Versatile Video Representation via Feed-Forward 2D Gaussian Splatting Tokenization

Recent video representation methods that rely on fixed-grid, patch-wise tokenization often exhibit limited versatility.Spatially, uniformly allocating a fixed number of tokens often leads to over-encoding in low-information regions. Temporally, reducing redundancy remains challenging without explicitly distinguishing between static and dynamic content. In this work, we introduce the Gaussian Video Transformer (GVT), a versatile video representation framework built on a feed-forward 2D Gaussian Splatting (2DGS) tokenization scheme. We first extract latent rigid features from a video clip and represent them with a set of 2D Gaussians generated by our proposed Spatio-Temporal Gaussian Embedding (STGE) mechanism in a feed-forward manner. Such 2D Gaussians not only enhance spatial adaptability by assigning higher (resp., lower) rendering weights to regions with higher (resp., lower) information content during rasterization, but also improve generalization by avoiding per-video optimization. To enhance the temporal versatility, we introduce a Gaussian Set Partitioning (GSP) strategy that separates the 2D Gaussians into static and dynamic sets, which explicitly model static content shared across different time-steps and dynamic content specific to each time-step, enabling a compact representation. We evaluate GVT across four tasks: video reconstruction, video action recognition, video compression, and video generation, on the UCF101, Kinetics, and DAVIS datasets. The results demonstrate state-of-the-art reconstruction and compression performance, improved action recognition, and video generation performance comparable to the baseline MAGVIT-v2.