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Related Experiment Video

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High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields.

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|October 31, 2024

Summary

This summary is machine-generated.

This study introduces HH-NeRF, a new framework for creating high-resolution, realistic talking portrait videos from audio. It achieves fast rendering and high fidelity by combining a detail-aware neural radiance fields (NeRF) module with an efficient super-resolution module.

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Area of Science:

Computer Vision
Computer Graphics
Artificial Intelligence

Background:

Generating realistic talking portrait videos from audio is a challenging task in computer graphics.
Existing methods often struggle with high-resolution output, fast rendering, and capturing fine details like eye blinks.

Purpose of the Study:

To propose a novel rendering framework, HH-NeRF, for high-fidelity, audio-driven talking portrait video generation.
To achieve both high resolution and fast rendering speeds.

Main Methods:

Developed a detail-aware neural radiance fields (NeRF) module for high-fidelity low-resolution talking head generation.
Integrated an efficient conditional super-resolution module using depth maps and audio features for high-resolution video synthesis.
Utilized encoded volume density estimation and audio-eye-aware color calculation within the NeRF module.

Main Results:

HH-NeRF successfully generates high-resolution (900x900) talking portraits with enhanced fidelity and distinctness.
The framework captures natural eye blinks and high-frequency details effectively.
Achieved rendering times comparable to previous fast methods while significantly improving output quality.

Conclusions:

HH-NeRF presents a significant advancement in audio-driven talking portrait video generation.
The proposed framework offers a robust solution for creating realistic, high-resolution digital avatars.
The method outperforms state-of-the-art techniques in terms of visual quality and detail preservation.