Kernekoncepter
Integrating gaze guidance with hand-object interactions for accurate motion prediction.
Resumé
The content introduces the GazeHOI dataset, a novel task for synthesizing gaze-guided hand-object interactions. It presents a hierarchical framework centered on the GHO-Diffusion model, emphasizing the importance of integrating gaze conditions with hand and object movements. The paper discusses data collection, annotation, statistics, and experiments to validate the proposed methodology.
Directory:
Introduction
Gaze's role in revealing human attention and intention.
Dataset Creation
Collection of 479 sequences with 3D modeling of gaze, hand, and object interactions.
Data Annotation
Extraction of 3D hand poses and object 6D poses.
Data Statistics
Details about the dataset comprising various tasks involving hand-object interactions.
GHO-Diffusion Model
Stacked gaze-guided hand-object motion generation using diffusion models.
Experiments and Baselines
Evaluation metrics comparing proposed method with baselines.
Ablation Study
Impact of different encoding methods and guidance strategies on results.
Statistik
Our dataset comprises 479 sequences with an average duration of 19.1 seconds.
The GHO-Diffusion model separates gaze conditions into spatial-temporal features and goal pose conditions.
Contact consistency optimization is used to refine hand-object interaction motions.
Citater
"Gaze plays a crucial role in revealing human attention and intention."
"Understanding the distribution of gaze is fundamental to understanding visually driven behavior."
"Gaze serves as a crucial behavioral signal encapsulating intentional cues."