LayerGS: Decomposition and Inpainting of Layered 3D Human Avatars via 2D Gaussian Splatting
This work addresses the challenge of creating high-fidelity 3D human assets for immersive applications like virtual try-on, though it is incremental as it builds on prior multi-layer approaches.
The paper tackles the problem of decomposing arbitrarily posed humans into animatable multi-layered 3D avatars by separating body and garments, achieving better rendering quality and layer decomposition than previous state-of-the-art methods on benchmark datasets like 4D-Dress and Thuman2.0.
We propose a novel framework for decomposing arbitrarily posed humans into animatable multi-layered 3D human avatars, separating the body and garments. Conventional single-layer reconstruction methods lock clothing to one identity, while prior multi-layer approaches struggle with occluded regions. We overcome both limitations by encoding each layer as a set of 2D Gaussians for accurate geometry and photorealistic rendering, and inpainting hidden regions with a pretrained 2D diffusion model via score-distillation sampling (SDS). Our three-stage training strategy first reconstructs the coarse canonical garment via single-layer reconstruction, followed by multi-layer training to jointly recover the inner-layer body and outer-layer garment details. Experiments on two 3D human benchmark datasets (4D-Dress, Thuman2.0) show that our approach achieves better rendering quality and layer decomposition and recomposition than the previous state-of-the-art, enabling realistic virtual try-on under novel viewpoints and poses, and advancing practical creation of high-fidelity 3D human assets for immersive applications. Our code is available at https://github.com/RockyXu66/LayerGS