Volumetric video is rapidly emerging as a next-generation media format for immersive VR/AR applications, offering free-viewpoint rendering and unprecedented realism. While 3D Gaussian Splatting (3DGS) has recently demonstrated impressive rendering quality and real-time performance, existing dynamic extensions often struggle with long sequences due to the lack of efficient and codec-friendly compression schemes. In particular, current methods are not yet VR/AR-ready, as they fail to balance high-fidelity rendering, compact storage, and real-time decoding across heterogeneous platforms. To address these challenges, we propose Dynamic Gaussian Streams, a compact, video-compatible representation for real-time immersive playback. Given multi-view video inputs, we leverage the DualGS framework to reconstruct a temporally coherent 4D Gaussian sequence, introducing key modifications that directly optimize the 3D positions of dense skin Gaussians to improve compressibility and rendering quality. Each frame is converted into structured 2D maps, where key appearance attributes are compressed using per-channel codebooks with uint8 index maps. Hierarchical index reordering and Morton layout optimize spatial and temporal locality, ensuring compatibility with standard H.264 codecs. For spatial attributes like position, a lossless uint16 quantization preserves sub-pixel accuracy. Our system strikes a strong balance between compression and visual fidelity, enabling real-time decoding and immersive rendering on platforms including mobile devices, and XR headsets such as Apple Vision Pro.

Speculative decoding is an effective and lossless method for Large Language Model (LLM) inference acceleration. It employs a smaller model to generate a draft token sequence, which is then verified by the original base model. In multi-GPU systems, inference latency can be further reduced through tensor parallelism (TP), while the optimal TP size of the draft model is typically smaller than that of the base model, leading to GPU idling during the drafting stage. We observe that such inefficiency stems from the sequential execution of layers, which is seemingly natural but actually unnecessary. Therefore, we propose EasySpec, a layer-parallel speculation strategy that optimizes the efficiency of multi-GPU utilization. EasySpec breaks the inter-layer data dependencies in the draft model, enabling multiple layers to run simultaneously across multiple devices as ``fuzzy'' speculation. After each drafting-and-verification iteration, the draft model’s key-value cache is calibrated in a single forward pass, preventing long-term fuzzy-error accumulation at minimal additional latency. EasySpec is a training-free and plug-in method. We evaluated EasySpec on several mainstream open-source LLMs, using smaller versions of models from the same series as drafters. The results demonstrate that EasySpec can achieve a peak speedup of 4.17x compared to vanilla decoding, while preserving the original distributions of the base LLMs. Specifically, the drafting stage can be accelerated by up to 1.62x with a maximum speculation accuracy drop of only 7\%. The code is available at https://github.com/Yize-Wu/EasySpec.

Volumetric video is emerging as a key medium for digitizing the dynamic physical world, creating the virtual environments with six degrees of freedom to deliver immersive user experiences. However, robustly modeling general dynamic scenes, especially those involving topological changes while maintaining long-term tracking remains a fundamental challenge. In this paper, we present TaoGS, a novel topology-aware dynamic Gaussian representation that disentangles motion and appearance to support, both, long-range tracking and topological adaptation. We represent scene motion with a sparse set of motion Gaussians, which are continuously updated by a spatio-temporal tracker and photometric cues that detect structural variations across frames. To capture fine-grained texture, each motion Gaussian anchors and dynamically activates a set of local appearance Gaussians, which are non-rigidly warped to the current frame to provide strong initialization and significantly reduce training time. This activation mechanism enables efficient modeling of detailed textures and maintains temporal coherence, allowing high-fidelity rendering even under challenging scenarios such as changing clothes. To enable seamless integration into codec-based volumetric formats, we introduce a global Gaussian Lookup Table that records the lifespan of each Gaussian and organizes attributes into a lifespan-aware 2D layout. This structure aligns naturally with standard video codecs and supports up to 40× compression. TaoGS provides a unified, adaptive solution for scalable volumetric video under topological variation, capturing moments where “elegance in motion” and “Power in Stillness”— delivering immersive experiences that harmonize with the physical world. Project page: https://guochch.github.io/TaoGS/.

Volumetric video represents a transformative advancement in visual media, enabling users to freely navigate immersive virtual experiences and narrowing the gap between digital and real worlds. However, the need for extensive manual intervention to stabilize mesh sequences and the generation of excessively large assets in existing workflows impedes broader adoption. In this paper, we present a novel Gaussian-based approach, dubbed DualGS , for real-time and high-fidelity playback of complex human performance with excellent compression ratios. Our key idea in DualGS is to separately represent motion and appearance using the corresponding skin and joint Gaussians. Such an explicit disentanglement can significantly reduce motion redundancy and enhance temporal coherence. We begin by initializing the DualGS and anchoring skin Gaussians to joint Gaussians at the first frame. Subsequently, we employ a coarse-to-fine training strategy for frame-by-frame human performance modeling. It includes a coarse alignment phase for overall motion prediction as well as a fine-grained optimization for robust tracking and high-fidelity rendering. To integrate volumetric video seamlessly into VR environments, we efficiently compress motion using entropy encoding and appearance using codec compression coupled with a persistent codebook. Our approach achieves a compression ratio of up to 120 times, only requiring approximately 350KB of storage per frame. We demonstrate the efficacy of our representation through photo-realistic, free-view experiences on VR headsets, enabling users to immersively watch musicians in performance and feel the rhythm of the notes at the performers' fingertips. Project page: https://nowheretrix.github.io/DualGS/.