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20202026
most citedBiHand: Recovering Hand Mesh with Multi-stage Bisected Hourglass Networks

26 citations · 67 across the 27 of their papers we have counts for

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17 papers · 1 filter

cs.CV2026

EgoAfford: Task-Oriented Affordance Grounding via Egocentric Referring Segmentation

Xinyuan Guan, Feifan Chen, Xinyu Zhan +3

Part-level affordance grounding has advanced the localization of functional object regions associated with elemental actions. Extending this capability to complex tasks calls for c…

cs.CV2026

VFM-Recon: Unlocking Cross-Domain Scene-Level Neural Reconstruction with Scale-Aligned Foundation Priors

Yuhang Ming, Tingkang Xi, Xingrui Yang +4

Scene-level neural volumetric reconstruction from monocular videos remains challenging, especially under severe domain shifts. Although recent advances in vision foundation models…

cs.CV2026

LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior

Xinkai Wang, Chenyi Wang, Yifu Xu +7

We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models reg…

cs.CV2025

OmniCam: Unified Multimodal Video Generation via Camera Control

Xiaoda Yang, Jiayang Xu, Kaixuan Luan +9

Camera control, which achieves diverse visual effects by changing camera position and pose, has attracted widespread attention. However, existing methods face challenges such as co…

cs.CV2024

Multi-view Hand Reconstruction with a Point-Embedded Transformer

Lixin Yang, Licheng Zhong, Pengxiang Zhu +4

This work introduces a novel and generalizable multi-view Hand Mesh Reconstruction (HMR) model, named POEM, designed for practical use in real-world hand motion capture scenarios.…

cs.CV2024

SemGrasp: Semantic Grasp Generation via Language Aligned Discretization

Kailin Li, Jingbo Wang, Lixin Yang +2

Generating natural human grasps necessitates consideration of not just object geometry but also semantic information. Solely depending on object shape for grasp generation confines…