Learning to synthesize interactive 3D models

This project aims to automate the generation of interactive 3D models using deep learning to enhance virtual environments and applications in animation, robotics, and digital entertainment.

Subsidie
€ 2.000.000
2024

Projectdetails

Introduction

Visualizing our surroundings and imagination has been an integral part of human history. In today's era, we have the privilege to immerse in 3D digital environments and interact with virtual objects and characters.

Challenges in 3D Model Creation

However, creating digital representations of environments (i.e., 3D models) often requires an excessive amount of manual effort and time, even for trained 3D artists. Over the recent years, there have been remarkable advances in deep learning methods that attempt to reconstruct 3D models from real-world data captured in images or scans.

Limitations of Current Methods

Despite these advances, we are still far from automatically producing 3D models usable in interactive 3D environments and simulations. The resulting reconstructed 3D models lack controllers and metadata related to their articulation structure, possible motions, and interaction with other objects or agents.

Importance of Automation

Automating the synthesis of interactive 3D models is crucial for several applications, such as:

  1. Virtual and mixed reality environments where objects and characters are not static, but instead move and interact with each other.
  2. Automating animation pipelines.
  3. Training robots for object interaction in simulated environments.
  4. 3D printing of functional objects.
  5. Digital entertainment.

Project Objective

In this project, we will answer the question: "How can we automate the generation of interactive 3D models of objects and characters?"

Project Thrusts

Our project will include the following thrusts:

  1. We will design deep architectures that automatically infer motion controllers and interaction-related metadata for input 3D models, effectively making them interactive.
  2. We will develop learning methods that replace dynamic real-world objects and characters captured in scans and video with high-quality, interactive, and animated 3D models as digital representatives.
  3. We will develop generative models that synthesize interactive 3D objects and characters automatically, and further help reconstructing them from scans and video more faithfully.

Financiële details & Tijdlijn

Financiële details

Subsidiebedrag€ 2.000.000
Totale projectbegroting€ 2.000.000

Tijdlijn

Startdatum1-10-2024
Einddatum30-9-2029
Subsidiejaar2024

Partners & Locaties

Projectpartners

  • POLYTECHNEIO KRITISpenvoerder

Land(en)

Greece

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