Efficient 3D Neural Head Avatar with Vertex-Anchored Hash Table Blendshapes for Real-Time Rendering

Core Concepts

We propose a novel 3D neural head avatar representation that achieves real-time rendering while maintaining high-quality and fine-grained controllability. The key idea is to attach multiple small hash tables as "local blendshapes" to each vertex of an underlying 3D face model, which are then linearly merged with expression-dependent weights to produce expression-specific embeddings for efficient neural radiance field decoding.

Abstract

The paper presents a novel approach for building efficient and high-quality 3D neural head avatars from monocular RGB videos. The core of the method is the introduction of "mesh-anchored hash table blendshapes", where multiple small hash tables are attached to each vertex of a 3D face parametric model (3DMM). These hash tables act as local "blendshapes" that can be linearly combined with expression-dependent weights to capture fine-grained facial details.
The pipeline consists of three main steps:

Mesh-anchored Hash Table Blendshapes (Sec. 3.1): For each 3DMM vertex, multiple small hash tables are attached as local "blendshapes" to encode the local radiance field information.
Merge Mesh-anchored Blendshapes (Sec. 3.2): A U-Net CNN predicts expression-dependent weights to linearly combine the hash table blendshapes on each vertex, producing expression-specific embeddings.
Neural Radiance Field Decoding (Sec. 3.3): For each query point, the method performs a k-nearest-neighbor search on the 3DMM vertices to pull the merged hash table embeddings, which are then fed into a lightweight MLP to predict the final density and color.

The proposed representation enables efficient inference using hash encoding, while maintaining the high-quality and fine-grained controllability of 3DMM-anchored NeRFs. The authors also introduce a hierarchical k-NN search method to further accelerate the rendering speed. Extensive experiments demonstrate that the method achieves real-time rendering (>30 FPS) with comparable quality to state-of-the-art high-quality 3D head avatars, and significantly better results on challenging expressions compared to prior efficient 3D avatar approaches.

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Key Insights Distilled From

Efficient 3D Implicit Head Avatar with Mesh-anchored Hash Table Blendshapes

by Ziqian Bai,F... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01543.pdf

Deeper Inquiries

How can the proposed vertex-anchored hash table blendshapes be extended to handle other types of dynamic 3D content beyond head avatars, such as full-body avatars or articulated objects

The proposed vertex-anchored hash table blendshapes can be extended to handle other types of dynamic 3D content beyond head avatars by adapting the concept to different mesh structures and deformation models. For full-body avatars, the same idea of attaching multiple hash tables to each vertex can be applied to capture localized deformations and details specific to different body parts. By incorporating additional parameters for body poses and movements, the blendshapes can be controlled to represent dynamic poses and gestures. Similarly, for articulated objects, the hash tables can be attached to key joints or segments to capture the intricate movements and interactions between different parts. This extension would enable the efficient rendering of realistic and controllable full-body avatars or articulated objects in real-time applications.

What are the potential limitations of the current approach, and how could it be further improved to handle even more challenging facial expressions or lighting conditions

One potential limitation of the current approach is the complexity of handling extremely challenging facial expressions or varying lighting conditions. To further improve the method, several enhancements can be considered. Firstly, increasing the number of hash tables or refining the resolution of the embeddings within each hash table can provide more detailed representations of facial features and expressions. Additionally, incorporating adaptive weighting schemes based on the intensity of the expression or the lighting conditions can enhance the realism and accuracy of the rendered avatars. Furthermore, integrating advanced machine learning techniques such as reinforcement learning for adaptive blending weights or generative adversarial networks for realistic texture synthesis can further improve the quality and robustness of the rendering process. By addressing these aspects, the approach can better handle a wider range of challenging facial expressions and lighting scenarios.

The paper focuses on efficient rendering of 3D head avatars, but how could the underlying representation be leveraged for other applications like 3D reconstruction, animation, or editing of facial performances

The underlying representation of the proposed vertex-anchored hash table blendshapes can be leveraged for various other applications beyond efficient rendering of 3D head avatars. For 3D reconstruction, the blendshapes can serve as a basis for capturing detailed facial geometry and expressions, enabling the reconstruction of high-fidelity 3D models from 2D images or videos. In animation, the blendshapes can be utilized to drive facial rigging and animation systems, providing a more controllable and expressive way to animate characters with realistic facial movements. In facial performance editing, the blendshapes can be manipulated to modify facial expressions, correct imperfections, or enhance the realism of captured performances. By integrating the representation into these applications, it can facilitate the creation of immersive virtual experiences, interactive simulations, and advanced content creation tools in various domains.