TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting (2503.17032v1)

Abstract: Realistic 3D full-body talking avatars hold great potential in AR, with applications ranging from e-commerce live streaming to holographic communication. Despite advances in 3D Gaussian Splatting (3DGS) for lifelike avatar creation, existing methods struggle with fine-grained control of facial expressions and body movements in full-body talking tasks. Additionally, they often lack sufficient details and cannot run in real-time on mobile devices. We present TaoAvatar, a high-fidelity, lightweight, 3DGS-based full-body talking avatar driven by various signals. Our approach starts by creating a personalized clothed human parametric template that binds Gaussians to represent appearances. We then pre-train a StyleUnet-based network to handle complex pose-dependent non-rigid deformation, which can capture high-frequency appearance details but is too resource-intensive for mobile devices. To overcome this, we "bake" the non-rigid deformations into a lightweight MLP-based network using a distillation technique and develop blend shapes to compensate for details. Extensive experiments show that TaoAvatar achieves state-of-the-art rendering quality while running in real-time across various devices, maintaining 90 FPS on high-definition stereo devices such as the Apple Vision Pro.

• The paper introduces TaoAvatar, a real-time system for creating lifelike full-body talking avatars optimized for augmented reality using 3D Gaussian Splatting.
• TaoAvatar uses a hybrid parametric representation binding Gaussians to a clothed human template (SMPLX++) to capture both subtle facial expressions and complex body movements.
• The system achieves high rendering quality and performance, running on mobile devices like Apple Vision Pro at up to 90 FPS, enabling practical AR applications.

TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting

The paper "TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting" addresses the development of realistic 3D full-body avatars that can operate in real-time on mobile devices. This has significant implications for augmented reality (AR) applications, including digital communication and e-commerce.

Summary of Contributions

TaoAvatar represents a significant advance in 3D avatar creation, utilizing a high-fidelity, lightweight approach based on 3D Gaussian Splatting (3DGS) techniques to produce lifelike avatars. Existing methods in this domain face challenges such as precise facial expression and body movement control, as well as difficulties rendering in real-time on resource-constrained devices like mobile phones. TaoAvatar overcomes these challenges through a novel integration of several techniques and represents a technological progression in several key areas:

1. Augmented Reality Compatibility: The system generates topology-consistent 3D full-body avatars from multi-view sequences, optimized for performance across various devices such as the Apple Vision Pro, achieving up to 90 FPS.
2. Hybrid Parametric Representation: The approach involves carefully binding Gaussians to a personalized clothed human parametric template (SMPLX++), which ensures the avatars account for both fine-grained facial expressions and complex non-rigid body movements, accommodating high-frequency appearance details.
3. Teacher-Student Framework: TaoAvatar employs a teacher-student learning paradigm, where a StyleUnet acts as a teacher network to pre-train on capturing dynamic deformations. This new framework succeeds in distilling complex deformation into a lightweight Multi-Layer Perceptron (MLP)-based network for efficient rendering.
4. High Rendering Quality with Resource Efficiency: The paper introduces novel strategies to augment rendering quality without compromising on performance. Specifically, by baking non-rigid deformations into the mesh and using blend shapes, the system achieves high-quality rendering even on limited resource platforms.
5. Dataset Introduction: The introduction of the TalkBody4D dataset, which focuses on full-body talking scenarios enriched with diverse facial expressions and gestures, is a key development. This dataset facilitates rigorous evaluation of 3D avatar systems.

Implications for Research and Practice

The innovations presented in TaoAvatar can significantly influence both the academic landscape and industrial applications. The system's real-time capabilities align closely with industry needs for mobile and AR platforms, presenting opportunities for deploying digital humans in diverse contexts, such as interactive customer support, retail experiences, and virtual meetings.

Practically, improved avatar realism and responsiveness will enhance user experiences in AR settings, pushing the boundary of what's achievable in virtual communication. Theoretically, the proposed methods may inspire new research avenues, exploring deeper integrations of neural networks with geometrical processing to handle various rendering constraints and maintain expressiveness.

Potential Directions for Future Research

1. Enhanced Deformation Modeling: Further research could explore combining the TaoAvatar framework with advanced techniques in physical simulation to better handle complex, flexible clothing under exaggerated poses.
2. Incorporation of Physically-Based Rendering: Integrating TaoAvatar with physically-based rendering techniques might address challenges related to illumination variability and increase visual realism.
3. Broader Dataset Utilization: Future work could focus on diversified datasets to provide a broader range of body types, clothing, and environmental conditions, enriching training capabilities and increasing adaptability.

In conclusion, TaoAvatar represents a sophisticated achievement in the development of real-time, realistic avatars for AR applications, poised to enhance digital interactions significantly. By effectively balancing resource constraints and quality imperatives, it sets a benchmark in the computational graphics and AR research community.