Learning World-Space Representations of Human Attention from Uncalibrated Views

Human gaze target estimation (GTE) remains challenging due to the limited camera field of view. While multi-view setups can mitigate this issue, prior attempts either operate in fixed camera setups or heavily rely on explicit bounding boxes. In this work, we present GazeWorld, a unified framework dedicated to learning world-space representations of human attention directly from uncalibrated multi-view images in the wild. By constructing a unified 3D-aware representation that jointly encodes scene geometry and semantics, GazeWorld leverages pre-trained geometry priors, establishes prompt-based subject association, and predicts per-view gaze targets using features expressed in a shared, estimated 3D coordinate frame. This world-space modeling allows our approach to process a variable number of uncalibrated views, helping resolve ambiguities across views. To support this paradigm, we curated a large-scale dataset from Ego-Exo4D containing over 260K verified multi-view GTE groups, alongside a self-collected in-the-wild benchmark featuring challenging head-invisible scenarios. Experiments show strong GTE performance on multiple benchmarks under both single-view and multi-view scenarios, and demonstrate consistent gains with increasing input views. Code, models, and benchmarks will be released upon acceptance.