111 citations · 146 across the 15 of their papers we have counts for
8 papers
Monocular Dynamic View Synthesis: A Reality Check
Hang Gao, Ruilong Li, Shubham Tulsiani +2
We study the recent progress on dynamic view synthesis (DVS) from monocular video. Though existing approaches have demonstrated impressive results, we show a discrepancy between th…
Studying Bias in GANs through the Lens of Race
Vongani H. Maluleke, Neerja Thakkar, Tim Brooks +5
In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets. By examining and controlling…
The One Where They Reconstructed 3D Humans and Environments in TV Shows
Georgios Pavlakos, Ethan Weber, Matthew Tancik +1
TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications. However, the majority of…
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
Zhengqi Li, Qianqian Wang, Noah Snavely +1
We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single…
Plenoxels: Radiance Fields without Neural Networks
Alex Yu, Sara Fridovich-Keil, Matthew Tancik +3
We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation…
Tracking People by Predicting 3D Appearance, Location & Pose
Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa +1
In this paper, we present an approach for tracking people in monocular videos, by predicting their future 3D representations. To achieve this, we first lift people to 3D from a sin…