STGV: Spatio-Temporal Hash Encoding for Gaussian-based Video Representation

• URL: http://arxiv.org/abs/2604.10910v2
• Date: Tue, 14 Apr 2026 02:52:04 GMT
• Title: STGV: Spatio-Temporal Hash Encoding for Gaussian-based Video Representation
• Abstract summary: 2DGS has recently become a promising paradigm for high-quality video representation.<n>This paper proposes a framework for Gaussian-Temporal Video representation (STGV)<n>Our method attains better video representation quality (+ PSN) against other Gaussian-based methods and achieves competitive performance in downstream video tasks.
• Score: 44.219307092626046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 2D Gaussian Splatting (2DGS) has recently become a promising paradigm for high-quality video representation. However, existing methods employ content-agnostic or spatio-temporal feature overlapping embeddings to predict canonical Gaussian primitive deformations, which entangles static and dynamic components in videos and prevents modeling their distinct properties effectively. These result in inaccurate predictions for spatio-temporal deformations and unsatisfactory representation quality. To address these problems, this paper proposes a Spatio-Temporal hash encoding framework for Gaussian-based Video representation (STGV). By decomposing video features into learnable 2D spatial and 3D temporal hash encodings, STGV effectively facilitates the learning of motion patterns for dynamic components while maintaining background details for static elements. In addition, we construct a more stable and consistent initial canonical Gaussian representation through a key frame canonical initialization strategy, preventing from feature overlapping and a structurally incoherent geometry representation. Experimental results demonstrate that our method attains better video representation quality (+0.98 PSNR) against other Gaussian-based methods and achieves competitive performance in downstream video tasks.

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