most citedNo time to train! Training-Free Reference-Based Instance Segmentation

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cs.CV2026

IMAGIN-4D: Image-Guided Controllable Interaction Generation

Sai Kumar Dwivedi, Federica Bogo, Buğra Tekin +6

Generating human-object interactions (HOI) is central to character animation, robotics, AR/VR, and embodied AI. Recent HOI generation methods synthesize motion from text, object ge…

cs.CV2026

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…

cs.CV20261 cited

No time to train! Training-Free Reference-Based Instance Segmentation

Miguel Espinosa, Chenhongyi Yang, Linus Ericsson +2

The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviate…

cs.CV2024

There is no SAMantics! Exploring SAM as a Backbone for Visual Understanding Tasks

Miguel Espinosa, Chenhongyi Yang, Linus Ericsson +2

The Segment Anything Model (SAM) was originally designed for label-agnostic mask generation. Does this model also possess inherent semantic understanding, of value to broader visua…

cs.CV2024

EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation

Chenhongyi Yang, Anastasia Tkach, Shreyas Hampali +3

We present EgoPoseFormer, a simple yet effective transformer-based model for stereo egocentric human pose estimation. The main challenge in egocentric pose estimation is overcoming…

cs.CV2024

PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

Chenhongyi Yang, Zehui Chen, Miguel Espinosa +4

We present PlainMamba: a simple non-hierarchical state space model (SSM) designed for general visual recognition. The recent Mamba model has shown how SSMs can be highly competitiv…