Coherent 3D Portrait Video Reconstruction via Triplane Fusion: Preserving Temporal Consistency and Dynamic Appearance

Core Concepts

Our method fuses a personalized 3D subject prior with per-frame information to produce temporally stable 3D videos with faithful reconstruction of the user's dynamic appearances, such as facial expressions and lighting.

Abstract

The paper proposes a novel triplane fusion method for reconstructing coherent 3D portrait videos from a monocular RGB video. The key insight is to leverage a personalized 3D subject prior extracted from a frontal reference image, and fuse it with per-frame information to capture both temporal consistency and dynamic appearance of the user. The method first uses a pre-trained LP3D model to construct a personal triplane prior from a frontal image of the user. During video reconstruction, it lifts each input frame into a raw triplane, which is then undistorted and fused with the personal triplane prior. The undistorter module learns to correct view-dependent distortions in the raw triplane, while the fuser module densely aligns and fuses the undistorted triplane with the reference triplane, preserving dynamic lighting, expression and posture information from the input. The method is trained using only synthetic data generated by a pre-trained 3D GAN, with carefully designed augmentations to account for shoulder motion and lighting changes. Evaluations on both in-studio and in-the-wild datasets demonstrate that the proposed method achieves state-of-the-art performance in both temporal consistency and reconstruction accuracy, outperforming recent single-view 3D reconstruction and reenactment methods.

Stats

Our method achieves state-of-the-art performance in both temporal consistency and reconstruction accuracy, outperforming recent single-view 3D reconstruction and reenactment methods.

Quotes

"We recognize the need to maintain both coherent identity and dynamic per-frame appearance to enable the best possible realism." "Our key insight to solving this problem is to employ a fusion-based approach to achieve all of these properties: the approach needs to leverage the stability and accuracy of a personalized 3D prior, and needs to fuse the prior with per-frame observations to capture the diverse deviations from the prior."

Key Insights Distilled From

Coherent 3D Portrait Video Reconstruction via Triplane Fusion

by Shengze Wang... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00794.pdf

Deeper Inquiries

How could incorporating multiple reference images with different expressions and head poses further improve the fusion process and reconstruction quality

Incorporating multiple reference images with different expressions and head poses can significantly enhance the fusion process and reconstruction quality in several ways. Firstly, by utilizing a diverse set of reference images, the model can capture a broader range of facial expressions and head poses, leading to a more comprehensive understanding of the subject's dynamic appearance. This increased variability in the training data can help the model generalize better to unseen expressions and poses, resulting in more accurate reconstructions. Moreover, multiple reference images can provide complementary information that may not be present in a single image. For example, one reference image may capture a specific facial feature or expression more clearly than another, allowing the model to leverage this additional detail for improved reconstruction. By fusing information from multiple references, the model can create a more holistic and detailed representation of the subject, leading to higher fidelity reconstructions. Additionally, incorporating multiple reference images can help address occlusions and ambiguities that may arise from a single viewpoint. By combining information from different perspectives, the model can fill in missing details and resolve inconsistencies, resulting in more coherent and accurate reconstructions. Overall, the inclusion of multiple reference images can enrich the fusion process, enhance reconstruction quality, and improve the overall realism of the 3D portrait videos.

What are the potential limitations of the triplane representation, and how could alternative 3D representations be explored to address these limitations

While the triplane representation offers a compact and efficient way to encode 3D facial geometry, it also has certain limitations that may impact reconstruction quality. One potential limitation is the inherent ambiguity in single-image reconstruction, which can lead to distortions and artifacts in the reconstructed triplanes, especially for challenging viewpoints or expressions. Additionally, the triplane representation may struggle to capture fine details and subtle nuances in facial geometry, leading to potential loss of fidelity in the reconstructed 3D portraits. To address these limitations, alternative 3D representations could be explored to improve reconstruction quality. For example, voxel-based representations offer a volumetric approach that can capture detailed geometry and texture information more accurately. By representing the face as a 3D grid of voxels, the model can preserve fine details and handle occlusions more effectively, leading to higher fidelity reconstructions. Another alternative is mesh-based representations, such as the popular 3D Morphable Models (3DMM), which provide a flexible and detailed way to represent facial geometry. By leveraging a mesh representation, the model can capture complex facial shapes and expressions with greater precision, enhancing the realism of the reconstructed 3D portraits. Exploring these alternative representations alongside the triplane model can help overcome its limitations and improve reconstruction quality in 3D portrait video reconstruction.

How could the proposed fusion-based approach be extended to other domains beyond 3D portrait reconstruction, such as general 3D scene understanding and reconstruction

The proposed fusion-based approach for 3D portrait reconstruction can be extended to other domains beyond portrait reconstruction, such as general 3D scene understanding and reconstruction, by adapting the fusion process to accommodate different types of 3D data and structures. One potential application is in the field of 3D object reconstruction, where the fusion of multiple views or reference images can help create more accurate and detailed 3D models of objects from different perspectives. In the context of 3D scene understanding, the fusion-based approach can be applied to reconstruct complex scenes from single or multiple viewpoints, incorporating dynamic elements such as lighting changes, object movements, and environmental factors. By fusing information from various sources, such as depth sensors, RGB cameras, and LiDAR data, the model can create a comprehensive 3D representation of the scene with enhanced realism and accuracy. Furthermore, the fusion process can be adapted for applications in virtual reality (VR) and augmented reality (AR), where real-time 3D reconstruction and rendering are essential for immersive user experiences. By integrating the fusion-based approach with real-time data streams from sensors and cameras, the model can generate dynamic and coherent 3D scenes that respond to user interactions and environmental changes, enhancing the overall realism and interactivity of VR and AR applications. Overall, the fusion-based approach has the potential to revolutionize 3D scene understanding and reconstruction across various domains, opening up new possibilities for realistic and interactive 3D content creation.