2 citations · 2 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation
Zhenyu Li, Shariq Farooq Bhat, Peter Wonka
This paper introduces PatchRefiner, an advanced framework for metric single image depth estimation aimed at high-resolution real-domain inputs. While depth estimation is crucial fo…
AvatarMMC: 3D Head Avatar Generation and Editing with Multi-Modal Conditioning
Wamiq Reyaz Para, Abdelrahman Eldesokey, Zhenyu Li +3
We introduce an approach for 3D head avatar generation and editing with multi-modal conditioning based on a 3D Generative Adversarial Network (GAN) and a Latent Diffusion Model (LD…