GSAC: Leveraging Gaussian Splatting for Photorealistic Avatar Creation with Unity Integration
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Abstract (arXiv)
Photorealistic avatars have become essential for immersive applications in virtual reality (VR) and augmented reality (AR), enabling lifelike interactions in areas such as training simulations, telemedicine, and virtual collaboration. These avatars bridge the gap between the physical and digital worlds, improving the user experience through realistic human representation. However, existing avatar creation techniques face significant challenges, including high costs, long creation times, and limited utility in virtual applications. Manual methods, such as MetaHuman, require extensive time and expertise, while automatic approaches, such as NeRF-based pipelines often lack efficiency, detailed facial expression fidelity, and are unable to be rendered at a speed sufficent for real-time applications. By involving several cutting-edge modern techniques, we introduce an end-to-end 3D Gaussian Splatting (3DGS) avatar creation pipeline that leverages monocular video input to create a scalable and efficient photorealistic avatar directly compatible with the Unity game engine. Our pipeline incorporates a novel Gaussian splatting technique with customized preprocessing that enables the user of "in the wild" monocular video capture, detailed facial expression reconstruction and embedding within a fully rigged avatar model. Additionally, we present a Unity-integrated Gaussian Splatting Avatar Editor, offering a user-friendly environment for VR/AR application development. Experimental results validate the effectiveness of our preprocessing pipeline in standardizing custom data for 3DGS training and demonstrate the versatility of Gaussian avatars in Unity, highlighting the scalability and practicality of our approach.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 17 Apr 2025.
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32 of 53 references.
References · 32
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
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- 2020Learning an Animatable Detailed 3D Face Model from In-The-Wild Imagescited by 477
- 2022Fast Dynamic Radiance Fields with Time-Aware Neural Voxelscited by 346
20 more
- 2016Convolutional Pose Machinescited by 325
- 2022Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusioncited by 309
- 2018Video Based Reconstruction of 3D People Modelscited by 238
- 2017Hand Keypoint Detection in Single Images using Multiview Bootstrappingcited by 231
- 2024Sapiens: Foundation for Human Vision Modelscited by 229
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- 20233DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splattingcited by 195
- 2023COLMAP-Free 3D Gaussian Splattingcited by 193
- 2023GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussianscited by 191
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- 2023GART: Gaussian Articulated Template Modelscited by 121
- 2023Drivable 3D Gaussian Avatarscited by 120
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- 2020Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimationcited by 81
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- 2024A Survey on 3D Human Avatar Modeling -- From Reconstruction to Generationcited by 13
- 2024Am I the Odd One? Exploring (In)Congruencies in the Realism of Avatars and Virtual Others in Virtual Realitycited by 1
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