Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video
Mohammed Suhail, Erika Lu, Zhengqi Li, Noah Snavely, Leonid Sigal, Forrester Cole
2023年份
3顶会引用
摘要
Columbia 2 Vector Institute for AI 3 Canada CIFAR AI Chair 4 Google Input RGB Input Masks Input Depth Layer 1 + Background Layer 2 + Background Figure 1. Layer decomposition under strong camera parallax. Given an input video with unconstrained camera motion and approximate object masks and depth (left), our method estimates a layered representation composed of a background layer and object layers containing the subjects of interest and their associated effects (e.g. shadows). Results of combining object layers and background are shown on right.
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引用它的顶会 Paper3
- Generating Humanless Environment Walkthroughs from Egocentric Walking Tour VideosYujin Ham, Junho Kim, Vivek Boominathan, Guha BalakrishnanCVPR 2026
- Generative Omnimatte: Learning to Decompose Video into LayersYao-Chih Lee, Erika Lu, Sarah Rumbley, Michal Geyer 等CVPR 2025
- EasyOmnimatte: Taming Pretrained Inpainting Diffusion Models for End-to-End Video Layered DecompositioYihan Hu, Xuelin Chen, Xiaodong CunCVPR 2026
它引用的顶会 Paper16
- Dynamic View Synthesis from Dynamic Monocular VideoChen Gao, Ayush Saraf, Johannes Kopf, Jia-Bin HuangICCV 2021 · 被引用 522 次
- Consistent video depth estimationXuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen 等SIGGRAPH 2020 · 被引用 321 次
- DeepV2D: Video to Depth with Differentiable Structure from MotionZachary Teed, Jia DengICLR 2020 · 被引用 314 次
- Free-Form Video Inpainting With 3D Gated Convolution and Temporal PatchGANYa-Liang Chang, Zhe Yu Liu, Kuan-Ying Lee, Winston H. HsuICCV 2019 · 被引用 213 次
- Copy-and-Paste Networks for Deep Video InpaintingSungho Lee, Seoung Wug Oh, DaeYeun Won, Seon Joo KimICCV 2019 · 被引用 137 次
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