Gaze-guided Hand-Object Interaction Synthesis: Benchmark and Method

Core Concepts

Integrating gaze guidance with hand-object interactions for accurate motion prediction.

Abstract

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.

Stats

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.

Quotes

"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."

Key Insights Distilled From

Gaze-guided Hand-Object Interaction Synthesis

by Jie Tian,Lin... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16169.pdf

Deeper Inquiries

How can integrating gaze guidance improve human-robot interaction scenarios

Integrating gaze guidance can significantly enhance human-robot interaction scenarios by providing robots with a deeper understanding of human intentions and actions. By incorporating gaze information, robots can interpret subtle cues from humans' eye movements, allowing for more intuitive and natural interactions. This integration enables robots to anticipate human needs and respond accordingly, leading to smoother communication and collaboration between humans and machines. Additionally, gaze guidance can help eliminate ambiguities in movement instructions, making interactions safer and more efficient.

What are the implications of neglecting fine-grained reconstruction in hand-object motion synthesis

Neglecting fine-grained reconstruction in hand-object motion synthesis can lead to unrealistic or inaccurate results in the generated motions. Fine-grained reconstruction is crucial for capturing the intricate details of hand-object interactions, such as grasping poses, object manipulation techniques, and contact points. Without this level of detail, the synthesized motions may lack realism or fail to accurately represent complex interactions between hands and objects. Neglecting fine-grained reconstruction limits the fidelity of the synthesized motions and hinders their applicability in real-world scenarios where precise movements are essential.

How can the Spherical Gaussian constraint enhance goal pose alignment in motion synthesis

The Spherical Gaussian constraint plays a vital role in enhancing goal pose alignment in motion synthesis by providing precise guidance during denoising steps. By introducing this constraint, the generation process is guided towards aligning with specific goal poses while maintaining naturalness and smoothness in motion transitions. The Spherical Gaussian constraint helps prevent unrealistic scenarios where hand motions deviate from intended goals or fail to reach target positions accurately. This enhancement ensures that the generated hand-object interaction motions remain within realistic boundaries while achieving high levels of accuracy in aligning with desired goal poses.