no code implementations • 18 Mar 2024 • Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, Varun Jampani
In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS.
no code implementations • 8 Feb 2024 • Shimon Vainer, Mark Boss, Mathias Parger, Konstantin Kutsy, Dante De Nigris, Ciara Rowles, Nicolas Perony, Simon Donné
Current 3D content generation approaches build on diffusion models that output RGB images.
no code implementations • 18 Jan 2024 • Andreas Engelhardt, Amit Raj, Mark Boss, Yunzhi Zhang, Abhishek Kar, Yuanzhen Li, Deqing Sun, Ricardo Martin Brualla, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background.
1 code implementation • 31 May 2022 • Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani
Our method works on in-the-wild online image collections of an object and produces relightable 3D assets for several use-cases such as AR/VR.
1 code implementation • NeurIPS 2021 • Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan T. Barron, Hendrik P. A. Lensch
Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics.
1 code implementation • ICCV 2021 • Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, Hendrik P. A. Lensch
This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination.
Ranked #5 on Image Relighting on Stanford-ORB
1 code implementation • CVPR 2020 • Mark Boss, Varun Jampani, Kihwan Kim, Hendrik P. A. Lensch, Jan Kautz
Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
no code implementations • 11 Oct 2019 • Mark Boss, Hendrik P. A. Lensch
Creating plausible surfaces is an essential component in achieving a high degree of realism in rendering.