GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency (2308.11617v2)

Abstract: Hands are dexterous and highly versatile manipulators that are central to how humans interact with objects and their environment. Consequently, modeling realistic hand-object interactions, including the subtle motion of individual fingers, is critical for applications in computer graphics, computer vision, and mixed reality. Prior work on capturing and modeling humans interacting with objects in 3D focuses on the body and object motion, often ignoring hand pose. In contrast, we introduce GRIP, a learning-based method that takes, as input, the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction. As a preliminary step before synthesizing the hand motion, we first use a network, ANet, to denoise the arm motion. Then, we leverage the spatio-temporal relationship between the body and the object to extract two types of novel temporal interaction cues, and use them in a two-stage inference pipeline to generate the hand motion. In the first stage, we introduce a new approach to enforce motion temporal consistency in the latent space (LTC), and generate consistent interaction motions. In the second stage, GRIP generates refined hand poses to avoid hand-object penetrations. Given sequences of noisy body and object motion, GRIP upgrades them to include hand-object interaction. Quantitative experiments and perceptual studies demonstrate that GRIP outperforms baseline methods and generalizes to unseen objects and motions from different motion-capture datasets.

A Method for Synthesizing Realistic Hand Movements in Human-Object Interactions

This paper introduces a sophisticated approach to generating realistic hand-object interaction poses by analyzing three-dimensional (3D) body and object motion. The method, referred to as GRIP, leverages complex spatial and temporal cues derived from the dynamics of the body and objects, allowing for the synthesis of hand poses that integrate seamlessly with the animated body movements.

Methodology Overview

The essence of the paper lies in its learning-based method, capable of synthesizing realistic finger and hand motions not explicitly tracked in the input data. The method overcomes the challenges endemic to capturing fine, continuous hand movements by proposing a two-stage inference system equipped with novel virtual sensors. These sensors, termed the Ambient Sensor and Proximity Sensor, provide rich spatio-temporal interaction cues that are crucial for understanding interaction contexts and generating coherent hand motions.

1. Hand Sensors:
   • Ambient Sensor: Captures broad geometric features and spatial proximity between hands and objects. This sensor ensures the algorithm understands the surrounding space and the relation between objects and hands.
   • Proximity Sensor: Focuses on fine-grained spatial details, crucial for capturing nuances of hand-object interaction, including contact moments and surface penetrations.
2. Two-Stage Hand Inference Pipeline:
   • Stage One (CNet): Implements a latent temporal consistency (LTC) mechanism that smooths motion transitions in the latent space rather than the output space, thus ensuring continuity and avoiding the high-frequency distortions that could arise from direct smoothness enforcement.
   • Stage Two (RNet): Refines hand poses generated in the first stage to eliminate issues like hand-object penetrations, focusing on enhancing interaction realism and accuracy.
3. Denoising Network (ANet): An innovative pre-processing layer that refines noisy body motion data before further inference. Ensures that the hand sensors gather precise interaction information.

Empirical Evaluation

The effectiveness of GRIP is underscored by robust quantitative metrics and perceptual evaluations:

• MPVPE and MPJPE: The method showcases lower mean per-vertex and mean per-joint position errors, indicating precision in synthesized hand placements.
• Contact Consistency (CC): Demonstrates the method’s ability to maintain coherent motion dynamics, essential for realistic virtual interactions.
• Perceptual Studies: Highlight that GRIP-generated motions closely match expert ratings on realism compared to ground-truth data.

Implications and Future Directions

The ramifications of GRIP’s approach are manifold in applications requiring realistic digital human representations—from virtual reality systems and gaming environments to human-computer interaction studies and digital content creation. Furthermore, the methodology can be extrapolated to enhance datasets lacking comprehensive hand motion data, providing rich material for further research and application.

The paper’s findings open avenues for future developments in reducing latency in interactive applications. Investigating anticipatory motion modelling could further enhance real-time applications by decreasing frame latencies. Additionally, expanding this framework to encompass more diverse and complex scenarios, such as human-scene interactions, holds potential for more comprehensive digital human modelling systems.

In conclusion, the described method contributes a significant advancement in synthesizing realistic hand-object interactions, marking a step towards more immersive digital environments and detailed human motion understanding.