Learning Pixel-Perfect 3D Vision and Generative Modeling

Project Information

PIPE

Grant agreement ID: 866435

DOI

10.3030/866435

Project closed

EC signature date 7 February 2020

Start date 1 September 2020

End date 31 August 2025

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 858 013,00

EU contribution

€ 1 858 013,00

Coordinated by

AALTO KORKEAKOULUSAATIO SR
Finland

Periodic Reporting for period 4 - PIPE (Learning Pixel-Perfect 3D Vision and Generative Modeling)

Reporting period: 2025-03-01 to 2025-08-31

Summary of the context and overall objectives of the project

PIPE seeks to advance computers' capability to reason about the physical world by looking at nothing but pictures. Moreover, PIPE seeks to enable this reasoning to stand on solid, physically meaningful quantities. Concretely, PIPE studies both the problem of understanding what shapes, materials, lights, objects, etc., appear in pictures of real-world scenes, as well as the problem of generative modeling, i.e. learning "a recipe" for the world.

Solutions to these fundamental problems in computer vision, computer graphics, and machine learning are relevant for a large number of applications from computer graphics, augmented and virtual reality to robotics; clearly, truly understanding the messy complexity of the real world must be necessary, e.g. for reliable, safe autonomous machines to emerge.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

PIPE seeks to advance the state of the art on three axes: (1) understanding the shapes in the world; (2) understanding the visual complexity of the surface materials in the world; and (3) learning generative computational recipes for things in the world. The most significant achievement since the beginning of the project is a result in the final direction that combines intuitive, controllable computational components with modern machine learning techniques. This enables photorealistic generative modeling in terms of physically meaningful concepts and quantities while requiring no human supervision.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

PIPE is expected to provide breakthrough results in how well computer vision and generative algorithms can understand our three-dimensional world.

Overview of the approach in "A hybrid generator architecture for controllable face synthesis"