Gaussian Head Avatar: Ultra High-Fidelity 3D Head Avatars with Controllable Expressions
核心概念
This paper proposes Gaussian Head Avatar, a novel representation that utilizes controllable 3D Gaussians to model expressive human head avatars, producing ultra high-fidelity synthesized images at 2K resolutions.
摘要
The paper introduces Gaussian Head Avatar, a new head avatar representation that employs controllable dynamic 3D Gaussians to model expressive human head avatars. The key highlights are:
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Gaussian Head Avatar uses a canonical neutral Gaussian model with expression-independent attributes, and a fully learned MLP-based expression-conditioned dynamic generator to capture complex expressions and dynamic details.
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The dynamic generator predicts displacements, color, rotation, scale, and opacity changes of the neutral Gaussians based on the input expression coefficients and head pose. This allows the model to accurately represent exaggerated and fine-grained facial expressions.
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The authors propose an efficient geometry-guided initialization strategy that leverages implicit signed distance functions and Deep Marching Tetrahedra to initialize the neutral Gaussian geometry and the dynamic generator, leading to stable training and convergence.
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Experiments show that the proposed Gaussian Head Avatar outperforms recent state-of-the-art methods in terms of reconstruction quality, achieving ultra high-fidelity rendering at 2K resolution even under exaggerated expressions.
Gaussian Head Avatar
統計資料
"Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups."
"Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions."
引述
"We propose Gaussian Head Avatar, a new head avatar representation that employs controllable dynamic 3D Gaussians to model expressive human head avatars, producing ultra high-fidelity synthesized images at 2K resolutions."
"For modeling high-frequency dynamic details, we employ a fully learned deformation field upon the 3D head Gaussians, which accurately model extremely complex and exaggerated facial expressions."
"We carefully design an efficient initialization strategy that leverages implicit representations to initialize the geometry and deformation, leading to efficient and robust convergence when training the Gaussian Head Avatar."
深入探究
How can the proposed Gaussian Head Avatar representation be extended to model full-body avatars with detailed clothing and accessories
To extend the Gaussian Head Avatar representation to model full-body avatars with detailed clothing and accessories, several modifications and enhancements can be implemented. One approach could involve incorporating additional Gaussian splatting techniques to represent the clothing and accessories as dynamic 3D Gaussians. By defining the positions, colors, rotations, scales, and opacities of these Gaussians, it would be possible to capture the intricate details of the clothing and accessories in a realistic manner. Furthermore, the deformation field MLPs could be adapted to handle the movement and deformation of the clothing and accessories based on the underlying body movements. This would allow for a seamless integration of the full-body avatar with detailed clothing and accessories, enhancing the overall realism and fidelity of the representation.
What are the potential limitations of the current Gaussian Head Avatar model, and how could it be further improved to handle more challenging scenarios such as occlusions or extreme head poses
While the Gaussian Head Avatar model offers significant advantages in high-fidelity image synthesis and expression accuracy, there are potential limitations that could be addressed for further improvement. One limitation is the handling of occlusions, where certain parts of the head may be obstructed or hidden from view. To overcome this challenge, advanced occlusion handling techniques could be integrated into the model, such as adaptive Gaussian representations that adjust based on occluded regions. Additionally, extreme head poses could pose a challenge in maintaining the accuracy of the model. Techniques like hierarchical Gaussian representations or adaptive deformation fields could be explored to better handle extreme head poses and ensure the model's robustness in challenging scenarios.
Given the high-fidelity rendering capabilities of the Gaussian Head Avatar, how could it be leveraged in applications beyond virtual avatars, such as photorealistic digital humans for film and gaming
The high-fidelity rendering capabilities of the Gaussian Head Avatar model open up a range of possibilities for applications beyond virtual avatars. In the context of photorealistic digital humans for film and gaming, the Gaussian Head Avatar could revolutionize character creation and animation. By leveraging the detailed dynamic Gaussians and fully learned deformation fields, filmmakers and game developers could create lifelike digital characters with unparalleled realism and expressiveness. The model could be used to generate realistic facial animations, intricate expressions, and nuanced movements, enhancing the storytelling and immersive experience for audiences. Additionally, the Gaussian Head Avatar could streamline the character creation process, allowing for efficient production of high-quality digital humans for various media and entertainment applications.
