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Learning to Recover 3D Scene Shape from a Single Image
Wei Yin, Jianming Zhang, Oliver Wang +4
Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth sh…
Revisiting Adaptive Convolutions for Video Frame Interpolation
Simon Niklaus, Long Mai, Oliver Wang
Video frame interpolation, the synthesis of novel views in time, is an increasingly popular research direction with many new papers further advancing the state of the art. But as e…
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
Zhengqi Li, Simon Niklaus, Noah Snavely +1
We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural S…
Real-time Semantic Segmentation with Fast Attention
Ping Hu, Federico Perazzi, Fabian Caba Heilbron +4
In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial context (large receptive fields) and fine spatial details (high resolution), both of which…
Swapping Autoencoder for Deep Image Manipulation
Taesung Park, Jun-Yan Zhu, Oliver Wang +4
Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing…
Temporally Distributed Networks for Fast Video Semantic Segmentation
Ping Hu, Fabian Caba Heilbron, Oliver Wang +3
We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation. We observe that features extracted from a certain high-level layer of…