Real3D-Portrait: One-Shot Realistic 3D Talking Portrait Synthesis at ICLR 2024

Core Concepts

Real3D-Portrait presents a framework for one-shot and realistic 3D talking portrait synthesis, achieving accurate 3D avatar reconstruction and animation with natural torso movement and switchable background.

Abstract

Abstract:

Real3D-Portrait aims to reconstruct a 3D avatar from an unseen image and animate it with a reference video or audio.
Existing methods fail to achieve accurate reconstruction and stable animation simultaneously.
The framework improves one-shot 3D reconstruction power, motion-conditioned animation, realistic video synthesis, and audio-driven face generation.
Introduction:

Talking head generation is crucial in computer graphics with real-world applications like video conferencing.
Neural radiance field-based 3D methods maintain realistic geometry but are often overfitted to specific persons.
Real3D-Portrait addresses the limitations of existing methods by improving reconstruction, animation, torso/background synthesis, and audio-to-motion transformation.
Data Extraction:

"Published as a conference paper at ICLR 2024"
"Video samples and source code are available at https://real3dportrait.github.io"
"arXiv:2401.08503v3 [cs.CV] 23 Mar 2024"

Stats

Published as a conference paper at ICLR 2024
Video samples and source code are available at https://real3dportrait.github.io
arXiv:2401.08503v3 [cs.CV] 23 Mar 2024

Quotes

"One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image."
"Real3D-Potrait improves the one-shot 3D reconstruction power with a large Image-to-plane model."
"Our method outperforms existing one-shot talking face systems."

Key Insights Distilled From

Real3D-Portrait

by Zhenhui Ye,T... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2401.08503.pdf

Deeper Inquiries

How can Real3D-Portrait's watermarks help prevent misuse of synthesized videos

Real3D-Portrait's watermarks can help prevent misuse of synthesized videos by serving as a visible indicator to the public that the video has been artificially generated. This transparency allows viewers to easily discern between real and synthetic content, reducing the likelihood of misinformation or deceptive practices. The presence of watermarks acts as a deterrent against unauthorized use or distribution of deepfake videos, as individuals are less likely to pass off manipulated content as authentic when clear markers indicate otherwise.

What ethical considerations should be taken into account when using deepfake-related technologies

When using deepfake-related technologies, several ethical considerations must be taken into account to mitigate potential harm and misuse. Firstly, there is a significant concern regarding privacy violations and consent issues when creating deepfake content without the explicit permission of individuals featured in the videos. It is crucial to respect individuals' rights over their likeness and ensure that they have given informed consent for any manipulation involving their image or voice.
Secondly, there is a risk of spreading misinformation and fake news through deepfake technology, leading to reputational damage, social unrest, or political manipulation. Users should exercise caution and responsibility when creating and sharing deepfake content to avoid contributing to disinformation campaigns or malicious activities.
Moreover, ethical guidelines should be established around the permissible uses of deepfakes in various contexts such as entertainment, journalism, research, or art. Clear regulations on how these technologies can be employed ethically while safeguarding individual rights and societal well-being are essential for maintaining trust in digital media landscapes.
Lastly, measures should be implemented to detect and counteract malicious uses of deepfakes such as fraudulence scams or identity theft. Education on recognizing manipulated media and promoting media literacy among users can also help combat the negative impacts associated with deceptive visual/audio content.

How does Real3D-Portrait compare to person-specific methods in terms of performance and efficiency

In terms of performance and efficiency compared to person-specific methods like RAD-NeRF (a state-of-the-art person-specific method), Real3D-Portrait demonstrates notable strengths while offering comparable results:

Performance: Real3D-Portrait achieves high-quality results in terms of identity preservation (CSIM), image fidelity (FID), visual quality (LPIPS), lip synchronization accuracy (AED), PSNR/SSIM scores for video-driven reenactment scenarios.

Efficiency: Unlike person-specific methods that require extensive training time for each new identity due to individual model fitting processes like RAD-NeRF does over 3-minute-long videos per subject; Real3D-Portrait offers one-shot generation capabilities across multiple identities without requiring lengthy training periods tailored specifically for each new face.

Overall Real3D-Portrait strikes a balance between performance quality similar-to-state-of-the-art techniques while providing efficiency benefits through its one-shot approach suitable for diverse applications needing rapid avatar synthesis from unseen images/audio inputs.