PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming
- URL: http://arxiv.org/abs/2605.11427v1
- Date: Tue, 12 May 2026 02:26:10 GMT
- Title: PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming
- Abstract summary: We present PD-4DGS, the first framework for progressive compression and on-demand transmission of 4DGS.<n>On the Dycheck iPhone benchmark, PD-4DGS cuts the streamed bitstream by >60% at matched rendering fidelity.
- Score: 20.021373872364897
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must download in full before any frame can be rendered, causing black-screen waits of tens to hundreds of seconds on mobile bandwidth and leaving 4DGS incompatible with modern adaptive-bitrate delivery. Progressive 3DGS compression alleviates this for static scenes, but it acts only on spatial anchors and cannot partition the temporal deformation networks that dominate dynamic-scene size. We present PD-4DGS, the first framework for progressive compression and on-demand transmission of 4DGS. Hierarchical Deformation Decomposition (HDD) externalises the coarse-to-fine motion hierarchy already latent in 4DGS into three independently transmittable layers -- a static scaffold, a global deformation, and a local refinement -- so that any prefix of the bitstream is already renderable, turning a single training run into a scalable, DASH/HLS-compatible bitstream. A Gaussian-entropy attribute rate-distortion loss together with a temporal mask consistency regulariser shrink the base layer while suppressing low-bitrate flicker; a capacity-weighted rollout schedule, gated online by a learnt activation rate rho, then prevents deformation-network under-training without any per-scene hyperparameter. On the Dycheck iPhone benchmark, PD-4DGS cuts the streamed bitstream by >60% at matched rendering fidelity and reduces first-frame latency from 73--930 s to ~1.7 s on a 2 Mbps link, uniquely enabling true on-demand progressive streaming for 4DGS.
Related papers
- Inst4DGS: Instance-Decomposed 4D Gaussian Splatting with Multi-Video Label Permutation Learning [52.867634524554795]
Inst4DGS is an instance-decomposed 4D Gaussian Splatting (4DGS) approach with long-horizon per-Gaussian trajectories.<n>We introduce per-video label-permutation latents that learn cross-video instance matches through a differentiable Sinkhorn layer.<n>This explicit label alignment yields sharp decision boundaries and temporally stable identities without identity drift.
arXiv Detail & Related papers (2026-03-19T01:49:20Z) - AirGS: Real-Time 4D Gaussian Streaming for Free-Viewpoint Video Experiences [8.988150010963382]
Free-viewpoint video (FVV) enables immersive viewing experiences by allowing users to view scenes from arbitrary perspectives.<n>We present AirGS, a streaming-optimized 4DGS framework that rearchitects the training and delivery to enable high-quality, low-latency experiences.
arXiv Detail & Related papers (2025-12-24T04:57:30Z) - TED-4DGS: Temporally Activated and Embedding-based Deformation for 4DGS Compression [14.026420167067117]
We present TED-4DGS, a temporally activated and embedding-based deformation scheme for rate-distortion-optimized 4DGS compression.<n>Our scheme achieves state-of-the-art rate-distortion performance on several real-world datasets.
arXiv Detail & Related papers (2025-12-05T05:46:35Z) - Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models [79.06910348413861]
We introduce Diff4Splat, a feed-forward method that synthesizes controllable and explicit 4D scenes from a single image.<n>Given a single input image, a camera trajectory, and an optional text prompt, Diff4Splat directly predicts a deformable 3D Gaussian field that encodes appearance, geometry, and motion.
arXiv Detail & Related papers (2025-11-01T11:16:25Z) - RAPID^3: Tri-Level Reinforced Acceleration Policies for Diffusion Transformer [86.57077884971478]
Diffusion Transformers (DiTs) excel at visual generation yet remain hampered by slow sampling.<n>We introduce RAPID3: Tri-Level Reinforced Acceleration Policies for Diffusion Transformers.<n>It delivers image-wise acceleration with zero updates to the base generator.<n>It achieves nearly 3x faster sampling with competitive generation quality.
arXiv Detail & Related papers (2025-09-26T13:20:52Z) - 4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming [52.76837132019501]
We introduce 4DGCPro, a novel hierarchical 4D compression framework.<n>4DGCPro facilitates real-time mobile decoding and high-quality rendering via progressive volumetric video streaming.<n>We present an end-to-end entropy-optimized training scheme.
arXiv Detail & Related papers (2025-09-22T08:38:17Z) - D-FCGS: Feedforward Compression of Dynamic Gaussian Splatting for Free-Viewpoint Videos [12.24209693552492]
Free-viewpoint video (FVV) enables immersive 3D experiences, but efficient compression of dynamic 3D representations remains a major challenge.<n>This paper presents Feedforward Compression of Dynamic Gaussian Splatting (D-FCGS), a novel feedforward framework for compressing temporally correlated Gaussian point cloud sequences.<n> Experiments show that it matches the rate-distortion performance of optimization-based methods, achieving over 40 times compression in under 2 seconds.
arXiv Detail & Related papers (2025-07-08T10:39:32Z) - Speedy Deformable 3D Gaussian Splatting: Fast Rendering and Compression of Dynamic Scenes [57.69608119350651]
Recent extensions of 3D Gaussian Splatting (3DGS) to dynamic scenes achieve high-quality novel view synthesis by using neural networks to predict the time-varying deformation of each Gaussian.<n>However, performing per-Gaussian neural inference at every frame poses a significant bottleneck, limiting rendering speed and increasing memory and compute requirements.<n>We present Speedy Deformable 3D Gaussian Splatting (SpeeDe3DGS), a general pipeline for accelerating the rendering speed of dynamic 3DGS and 4DGS representations by reducing neural inference through two complementary techniques.
arXiv Detail & Related papers (2025-06-09T16:30:48Z) - 4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video [56.04182926886754]
3D Gaussian Splatting (3DGS) has substantial potential for enabling photorealistic Free-Viewpoint Video (FVV) experiences.<n>Existing methods typically handle dynamic 3DGS representation and compression separately, motion information and the rate-distortion trade-off during training.<n>We propose 4DGC, a rate-aware 4D Gaussian compression framework that significantly reduces storage size while maintaining superior RD performance for FVV.
arXiv Detail & Related papers (2025-03-24T08:05:27Z) - Light4GS: Lightweight Compact 4D Gaussian Splatting Generation via Context Model [21.375070073632944]
3D view synthesisting (3DGS) has emerged as an efficient high-fidelity paradigm for novel and novel content.<n>To adapt 3DGS for dynamic content, deformable 3DGS incorporates temporally deformable primitives with learnable latent embeddings to capture complex motions.<n>Despite its impressive performance, the high-dimensional embeddings and vast number of primitives lead to substantial storage requirements.
arXiv Detail & Related papers (2025-03-18T06:28:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.