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cs.CV2024
Amodal Depth Anything: Amodal Depth Estimation in the Wild
Zhenyu Li, Mykola Lavreniuk, Jian Shi +2
Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene. This task addresses the question of whether models can effectively perceive t…
cs.CV2024
DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation
Jian Shi, Qian Wang, Zhenyu Li +3
Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video synthesis using diffusion model…
cs.CV2024
ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learning
Jian Shi, Zhenyu Li, Peter Wonka
We introduce \textit{ImmersePro}, an innovative framework specifically designed to transform single-view videos into stereo videos. This framework utilizes a novel dual-branch arch…