SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting

Temel Kavramlar

SplattingAvatar introduces a novel approach to creating realistic human avatars by combining Gaussian Splatting with trainable embeddings on a mesh, achieving high-quality rendering and computational efficiency.

Özet

SplattingAvatar presents a groundbreaking method for generating photorealistic human avatars by disentangling motion and appearance using mesh geometry and Gaussian splatting. The approach offers efficient real-time rendering capabilities while maintaining high-fidelity details across multiple datasets. By integrating explicit motion control with implicit rendering, SplattingAvatar showcases superior quality compared to existing methods like NeRF and MLP-based techniques.
The paper discusses the challenges faced in representing detailed geometry in 3D avatars and highlights the limitations of traditional mesh-based approaches. It introduces the concept of Neural Radiance Fields (NeRF) for capturing high-frequency details but points out ambiguities in reverse mapping processes.
The proposed solution leverages Gaussian Splatting with trainable embeddings on a mesh to address these challenges effectively. By optimizing parameters simultaneously, the method achieves accurate reconstruction of avatars from monocular videos, demonstrating state-of-the-art rendering quality and adaptability to diverse scenarios.
The experiments conducted on head and full-body avatars showcase the superiority of SplattingAvatar over existing methods in terms of rendering quality, pixel-wise errors, and perceptual metrics. The ablation study confirms the importance of trainable embeddings and scaling regularization in improving rendering results.
Overall, SplattingAvatar sets a new standard for creating realistic human avatars through innovative hybrid representations that combine explicit motion control with implicit rendering techniques.

İstatistikler

Our method achieves over 300 FPS on an NVIDIA RTX 3090 GPU and 30 FPS on an iPhone 13.
Trainable embedding E = {k, u, v, d} approximates a first-order continuous space around the mesh surface.
The Gaussians are parameterized by position, rotation, scale, color, and opacity for semi-transparent 3D particle rendering.
The pipeline involves differentiable Gaussian rendering based on mean µ and covariance matrix Σ for camera view splatting.
Optimization includes Adam optimization for Gaussian parameters and embedding parameters simultaneously.

Alıntılar

"By integrating explicit motion control with implicit rendering capabilities through Gaussian Splatting embedded on a mesh, our method achieves superior quality in avatar representation."
"Our approach demonstrates real-time rendering capabilities while maintaining high editability crucial for adapting avatars to various scenarios."
"The use of trainable embeddings allows Gaussians to optimize their locations on the mesh efficiently."

Önemli Bilgiler Şuradan Elde Edildi

SplattingAvatar

by Zhijing Shao... : arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05087.pdf

Daha Derin Sorular

How does SplattingAvatar's approach compare to other methods in terms of computational efficiency when handling complex or thin structures

SplattingAvatar's approach stands out in terms of computational efficiency when handling complex or thin structures compared to other methods. By disentangling motion and appearance, SplattingAvatar utilizes a hybrid representation that combines mesh-based motion control with Gaussian Splatting for rendering high-frequency geometry and detailed appearance details efficiently. This approach allows for the explicit control of Gaussians by the mesh movements, reducing computational load compared to traditional methods like MLP-based linear blend skinning (LBS) fields. The trainable embedding technique used in SplattingAvatar optimizes parameters simultaneously, enabling accurate reconstruction while maintaining efficiency.

What implications could the disentanglement of motion and appearance have on future developments in avatar creation beyond this study

The disentanglement of motion and appearance introduced by SplattingAvatar has significant implications for future developments in avatar creation beyond this study. By separating these two aspects, creators can have more flexibility and control over avatar customization and animation. This disentanglement opens up possibilities for creating more realistic avatars with diverse appearances and movements without compromising computational efficiency. Future advancements could leverage this concept to enhance personalization, realism, and expressiveness in virtual characters across various applications such as gaming, extended reality storytelling, tele-presentation, and more.

How might advancements in hybrid representations like those introduced by SplattingAvatar impact industries such as gaming or virtual reality storytelling

Advancements in hybrid representations like those introduced by SplattingAvatar could have a profound impact on industries such as gaming or virtual reality storytelling. By offering a novel approach that combines mesh-based motion representation with Gaussian Splatting for rendering high-quality avatars efficiently in real-time on modern GPUs or mobile devices, SplattingAvatar sets a new standard for avatar creation. In gaming, this technology could lead to more immersive experiences with lifelike characters that respond realistically to player interactions. In virtual reality storytelling, it could enable the creation of highly detailed human avatars that enhance narrative engagement and emotional connection with viewers/users.