Accurately representing and rendering dynamic scenes over time remains a central challenge in neural rendering and computer graphics. Existing dynamic Gaussian-based methods often suffer from limited temporal consistency, flickering under fast motion, and poor adaptability to non-human structures. To address these issues, we propose DG-4DGS, a deformation-graph-constrained 4D Gaussian splatting framework for temporally stable dynamic rendering. The method anchors all Gaussians in a canonical space and enforces cross-frame geometric alignment through a deformation graph. Based on neighborhood-consistency features, a multi-head residual decoder refines position, rotation/scale, and color attributes to achieve fine-detail fidelity without relying on online densification or pruning. Compared with 4DGS and avatar-based approaches, DG-4DGS achieves higher PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) scores and significantly smaller model size on both the TalkBody4D (human) and Horse (non-human) datasets. It effectively suppresses temporal flickering and cross-frame drift in high-frequency regions such as hair strands, cloth wrinkles, and limb extremities. The framework does not depend on parametric templates, facilitating extension to non-human and complex clothing scenarios, though its performance still depends on deformation-tracking quality and neighborhood topology selection.
CHEN et al. (2026) studied this question.