GaussianTalker: A Novel Framework for Speaker-Specific Talking Head Synthesis using 3D Gaussian Splatting

Centrala begrepp

GaussianTalker is a novel framework that utilizes 3D Gaussian Splatting bound to the FLAME model to generate lifelike and realistic talking head videos by associating multimodal data with specific speakers.

Sammanfattning

The paper proposes GaussianTalker, a novel framework for audio-driven talking head synthesis that leverages 3D Gaussian Splatting and the FLAME model. The key highlights are:

1. GaussianTalker consists of two main modules:

   • Speaker-Specific Motion Translator: This module decouples identity information from audio features and employs personalized embedding to generate FLAME parameters that closely align with the target speaker's talking style.
   • Dynamic Gaussian Renderer: This module binds 3D Gaussians to the FLAME mesh, driving the Gaussians through the deformation of the FLAME model. It also introduces Speaker-specific BlendShapes to enhance the representation of facial details.

2. The framework achieves precise lip synchronization and exceptional visual quality, outperforming state-of-the-art methods in both quantitative and qualitative evaluations.

3. GaussianTalker can render videos at 130 FPS on an NVIDIA RTX4090 GPU, significantly exceeding real-time performance thresholds, and can potentially be deployed on other hardware platforms.

4. Extensive experiments and ablation studies demonstrate the effectiveness of the key components, including the Universal Audio Encoder, Speaker-specific BlendShapes, and Gaussian Semantic Loss.

Statistik

GaussianTalker achieves a PSNR of 37.08, SSIM of 0.9676, and LPIPS of 0.0239 on the test set.
The Landmark Distance (LMD) metric is 3.278, indicating accurate lip synchronization.
The Lip Sync Error Confidence (LSE-C) and Lip Sync Error Distance (LSE-D) scores are 7.015 and 7.562, respectively, outperforming other methods.
GaussianTalker can render videos at 130 FPS on an NVIDIA RTX4090 GPU.

Citat

"GaussianTalker consists of two main modules: Speaker-Specific Motion Translator and Dynamic Gaussian Renderer."
"By binding the Gaussians to the geometric topology of the FLAME model, dynamic talking heads can be generated by synchronizing the displacement of the bound Gaussians with changes in the facial attribute parameters."
"To address the challenge of unnatural lip movements caused by inconsistent distributions, we propose a Speaker-specific Motion Translator."
"To confront the challenge posed by unrealistic visual effects caused by the inherent limitations of 3D facial models, the Dynamic Gaussian Renderer introduces Speaker-specific BlendShapes."

Viktiga insikter från

GaussianTalker: Speaker-specific Talking Head Synthesis via 3D Gaussian Splatting

by Hongyun Yu,Z... på arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14037.pdf

Djupare frågor

How can the GaussianTalker framework be extended to handle more complex facial expressions and emotions beyond just lip movements?

To extend the GaussianTalker framework to handle more complex facial expressions and emotions beyond just lip movements, several enhancements can be implemented:

Facial Landmark Detection: Integrating advanced facial landmark detection algorithms can help capture a wider range of facial expressions, including eyebrow movements, eye expressions, and forehead wrinkles.

Emotion Recognition: Incorporating emotion recognition models can enable the system to interpret and replicate a broader spectrum of emotions such as happiness, sadness, anger, and surprise.

Dynamic Texture Mapping: Implementing dynamic texture mapping techniques can add realism to facial expressions by simulating changes in skin texture, wrinkles, and color variations based on emotional cues.

Facial Action Coding System (FACS): Utilizing FACS principles can provide a standardized way to categorize facial expressions and map them to corresponding muscle movements, enhancing the accuracy and diversity of expressions.

Machine Learning for Expression Generation: Training the system on a diverse dataset of facial expressions and emotions can improve its ability to generate realistic and nuanced expressions, going beyond basic lip movements.

By incorporating these enhancements, GaussianTalker can evolve into a more comprehensive and versatile framework capable of accurately capturing and synthesizing a wide range of facial expressions and emotions.