Search Results for author: George Drettakis

Found 11 papers, 5 papers with code

3D Gaussian Splatting for Real-Time Radiance Field Rendering

1 code implementation8 Aug 2023 Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis

Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos.

Camera Calibration Novel View Synthesis

Neural Point Catacaustics for Novel-View Synthesis of Reflections

no code implementations3 Jan 2023 Georgios Kopanas, Thomas Leimkühler, Gilles Rainer, Clément Jambon, George Drettakis

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms.

Neural Rendering Novel View Synthesis

Deep scene-scale material estimation from multi-view indoor captures

no code implementations15 Nov 2022 Siddhant Prakash, Gilles Rainer, Adrien Bousseau, George Drettakis

The movie and video game industries have adopted photogrammetry as a way to create digital 3D assets from multiple photographs of a real-world scene.

FreeStyleGAN: Free-view Editable Portrait Rendering with the Camera Manifold

no code implementations20 Sep 2021 Thomas Leimkühler, George Drettakis

In our solution, we use a few images of a face to perform 3D reconstruction, and we introduce the notion of the GAN camera manifold, the key element allowing us to precisely define the range of images that the GAN can reproduce in a stable manner.

3D Reconstruction Mixed Reality +1

Free-viewpoint Indoor Neural Relighting from Multi-view Stereo

no code implementations24 Jun 2021 Julien Philip, Sébastien Morgenthaler, Michaël Gharbi, George Drettakis

We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation.

Guided Fine-Tuning for Large-Scale Material Transfer

1 code implementation6 Jul 2020 Valentin Deschaintre, George Drettakis, Adrien Bousseau

Our solution is extremely simple: we fine-tune a deep appearance-capture network on the provided exemplars, such that it learns to extract similar SVBRDF values from the target image.

Flexible SVBRDF Capture with a Multi-Image Deep Network

1 code implementation27 Jun 2019 Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, Adrien Bousseau

Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph.

Graphics I.3

Deep Blending for Free-Viewpoint Image-Based-Rendering

1 code implementation SIGGRAPH Asia 2018 2018 Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, Gabriel Brostow

We present a new deep learning approach to blending for IBR, in which we use held-out real image data to learn blending weights to combine input photo contributions.

Novel View Synthesis

Single-Image SVBRDF Capture with a Rendering-Aware Deep Network

1 code implementation23 Oct 2018 Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, Adrien Bousseau

Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures.

Graphics I.3

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