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New Promptable Gaze Estimation Model Integrates Subject Localization

Researchers have introduced Promptable Gaze Target Estimation (PGE), a novel end-to-end approach for analyzing human gaze in images. Unlike previous methods that rely on multi-stage pipelines and explicit inputs, PGE uses natural language or visual prompts to identify subjects and predict gaze targets. The new GazeAnywhere model, built for PGE, integrates subject localization and gaze estimation, achieving state-of-the-art results on benchmarks and demonstrating potential for clinical applications. A dataset of 120,000 prompt-annotated image pairs, Gaze-Co, has also been released to support this task. AI

IMPACT This new approach to gaze estimation could improve human-computer interaction and enable more sophisticated analysis in fields like clinical research.

RANK_REASON The cluster describes a new research paper detailing a novel task, model, and dataset in computer vision.

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New Promptable Gaze Estimation Model Integrates Subject Localization

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Xu Cao, Houze Yang, Vipin Gunda, Zhongyi Zhou, Tianyu Xu, Adarsh Kowdle, Inki Kim, James M. Rehg ·

    Gaze Target Estimation Anywhere with Concepts

    arXiv:2608.11367v1 Announce Type: cross Abstract: Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that require explicit inputs, like head bounding boxes and human pose, …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Gaze Target Estimation Anywhere with Concepts

    Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that require explicit inputs, like head bounding boxes and human pose, in order to identify the subject of gaze analysis.…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Gaze Target Estimation Anywhere with Concepts

    A new promptable paradigm integrates subject localization and gaze estimation into an end-to-end transformer model that uses text or visual prompts to identify subjects and predict gaze targets without multi-stage pipelines.