Search Results for author: Aljaz Bozic

Found 8 papers, 1 papers with code

Quaffure: Real-Time Quasi-Static Neural Hair Simulation

no code implementations13 Dec 2024 Tuur Stuyck, Gene Wei-Chin Lin, Egor Larionov, Hsiao-yu Chen, Aljaz Bozic, Nikolaos Sarafianos, Doug Roble

Realistic hair motion is crucial for high-quality avatars, but it is often limited by the computational resources available for real-time applications.

3D Mesh Editing using Masked LRMs

no code implementations11 Dec 2024 Will Gao, Dilin Wang, Yuchen Fan, Aljaz Bozic, Tuur Stuyck, Zhengqin Li, Zhao Dong, Rakesh Ranjan, Nikolaos Sarafianos

We formulate shape editing as a conditional reconstruction problem, where the model must reconstruct the input shape with the exception of a specified 3D region, in which the geometry should be generated from the conditional signal.

3D Reconstruction

Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media

no code implementations24 May 2024 Jorge Condor, Sebastien Speierer, Lukas Bode, Aljaz Bozic, Simon Green, Piotr Didyk, Adrian Jarabo

We demonstrate our method as a compact and efficient alternative to other forms of volume modeling for forward and inverse rendering of scattering media.

Inverse Rendering

A Local Appearance Model for Volumetric Capture of Diverse Hairstyle

no code implementations14 Dec 2023 Ziyan Wang, Giljoo Nam, Aljaz Bozic, Chen Cao, Jason Saragih, Michael Zollhoefer, Jessica Hodgins

In this paper, we present a novel method for creating high-fidelity avatars with diverse hairstyles.

Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient

no code implementations15 Jun 2023 Shizhan Zhu, Shunsuke Saito, Aljaz Bozic, Carlos Aliaga, Trevor Darrell, Christop Lassner

Reconstructing and relighting objects and scenes under varying lighting conditions is challenging: existing neural rendering methods often cannot handle the complex interactions between materials and light.

Neural Rendering

DeepDeform: Learning Non-Rigid RGB-D Reconstruction With Semi-Supervised Data

1 code implementation CVPR 2020 Aljaz Bozic, Michael Zollhofer, Christian Theobalt, Matthias Niessner

Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus.

3D Reconstruction RGB-D Reconstruction

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