XHand: Real-time Expressive Hand Avatar (2407.21002v1)

Abstract: Hand avatars play a pivotal role in a wide array of digital interfaces, enhancing user immersion and facilitating natural interaction within virtual environments. While previous studies have focused on photo-realistic hand rendering, little attention has been paid to reconstruct the hand geometry with fine details, which is essential to rendering quality. In the realms of extended reality and gaming, on-the-fly rendering becomes imperative. To this end, we introduce an expressive hand avatar, named XHand, that is designed to comprehensively generate hand shape, appearance, and deformations in real-time. To obtain fine-grained hand meshes, we make use of three feature embedding modules to predict hand deformation displacements, albedo, and linear blending skinning weights, respectively. To achieve photo-realistic hand rendering on fine-grained meshes, our method employs a mesh-based neural renderer by leveraging mesh topological consistency and latent codes from embedding modules. During training, a part-aware Laplace smoothing strategy is proposed by incorporating the distinct levels of regularization to effectively maintain the necessary details and eliminate the undesired artifacts. The experimental evaluations on InterHand2.6M and DeepHandMesh datasets demonstrate the efficacy of XHand, which is able to recover high-fidelity geometry and texture for hand animations across diverse poses in real-time. To reproduce our results, we will make the full implementation publicly available at https://github.com/agnJason/XHand.

• The paper's main contribution is the XHand framework that integrates feature embedding modules and a mesh-based neural renderer to capture fine hand details in real time.
• It employs part-aware Laplace smoothing and demonstrates state-of-the-art performance with a PSNR of 34.32 dB on InterHand2.6M and a rendering speed of 56 fps.
• The work sets a new benchmark for lifelike hand avatars, offering enhanced visual fidelity and practical applications in VR, gaming, and telepresence.

Overview of XHand: Real-time Expressive Hand Avatar

The paper "XHand: Real-time Expressive Hand Avatar" presents a novel framework designed to achieve both highly detailed hand geometry and photorealistic rendering in real-time. The authors, Gan et al., address crucial challenges in hand avatar modeling by introducing a comprehensive methodology that combines feature embedding modules and a mesh-based neural renderer, leveraging the MANO model for hand pose and shape parameters. The proposal advances the state of the art by emphasizing fine-grained geometry while maintaining real-time rendering capabilities.

Technical Contributions

The paper's key contribution is the development of XHand, an animatable hand model that balances detail fidelity with computational efficiency. This is accomplished through a unique integration of several innovative components:

1. Feature Embedding Modules: The authors propose three distinct feature embedding modules aimed at predicting hand deformation displacements, albedo, and linear blending skinning weights. These modules effectively separate pose-driven features from average hand mesh features, simplifying the task of capturing dynamic hand geometries and textures under various poses.
2. Mesh-based Neural Rendering: Employing a mesh-based neural renderer, XHand bypasses the heavy computational demands of volumetric approaches, offering a streamlined approach that preserves the mesh's topological consistency. This results in significantly enhanced visual fidelity and detail preservation without compromising rendering speed.
3. Part-aware Laplace Smoothing: To mitigate artifacts while extracting intricate mesh details, a part-aware Laplace smoothing strategy is implemented. This approach introduces hierarchical weights that adaptively regulate smoothing based on the geometric complexity and pose-specific variations, aiding in maintaining high detail reproduction.

Experimental Evaluation and Results

The evaluation of XHand is conducted on the InterHand2.6M and DeepHandMesh datasets, showing that XHand delivers superior performance in both rendering and geometry reconstruction. Quantitatively, XHand achieves state-of-the-art results, including a noteworthy PSNR of 34.32 dB on the InterHand2.6M dataset. The model demonstrates its robustness across diverse poses, and outperforms existing methods like LiveHand and HandNeRF in terms of both photorealism and computational efficiency, achieving a rendering speed of 56 frames per second.

The experiments highlight XHand's capacity to produce high-fidelity meshes with enhanced detail, validated against 3D ground truth from DeepHandMesh, where it shows a reduction in average P2S error relative to other contemporary methods.

Implications and Future Work

The implications of XHand are multifaceted, impacting various domains such as virtual reality, gaming, and telepresence, where accurate and expressive hand representations significantly enhance user experience. The proposed framework lays a foundation for future explorations into personalized hand avatar systems that can adapt to varying dynamic conditions, potentially extending beyond current applications to include more holistic human body representations.

Future work could explore the integration of advanced neural rendering techniques, such as those incorporating complex material properties or lighting conditions, to further refine the visual realism of hand models across disparate environments. Additionally, further efforts to optimize the feature embedding modules could provide an avenue for scaling these techniques to broader and more complex applications in virtual environments.

In conclusion, XHand sets a new benchmark in expressive hand avatar modeling, balancing detail, realism, and speed. It exemplifies a significant step forward in achieving lifelike digital hand representations, with promising potential for future advancements and applications in AI-driven interactive systems.