Animatable Human Avatars via 3D Gaussian Splatting: Real-time Rendering of Dynamic Characters from Multi-view Videos

Core Concepts

This work proposes an efficient algorithm called Human Gaussian Splatting (HuGS) that represents and animates photorealistic human avatars using 3D Gaussian primitives. HuGS combines forward skinning and local non-rigid refinement to enable real-time rendering of dynamic characters from multi-view video inputs.

Abstract

The paper presents HuGS, a novel approach for creating and animating virtual human avatars based on 3D Gaussian Splatting. The key contributions are:

HuGS represents the human body using a set of 3D Gaussian primitives in a canonical space. This enables fast tile-based rasterization for real-time rendering, in contrast to the slow ray marching of neural radiance field approaches.

The Gaussian primitives are deformed using a coarse-to-fine approach. First, a linear blend skinning (LBS) module applies forward skinning to move the Gaussians according to the body pose. Then, a shallow neural network refines the local non-rigid deformations of the garments.

The method is trained end-to-end from multi-view video data, learning the Gaussian parameters and the skinning weights. Regularization terms are crucial to guide the over-parameterized model towards a solution that generalizes well to novel poses.

Extensive experiments on public datasets show that HuGS achieves state-of-the-art performance on both novel view and novel pose synthesis, while being one order of magnitude faster at rendering compared to previous neural rendering approaches.

The paper also discusses limitations, such as the degradation of reconstruction quality with sparse camera setups, and the inability to extrapolate novel garment deformations. Overall, HuGS demonstrates the potential of Gaussian Splatting for efficient and high-quality human avatar modeling and animation.

Stats

"Using multi-view video frames of a dynamic human, HuGS learns a photorealistic 3D human avatar represented by a 3D Gaussian Splatting model."
"Our method achieves 1.5 dB PSNR improvement over the state-of-the-art on THuman4 dataset while being able to render in real-time (≈80 fps for 512 × 512 resolution)."

Quotes

"While the classical approaches to model and render virtual humans generally use a textured mesh, recent research has developed neural body representations that achieve impressive visual quality. However, these models are difficult to render in real-time and their quality degrades when the character is animated with body poses different than the training observations."
"We propose an animatable human model based on 3D Gaussian Splatting, that has recently emerged as a very efficient alternative to neural radiance fields."

Key Insights Distilled From

Human Gaussian Splatting

by Arth... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2311.17113.pdf

Deeper Inquiries

How could the proposed Gaussian Splatting representation be extended to handle more complex garment deformations, such as wrinkles and folds, beyond the current local refinement approach?

In order to handle more complex garment deformations like wrinkles and folds, the HuGS method could be extended in several ways:

Physics-based Modeling: Integrate physics-based modeling techniques to simulate the behavior of fabrics and clothing materials. This would involve incorporating algorithms that can simulate the dynamics of cloth deformation based on factors like material properties, gravity, and external forces.

Cloth Simulation: Implement cloth simulation algorithms that can interact with the underlying Gaussian Splatting representation. By incorporating cloth simulation techniques, the avatars can exhibit realistic cloth movements, wrinkles, and folds based on the underlying body movements.

Dynamic Texture Mapping: Utilize dynamic texture mapping techniques to simulate the appearance of different fabric textures under varying lighting conditions and deformation. This would enhance the realism of the avatars by dynamically adjusting the texture mapping based on the garment deformation.

Multi-layered Representation: Develop a multi-layered representation for garments, where different layers of the clothing can be individually modeled and deformed. This would allow for more detailed and realistic representation of complex garment structures.

Machine Learning for Garment Deformation: Explore the use of machine learning algorithms to learn and predict garment deformations based on the underlying body movements. This could involve training models on a diverse dataset of garment deformations to improve the accuracy of the deformation process.

By incorporating these advanced techniques, the HuGS framework can be extended to handle more intricate garment deformations, resulting in highly realistic and dynamic virtual human avatars.

How could the HuGS method be adapted to enable interactive control and editing of the virtual human avatars, beyond just rendering novel poses?

To enable interactive control and editing of virtual human avatars using the HuGS method, the following adaptations can be considered:

Real-time Pose Editing: Implement a user interface that allows real-time manipulation of the avatar's pose. Users can interactively adjust the body movements, gestures, and expressions of the avatar using intuitive controls.

Parameterized Controls: Introduce parameterized controls that enable users to modify specific aspects of the avatar, such as body shape, clothing style, and facial features. These parameters can be adjusted in real-time to customize the avatar according to user preferences.

Keyframe Animation: Incorporate keyframe animation techniques to create predefined poses and animations that can be easily applied to the avatar. Users can select from a library of keyframes or create custom animations for the avatar.

Inverse Kinematics: Implement inverse kinematics algorithms to ensure natural and realistic movements of the avatar based on user input. This would allow for smooth and natural transitions between different poses and gestures.

Interactive Rendering: Enable interactive rendering capabilities that provide immediate feedback on the changes made to the avatar. Users can see the effects of their edits in real-time, facilitating a more engaging and interactive editing experience.

By incorporating these adaptations, the HuGS framework can be transformed into a versatile tool for interactive control and editing of virtual human avatars, enhancing user engagement and customization options.