July 31, 2019
Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion. Mesh-based reconstruction and tracking often fail in these cases, and other approaches (e.g., light field video) typically rely on constrained viewing conditions, which limit interactivity. We circumvent these difficulties by presenting a learning-based approach to representing dynamic objects inspired by the integral projection model used in tomographic imaging. The approach is supervised directly from 2D images in a multi-view capture setting and does not require explicit reconstruction or tracking of the object. Our method has two primary components: an encoder-decoder network that transforms input images into a 3D volume representation, and a differentiable ray-marching operation that enables end-to-end training. By virtue of its 3D representation, our construction extrapolates better to novel viewpoints compared to screen-space rendering techniques. The encoder-decoder architecture learns a latent representation of a dynamic scene that enables us to produce novel content sequences not seen during training. To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.
Publisher
Research Topics
November 10, 2022
Unnat Jain, Abhinav Gupta, Himangi Mittal, Pedro Morgado
November 10, 2022
November 06, 2022
Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan
November 06, 2022
October 25, 2022
Mustafa Mukadam, Austin Wang, Brandon Amos, Daniel DeTone, Jing Dong, Joe Ortiz, Luis Pineda, Maurizio Monge, Ricky Chen, Shobha Venkataraman, Stuart Anderson, Taosha Fan, Paloma Sodhi
October 25, 2022
October 22, 2022
Naila Murray, Lei Wang, Piotr Koniusz, Shan Zhang
October 22, 2022
April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
December 11, 2019
Eliya Nachmani, Lior Wolf
December 11, 2019
April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
November 01, 2018
Yedid Hoshen, Lior Wolf
November 01, 2018
Foundational models
Our approach
Latest news
Foundational models