Generating Human Grasps for Single-View Scene Point Clouds

Core Concepts

The proposed S2HGrasp framework can generate natural and plausible human grasps for objects based on single-view scene point clouds, by globally perceiving partial object shapes and generating high-quality grasps despite interference from scene points.

Abstract

The paper introduces a novel task of generating human grasps based on single-view scene point clouds, which is more aligned with real-world scenarios compared to previous methods that rely on full 3D object models.
The key highlights are:

The authors propose the S2HGrasp framework, which consists of two main modules:

Global Perception module: Enables the model to globally perceive the partial object shape from single-view point clouds, preventing hand-object penetration.
DiffuGrasp module: Generates high-quality human grasps that are close to the object and avoid collision with scene points, by leveraging the power of conditional diffusion models.

The authors introduce the S2HGD dataset, which contains around 99,000 single-view scene point clouds of 1,668 unique objects, each annotated with a human grasp.

Extensive experiments demonstrate that S2HGrasp outperforms existing methods in generating natural and plausible grasps, while also exhibiting strong generalization capabilities to unseen objects.

The paper highlights the challenges of incomplete object point clouds and interference from scene points, and how the proposed framework effectively addresses these issues.

Stats

The average penetration depth between the generated hand and the object is 0.21 cm on the View-S2HGD dataset and 0.21 cm on the Object-S2HGD dataset.
The average penetration volume is 6.58 cm^3 on the View-S2HGD dataset and 5.58 cm^3 on the Object-S2HGD dataset.
The average grasp displacement is 2.73 ± 3.16 cm on the View-S2HGD dataset and 3.26 ± 3.38 cm on the Object-S2HGD dataset.
The average contact ratio between the hand and the object is 99.41% on the View-S2HGD dataset and 98.67% on the Object-S2HGD dataset.

Quotes

"The generated grasps should be not only natural and plausible, but also able to firmly hold objects in physics-based simulators."
"Due to the incompleteness of object point clouds, the model must possess the capability to globally perceive the object to prevent hands from penetrating into the invisible parts of the object."
"Moreover, the model needs to generate high-quality human grasps despite interference from scene points."

Key Insights Distilled From

Single-View Scene Point Cloud Human Grasp Generation

by Yan-Kang Wan... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15815.pdf

Deeper Inquiries

How can the proposed framework be extended to handle more complex scenes with multiple objects?

The proposed framework can be extended to handle more complex scenes with multiple objects by incorporating techniques for object segmentation and recognition. By enhancing the Global Perception module to not only perceive the global geometric features of individual objects but also to segment and recognize multiple objects within the scene, the model can generate human grasps for each object separately. This extension would involve training the model to differentiate between different objects in the scene and generate appropriate grasps for each object based on their individual characteristics. Additionally, the DiffuGrasp module can be adapted to handle multiple objects by incorporating mechanisms to prioritize grasping one object over another based on certain criteria, such as proximity or importance.

What are the potential limitations of the diffusion-based approach used in the DiffuGrasp module, and how could it be further improved?

One potential limitation of the diffusion-based approach used in the DiffuGrasp module is the challenge of effectively denoising the hand parameters while conditioning on the scene features. Diffusion models can sometimes struggle with capturing complex dependencies and may require extensive training data to learn the denoising process effectively. To improve this, techniques such as incorporating hierarchical diffusion models or utilizing more advanced denoising strategies like adversarial training could be explored. Additionally, enhancing the conditioning mechanism to better capture the relationship between scene features and hand parameters could improve the overall performance of the diffusion-based approach in generating human grasps.

Given the focus on single-view point clouds, how could the framework be adapted to leverage additional modalities, such as RGB images, to enhance the grasp generation performance?

To leverage additional modalities such as RGB images and enhance grasp generation performance in the framework focused on single-view point clouds, a multi-modal approach can be adopted. By incorporating RGB images alongside point clouds, the model can benefit from the complementary information provided by visual data. This integration can be achieved by using techniques like multi-modal fusion networks, where features extracted from RGB images and point clouds are combined to provide a more comprehensive representation of the scene. Furthermore, techniques like cross-modal learning can be employed to enable the model to learn correlations between RGB images and point clouds, enhancing the grasp generation performance by leveraging the strengths of both modalities.