JGHand: Joint-Driven Animatable Hand Avater via 3D Gaussian Splatting

Hands are a primary interface in daily interactions, making high-quality, controllable hand modeling and realistic real-time rendering crucial. Thus, we propose JGHand (Joint-driven 3D Gaussian Hand), a novel 3D Gaussian Splatting (3DGS)-based hand representation that renders high-fidelity hand images in real time across diverse poses and characters. Unlike existing articulated neural rendering techniques, we introduce a differentiable spatial transformation process based on 3D key points, enabling flexible deformations for varying bone lengths and poses. Additionally, we propose a real-time shadow simulation method based on per-pixel depth to simulate self-occlusion shadows from finger movements. Finally, we incorporate hand priors to develop an animatable 3DGS hand representation driven solely by 3D key points. We validate the effectiveness of each component through comprehensive ablation studies. Experimental results on public datasets demonstrate that JGHand achieves real-time rendering speeds with enhanced quality, surpassing state-of-the-art methods.