XHand: Real-time Expressive Hand Avatar

🌈 Abstract

The article discusses the development of an expressive hand avatar, named XHand, that can generate high-fidelity hand shape, appearance, and deformations in real-time. The key points are:

• Previous studies have focused on photo-realistic hand rendering, but paid little attention to reconstructing the hand geometry with fine details, which is essential for rendering quality.
• XHand uses three feature embedding modules to predict hand deformation displacements, albedo, and linear blending skinning weights, enabling the capture of fine-grained hand meshes.
• A mesh-based neural renderer is employed to achieve photo-realistic hand rendering on the fine-grained meshes, leveraging mesh topological consistency and latent codes from the embedding modules.
• A part-aware Laplace smoothing strategy is proposed during training to effectively maintain the necessary details and eliminate undesired artifacts.
• Experiments on the InterHand2.6M and DeepHandMesh datasets demonstrate the efficacy of XHand in recovering high-fidelity geometry and texture for hand animations in real-time.

🙋 Q&A

[01] Detailed Hand Representation

1. What are the key components of the XHand model for obtaining fine-grained hand meshes? The XHand model uses three feature embedding modules to predict:

• Hand deformation displacements
• Vertex albedo
• Linear blending skinning (LBS) weights

These modules utilize average features of the hand mesh and compute feature offsets for different poses, addressing the difficulty in directly learning dynamic personalized hand color and texture.

2. How does the XHand model refine the MANO hand mesh to capture intricate details? XHand employs a subdivision strategy to increase the resolution of the MANO mesh from 778 vertices and 1538 faces to 49,281 vertices and 98,432 faces. This allows the model to capture finer geometric details of the hand.

3. What is the purpose of the part-aware Laplace smoothing strategy used in XHand? The part-aware Laplace smoothing strategy is introduced to enhance the efficiency of geometric information extraction from images. It applies varying degrees of Laplacian smoothing regularization to different parts of the hand mesh, reducing excessive roughness without affecting the fine details.

[02] Mesh Rendering

1. How does the XHand model achieve real-time and photo-realistic rendering of the hand meshes? XHand employs a mesh-based neural renderer that leverages the latent codes from the feature embedding modules. This maintains the topological consistency of the mesh and preserves detailed features while minimizing artifacts through the use of various regularization levels.

2. What is the role of the Lambertian reflectance model in the XHand rendering pipeline? The Lambertian reflectance model is used in the inverse rendering process to effectively disentangle the hand geometry and appearance. By analyzing how the variations in brightness relate to the hand shape, the inverse rendering can accurately reconstruct the hand geometry.

[03] Experimental Results

1. How does the XHand model perform compared to other state-of-the-art methods in terms of rendering quality and geometry accuracy? Experimental results show that XHand outperforms previous methods, including parametric model-based approaches and NeRF-based techniques, in terms of rendering quality (PSNR, SSIM, LPIPS) and geometry accuracy (average point-to-surface distance).

2. What are the key factors that contribute to the superior performance of the XHand model? The key factors contributing to XHand's performance are:

• The effective feature embedding modules that distinguish between average and pose-dependent features
• The part-aware Laplace smoothing strategy that enhances geometric information extraction
• The mesh-based neural renderer that leverages the topological consistency of the hand mesh