2 citations · 2 across the 11 of their papers we have counts for
13 papers
DivRL: Disentangled Self-Similarity Rewards for Diverse Subject-Driven Generation
Qian Wang, Zhenyu Li, Abdelrahman Eldesokey +1
Subject-driven image generation faces an "Identity-Diversity Paradox", where strong identity preservation often leads to rigid and low-diversity outputs. We propose a post-training…
Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning
Xuyang Wang, Zhenyu Li, Jian Ding +4
Reconstructing articulated objects from sparse images requires recovering complete geometry, movable parts, and motion parameters. Recent methods typically separate geometry recons…
EgoPoseFormer v2: Accurate Egocentric Human Motion Estimation for AR/VR
Zhenyu Li, Sai Kumar Dwivedi, Filip Maric +11
Egocentric human motion estimation is essential for AR/VR experiences, yet remains challenging due to limited body coverage from the egocentric viewpoint, frequent occlusions, and…
Any Resolution Any Geometry: From Multi-View To Multi-Patch
Wenqing Cui, Zhenyu Li, Mykola Lavreniuk +4
Joint estimation of surface normals and depth is essential for holistic 3D scene understanding, yet high-resolution prediction remains difficult due to the trade-off between preser…
Geometry without Position? When Positional Embeddings Help and Hurt Spatial Reasoning
Jian Shi, Michael Birsak, Wenqing Cui +2
This paper revisits the role of positional embeddings (PEs) within vision transformers (ViTs) from a geometric perspective. We show that PEs are not mere token indices but effectiv…
Depth Anything 3: Recovering the Visual Space from Any Views
Haotong Lin, Sili Chen, Junhao Liew +5
We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of…
PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation
Zhenyu Li, Wenqing Cui, Shariq Farooq Bhat +1
While current high-resolution depth estimation methods achieve strong results, they often suffer from computational inefficiencies due to reliance on heavyweight models and multipl…
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…
PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation
Zhenyu Li, Shariq Farooq Bhat, Peter Wonka
Single image depth estimation is a foundational task in computer vision and generative modeling. However, prevailing depth estimation models grapple with accommodating the increasi…