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LiveNeRF: Efficient Face Replacement Through Neural Radiance Fields Integration

Tung Vu, Hai Nguyen, Cong Tran

TL;DR

LiveNeRF proposes a unified, real-time framework that integrates face replacement directly into NeRF-based rendering to synthesize audio-driven talking heads from a single reference image. By combining a tri-plane hash representation, region- and eyeblink-aware conditioning, and an integrated face replacement module, the approach achieves competitive visual fidelity (PSNR ≈ 33 dB, LPIPS ≈ 0.031) at 33 FPS, while enabling zero-shot deployment without subject-specific training. The paper provides a theoretical complexity analysis and extensive empirical results demonstrating real-time performance, cross-subject robustness, and favorable comparisons to diffusion-based and Gaussian Splatting methods. This integration reduces computational overhead and enables practical deployment in live streaming, telepresence, and interactive media, albeit with acknowledged ethical considerations and potential misuse that must be mitigated with provenance verification and detection. Overall, LiveNeRF advances real-time, identity-preserving neural rendering by unifying motion synthesis and photorealistic rendering in a single, efficient framework with practical impact for interactive applications.

Abstract

Face replacement technology enables significant advancements in entertainment, education, and communication applications, including dubbing, virtual avatars, and cross-cultural content adaptation. Our LiveNeRF framework addresses critical limitations of existing methods by achieving real-time performance (33 FPS) with superior visual quality, enabling practical deployment in live streaming, video conferencing, and interactive media. The technology particularly benefits content creators, educators, and individuals with speech impairments through accessible avatar communication. While acknowledging potential misuse in unauthorized deepfake creation, we advocate for responsible deployment with user consent verification and integration with detection systems to ensure positive societal impact while minimizing risks.

LiveNeRF: Efficient Face Replacement Through Neural Radiance Fields Integration

TL;DR

LiveNeRF proposes a unified, real-time framework that integrates face replacement directly into NeRF-based rendering to synthesize audio-driven talking heads from a single reference image. By combining a tri-plane hash representation, region- and eyeblink-aware conditioning, and an integrated face replacement module, the approach achieves competitive visual fidelity (PSNR ≈ 33 dB, LPIPS ≈ 0.031) at 33 FPS, while enabling zero-shot deployment without subject-specific training. The paper provides a theoretical complexity analysis and extensive empirical results demonstrating real-time performance, cross-subject robustness, and favorable comparisons to diffusion-based and Gaussian Splatting methods. This integration reduces computational overhead and enables practical deployment in live streaming, telepresence, and interactive media, albeit with acknowledged ethical considerations and potential misuse that must be mitigated with provenance verification and detection. Overall, LiveNeRF advances real-time, identity-preserving neural rendering by unifying motion synthesis and photorealistic rendering in a single, efficient framework with practical impact for interactive applications.

Abstract

Face replacement technology enables significant advancements in entertainment, education, and communication applications, including dubbing, virtual avatars, and cross-cultural content adaptation. Our LiveNeRF framework addresses critical limitations of existing methods by achieving real-time performance (33 FPS) with superior visual quality, enabling practical deployment in live streaming, video conferencing, and interactive media. The technology particularly benefits content creators, educators, and individuals with speech impairments through accessible avatar communication. While acknowledging potential misuse in unauthorized deepfake creation, we advocate for responsible deployment with user consent verification and integration with detection systems to ensure positive societal impact while minimizing risks.

Paper Structure

This paper contains 35 sections, 1 theorem, 19 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Neural Rendering for Portrait Generation
  4. Audio-Driven Facial Animation
  5. Current Limitations and Research Gaps
  6. Proposed Methodology
  7. Problem Definition
  8. Audio Feature Extraction
  9. Enhanced ER-NeRF Architecture
  10. Tri-plane Hash Representation
  11. Region Attention Module
  12. Audio attention.
  13. Eyeblink attention.
  14. Volume Rendering
  15. Integrated Face Replacement
  16. ...and 20 more sections

Key Result

Theorem 1

Following Algorithm alg:livenerf, the proposed LiveNeRF framework achieves linear time complexity with respect to the number of output frames $N$, ensuring real-time performance for streaming applications.

Figures (3)

  • Figure 1: LiveNeRF Architecture: Enhanced ER-NeRF Pipeline with Real-Time Face Replacement Modules. Our approach extends ER-NeRF components with specialized real-time face manipulation modules to achieve efficient, high-quality cross-identity synthesis with minimal computational overhead.
  • Figure 2: Visual results of LiveNeRF applied to various source images.
  • Figure 3: Empirical validation of theoretical complexity predictions across different system components.

Theorems & Definitions (2)

  • Theorem 1: LiveNeRF Linear Scalability
  • proof