One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing (2011.15126v3)

Abstract: We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. Our model learns to synthesize a talking-head video using a source image containing the target person's appearance and a driving video that dictates the motion in the output. Our motion is encoded based on a novel keypoint representation, where the identity-specific and motion-related information is decomposed unsupervisedly. Extensive experimental validation shows that our model outperforms competing methods on benchmark datasets. Moreover, our compact keypoint representation enables a video conferencing system that achieves the same visual quality as the commercial H.264 standard while only using one-tenth of the bandwidth. Besides, we show our keypoint representation allows the user to rotate the head during synthesis, which is useful for simulating face-to-face video conferencing experiences.

• The paper introduces a neural synthesis approach that decomposes talking-head videos into unsupervisedly-learned 3D keypoints, enabling dynamic free-view adjustments.
• The paper demonstrates a compact keypoint representation that maintains H.264-level visual quality while using only 10% of the typical bandwidth.
• The paper validates its method with extensive experiments, outperforming state-of-the-art techniques in video reconstruction, motion transfer, and face redirection.

Overview of "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

This paper, authored by Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu from NVIDIA Corporation, presents a novel approach to synthesizing talking-head videos, specifically for applications in video conferencing. The paper introduces a pure neural rendering method that utilizes a one-shot synthesis model, producing talking-head videos from a single source image and a driving video. This model marks a leap forward from traditional 3D graphics models by leveraging 2D techniques and introducing a novel 3D keypoint decomposition scheme, which separates person-specific geometries from motion-related transformations.

Key Contributions

1. Neural Synthesis Approach: The authors introduce a neural synthesis framework that efficiently decomposes a video into a series of unsupervisedly-learned 3D keypoints. This decomposition allows the model to capture intricate facial expressions and head movements, enabling it to synthesize realistic talking-head videos. Unlike existing methods, this approach facilitates the dynamic adjustment of viewpoint, allowing for simulating face-to-face interactions that are often lacking in typical video conferencing setups.
2. Compact Keypoint Representation: This system demonstrates a significant improvement in bandwidth efficiency. With the ability to maintain visual quality comparable to the H.264 standard while using just 10% of the bandwidth, the method offers substantial advancements in data transmission efficiency. Such efficiency is achieved by transmitting compact representations of keypoints, which encode motion and identity-specific details essential for video conferencing applications.
3. Free-View Video Synthesis: The approach enables local-free-view synthesis. Unlike previous methods, it allows for re-rendering the talking-head video from novel viewpoints. This capability is achieved through the proposed 3D keypoint decomposition, allowing users to manipulate the head pose and simulate different angles during video synthesis.

Experimental Validation

The paper supports its claims with extensive experimental verification against benchmark datasets. The results demonstrate that their model outperforms state-of-the-art methods in a variety of talking-head synthesis tasks, such as video reconstruction, motion transfer, and face redirection. The system not only produces higher visual quality and semantic consistency but also delivers quantitatively superior performance on conventional metrics like L1, PSNR, SSIM/MS-SSIM, and FID.

The authors present corroborative evidence that their method yields a significant bandwidth reduction in video conferencing compared to traditional codecs, such as H.264, without compromising on visual fidelity. This is particularly noteworthy given the increasing demand for efficient and high-quality video streaming solutions in contemporary communication technologies.

Implications and Future Directions

The implications of this research are profound for the field of artificial intelligence, particularly in video synthesis and remote communication. The approach introduces a scalable, bandwidth-efficient solution to video conferencing, which is crucial in today's digitally interconnected environment. From a theoretical perspective, the proposed 3D keypoint decomposition strategy can be applied beyond video conferencing, potentially influencing other domains such as virtual reality and synthetic media generation.

Future developments could focus on enhancing the robustness of the model under varying conditions, such as diverse lighting, different backgrounds, or occlusions, extending the model's capabilities. Moreover, exploring the integration of additional contextual information, such as audio cues, may further improve the realism and interactivity of the synthesized videos.

In conclusion, the paper presents a comprehensive methodological advancement in one-shot neural talking-head synthesis, offering a promising pathway towards low-bandwidth, high-quality video conferencing systems. The proposed framework not only addresses current technological constraints but also sets a precedent for future research in efficient neural rendering and synthetic media generation.