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arXiv Computer Vision

Patch-level Gaze Distribution Prediction for Gaze Following

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Hugging Face Trending Papers
Aug 11

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.

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arXiv Computer Vision
Oct 5

Gaze Attention: Query-Adaptive Visual Routing for Efficient Multimodal LLMs

Gaze Attention is a new mechanism for multimodal large language models that selectively focuses on relevant visual regions during each generation step, rather than attending to all visual tokens. By grouping tokens into spatial regions and using learnable context tokens to retain global information, it reduces attention computation and visual key‑value entries by up to 90%. Experiments on 13 image and 6 video benchmarks show that Gaze Attention matches or outperforms dense‑attention baselines while using fewer visual resources.

By Junha Song, Byeongho Heo, Geonmo Gu, Jaegul Choo, Dongyoon Han, Sangdoo Yun
llmsmultimodalbenchmarks
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arXiv Computer Vision
Oct 1

GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction

GazeFlow is a new framework for egocentric gaze prediction that models gaze as a joint distribution of temporal positions conditioned on both top‑down task cues and bottom‑up visual saliency. It employs conditional flow matching to iteratively transform Gaussian noise into realistic gaze trajectories, using a velocity field informed by video‑encoded visual features and global task queries. On standard benchmarks, GazeFlow outperforms existing methods on per‑frame metrics and produces trajectories that better reflect human gaze dynamics.

By Sheng Zhao, Weikai Lin, Yuhao Zhu
diffusionbenchmarks
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arXiv Computer Vision
2d ago

DiTGaze: Geometry-Conditioned Diffusion Augmentation for Few-Shot Gaze Estimation

DiTGaze is a calibration‑efficient augmentation system for few‑shot gaze estimation that uses a two‑stream 3D Gaussian Splatting backbone and a geometry‑conditioned latent Diffusion Transformer. It bridges rasterized Gaussian features to latent tokens via a learnable projection and frozen VAE, and incorporates timestep, identity, and expression conditioning through Adaptive Layer Normalization. The method densifies gaze supervision with intermediate yaw‑pitch sampling and applies an orthogonality constraint to reduce interference among gaze, pose, and expression, achieving a 4.1 % reduction in generator‑level gaze error on ETH‑XGaze and outperforming baselines across all calibration budgets up to nine real images.

By Abiram Panchalingam, Indu P. Bodala, Stuart E. Middleton
llmsdiffusion
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arXiv Computer Vision
Oct 23

AVAtt: A Free-Viewing Eye-Tracking Dataset and Benchmark for Visual Attention on Paintings

AVAtt is a free‑viewing eye‑tracking dataset comprising Images{} paintings from sixteen art movements and five centuries, with Scanpaths{} scanpaths and Fixations{} fixations collected from Participants{} participants. The dataset provides individual scanpaths, fixation timing, eye‑screen distance, fixation and saliency maps, temporal saliency videos, and rich art metadata. A benchmark of classic and deep saliency models shows significant room for improvement, and 44 scanpath‑similarity measures—including a new language‑free image‑encoder family—are ranked.

By Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani, Alessandro Bruno
benchmarks
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