GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians (2312.02134v3)

Abstract: We present GaussianAvatar, an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video. We start by introducing animatable 3D Gaussians to explicitly represent humans in various poses and clothing styles. Such an explicit and animatable representation can fuse 3D appearances more efficiently and consistently from 2D observations. Our representation is further augmented with dynamic properties to support pose-dependent appearance modeling, where a dynamic appearance network along with an optimizable feature tensor is designed to learn the motion-to-appearance mapping. Moreover, by leveraging the differentiable motion condition, our method enables a joint optimization of motions and appearances during avatar modeling, which helps to tackle the long-standing issue of inaccurate motion estimation in monocular settings. The efficacy of GaussianAvatar is validated on both the public dataset and our collected dataset, demonstrating its superior performances in terms of appearance quality and rendering efficiency.

• The paper introduces animatable 3D Gaussians that jointly optimize motion and appearance for realistic human avatar modeling.
• It achieves significant improvements in quality, evidenced by higher PSNR and SSIM across varied poses and clothing styles.
• The method robustly animates avatars under novel motions, opening pathways for advanced VR, film, and Metaverse applications.

Overview of GaussianAvatar: Realistic Human Avatar Modeling from a Single Video

The paper "GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians" proposes a novel approach for creating animatable human avatars using videos captured with a single camera. The method, named GaussianAvatar, leverages the strengths of 3D Gaussians to establish an animatable and explicit representation of human subjects, facilitating effective human avatar modeling.

Methodology

The authors introduce animatable 3D Gaussians as the core representation for human avatars. This explicit modeling allows for consistent and efficient fusion of 3D appearances from 2D observations. The representation is enhanced with dynamic properties, enabling pose-dependent appearance modeling through the design of a dynamic appearance network and an optimizable feature tensor. This network learns the mapping from motion to appearance, thus capturing human dynamics effectively.

A pivotal aspect of their methodology is the joint optimization of motion and appearance during the avatar modeling process. This approach addresses challenges associated with inaccurate motion estimation, a common issue in monocular video settings. By optimizing both appearance and motion parameters concurrently, the proposed method enhances the accuracy of avatar modeling and reduces artifacts in the rendered outcomes.

Key Results

The efficacy of GaussianAvatar is validated using both public datasets and a newly collected dataset. The results demonstrate superior performance in terms of appearance quality and rendering efficiency when compared to existing methods. Specifically, the approach achieves notable improvements in metrics such as PSNR and SSIM across various scenarios, which include different poses, clothing styles, and motion types.

Furthermore, the authors illustrate that the method can accurately animate avatars using out-of-distribution motions, maintaining a 3D consistent appearance across novel viewpoints. This robustness is crucial for applications in virtual reality, film production, and the emerging Metaverse.

Implications

The introduction of animatable 3D Gaussians as a representation for human avatars from monocular video advances the field by offering a more efficient and precise method for modeling dynamic human surfaces. This work has significant implications for real-time applications, where rendering speed and quality are paramount. The explicit representation also opens avenues for further research on integrating machine learning techniques to automate and refine tasks like pose estimation and dynamic appearance mapping.

The paper's insights suggest potential extensions, such as the incorporation of more complex clothing or hair dynamics and further exploration into hand animations, which are mentioned as successful without additional training.

Future Directions

Considering the constraints mentioned in the paper, such as the challenges with inaccurate segmentations and loose clothing, future research may focus on augmenting the scene understanding aspect of avatar modeling. Integrating scene models or employing more advanced segmentation techniques could mitigate these limitations.

Moreover, collaborations that integrate this method with other motion capture improvements could refine the accuracy of avatar animations. This prospective synergy could substantially enhance both qualitative and quantitative outcomes for avatar representations in more complex environments.

Conclusion

GaussianAvatar presents a significant contribution to the field of human avatar modeling from monocular videos by rethinking the representation of avatars through animatable 3D Gaussians. The methodological innovations combined with strong experimental results position this approach as a noteworthy advancement in efficient and realistic avatar generation for various applications. Continued exploration and refinement of this method could push the boundaries of what's possible in virtual human representation and animation.