206 citations · 535 across the 89 of their papers we have counts for
7 papers · 1 filter
ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field
Yuan Li, Zhi-Hao Lin, David Forsyth +2
Physical simulations produce excellent predictions of weather effects. Neural radiance fields produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse ph…
AMICO: Amodal Instance Composition
Peiye Zhuang, Jia-bin Huang, Ayush Saraf +3
Image composition aims to blend multiple objects to form a harmonized image. Existing approaches often assume precisely segmented and intact objects. Such assumptions, however, are…
Temporally Consistent Semantic Video Editing
Yiran Xu, Badour AlBahar, Jia-Bin Huang
Generative adversarial networks (GANs) have demonstrated impressive image generation quality and semantic editing capability of real images, e.g., changing object classes, modifyin…
Learning Dynamic View Synthesis With Few RGBD Cameras
Shengze Wang, YoungJoong Kwon, Yuan Shen +4
There have been significant advancements in dynamic novel view synthesis in recent years. However, current deep learning models often require (1) prior models (e.g., SMPL human mod…
Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer
Songwei Ge, Thomas Hayes, Harry Yang +5
Videos are created to express emotion, exchange information, and share experiences. Video synthesis has intrigued researchers for a long time. Despite the rapid progress driven by…
Neural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature
Zhixiang Wang, Xiang Ji, Jia-Bin Huang +3
Most computer vision systems assume distortion-free images as inputs. The widely used rolling-shutter (RS) image sensors, however, suffer from geometric distortion when the camera…
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
Yuliang Zou, Zizhao Zhang, Chun-Liang Li +3
We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conduc…