained image and video models for a 3D generation. Participants will learn how to use text and image inputs to generate scenes, which will then be imported into Unreal Engine for post-production and to create a concept reel.
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