Photorealistic Head Avatar Animation using 3D Gaussian Blendshapes

Główne pojęcia

A 3D Gaussian blendshape representation is introduced to model and animate photorealistic head avatars in real-time, outperforming state-of-the-art methods in quality and speed.

Streszczenie

The authors present a novel 3D Gaussian blendshape representation for modeling and animating photorealistic head avatars. The representation consists of a neutral base model and a group of expression blendshapes, all represented as 3D Gaussians.

The neutral model and expression blendshapes are learned from a monocular video input. The authors introduce an effective optimization strategy to ensure the semantic consistency between the Gaussian blendshapes and the corresponding mesh blendshapes from classical parametric face models. This allows the Gaussian blendshapes to be linearly blended with expression coefficients to synthesize high-fidelity head avatar animations in real-time (370fps) using Gaussian splatting.

Compared to state-of-the-art NeRF-based and point-based methods, the authors' Gaussian blendshape representation better captures high-frequency details observed in the input video and achieves significantly faster animation and rendering performance. The method also supports head motion control through joint and pose parameters.

The authors conduct extensive experiments and comparisons, demonstrating the superiority of their approach in terms of image quality metrics and runtime efficiency. They also provide ablation studies to validate the importance of the blendshape consistency and the mouth interior Gaussians.

Statystyki

Our method achieves 370fps for rendering head avatar animations.
NeRFBlendShape achieves 26fps, while INSTA achieves 70fps.
PointAvatar runs at 5fps on the same GPU as our method.

Cytaty

"Our 3D Gaussian blendshapes are analogous to mesh blendshapes in classical parametric face models, which can be linearly blended with expressions coefficients to synthesize photo-realistic avatar animations in real time (370fps)."
"Compared to state-of-the-art methods, our Gaussian blendshape representation better captures high-frequency details exhibited in input video, and achieves superior rendering performance."

Kluczowe wnioski z

3D Gaussian Blendshapes for Head Avatar Animation

by Shengjie Ma,... o arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19398.pdf

Głębsze pytania

How can the Gaussian blendshape representation be extended to handle more complex head and hair geometry beyond the current neutral model and expression blendshapes?

In order to extend the Gaussian blendshape representation to handle more complex head and hair geometry, several approaches can be considered:

Additional Blendshapes: Introducing more blendshapes beyond the current neutral model and expression blendshapes can help capture a wider range of facial expressions and head movements. These additional blendshapes can represent more intricate details in the geometry of the head and hair.

Dynamic Geometry: Incorporating dynamic geometry changes in the blendshapes can enhance the representation of complex head movements, such as hair swaying or facial muscles deforming during speech or expressions. This dynamic aspect can be achieved by updating the Gaussian properties over time to reflect these changes.

Hair Simulation: To handle complex hair geometry, specialized Gaussian representations can be introduced to model hair strands or volumes. These Gaussians can be controlled separately from the head model blendshapes to simulate realistic hair movements and interactions with the environment.

Hierarchical Blendshapes: Implementing a hierarchical structure of blendshapes can allow for more detailed control over specific regions of the head and hair. By organizing blendshapes in a hierarchical manner, finer details and complex interactions can be represented more effectively.

Texture and Color Blending: Extending the Gaussian blendshape representation to include texture and color information can further enhance the realism of the head and hair models. By blending texture and color properties along with geometric properties, a more comprehensive representation of complex head and hair geometry can be achieved.

By incorporating these strategies, the Gaussian blendshape representation can be extended to handle a broader range of complexities in head and hair geometry, enabling more realistic and detailed avatar animations.

How could non-linear blending techniques be incorporated to handle more exaggerated facial expressions, and what are the potential limitations of the linear blending approach used in this work?

Non-linear blending techniques can be incorporated to handle more exaggerated facial expressions by introducing more complex relationships between the blendshapes and expression coefficients. Some ways to incorporate non-linear blending techniques include:

Non-linear Weighting Functions: Instead of linearly interpolating between blendshapes based on expression coefficients, non-linear weighting functions can be used to assign varying degrees of influence to each blendshape. Functions like sigmoid, exponential, or polynomial functions can introduce non-linearities in the blending process.

Piecewise Blending: Dividing the range of expression coefficients into segments and applying different blending functions to each segment can allow for non-linear transformations in specific regions of the expression space. This approach can handle exaggerated expressions more effectively by focusing on critical areas of the face.

Deep Learning Models: Utilizing deep learning models, such as neural networks, can learn complex non-linear mappings between expression coefficients and blendshapes. These models can capture intricate relationships and nuances in facial expressions that may not be adequately represented by linear blending.

The potential limitations of the linear blending approach used in this work include:

Limited Expressiveness: Linear blending may not capture the full range of facial expressions and deformations, especially when dealing with exaggerated or extreme expressions. Non-linear blending can provide more flexibility in representing these complex variations.

Artifacts and Discontinuities: Linear blending may lead to artifacts or discontinuities in the animation when transitioning between blendshapes, particularly in regions where non-linear deformations are required. Non-linear blending can smooth out these transitions and improve the overall visual quality.

Difficulty in Fine Detail Representation: Linear blending may struggle to represent fine details and subtle nuances in facial expressions, as it relies on a simple linear combination of blendshapes. Non-linear techniques can better capture these intricate details and improve the fidelity of the animation.

By incorporating non-linear blending techniques, the representation of facial expressions can be enhanced to handle more exaggerated deformations and improve the overall realism of the avatar animations.

Given the real-time performance of the Gaussian blendshape method, how could it be leveraged in interactive applications such as virtual avatars, telepresence, or mixed reality experiences?

The real-time performance of the Gaussian blendshape method opens up various possibilities for interactive applications in virtual avatars, telepresence, and mixed reality experiences:

Virtual Avatars: The real-time rendering capabilities of the Gaussian blendshape method can be leveraged to create interactive virtual avatars that respond to user inputs in real-time. Users can control the expressions and movements of the avatar, leading to engaging and immersive virtual interactions.

Telepresence: In telepresence applications, the Gaussian blendshape method can enable real-time facial animation and expression transfer, allowing users to communicate with others in a more natural and expressive manner. This can enhance the sense of presence and emotional connection in remote communication.

Mixed Reality Experiences: By integrating the Gaussian blendshape method with mixed reality technologies, such as augmented reality (AR) or virtual reality (VR), interactive experiences can be enhanced with realistic and responsive avatars. Users can interact with virtual characters that mirror their facial expressions and gestures in real-time.

Gaming and Entertainment: The real-time performance of the Gaussian blendshape method can be utilized in gaming and entertainment applications to create lifelike characters with dynamic facial animations. Players can control the avatars' expressions and movements, leading to more immersive and engaging gameplay experiences.

Personalized Content Creation: Content creators can use the Gaussian blendshape method in real-time applications for personalized content creation, such as live streaming, virtual events, or interactive storytelling. The method allows for on-the-fly facial animation and expression manipulation, enabling dynamic and engaging content production.

Overall, the real-time capabilities of the Gaussian blendshape method open up a wide range of possibilities for interactive applications, offering enhanced user experiences and creative opportunities in various domains.