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arxiv: 2604.25330 · v1 · submitted 2026-04-28 · 📡 eess.IV

Recognition: unknown

Generalizable 3D Gaussian Splatting enabled Semantic Coding for Real-Time Immersive Video Communications

Dingxi Yang , Wenqi Guo , Yue Liu , Jungong Han , Zhijin Qin
Authors on Pith no claims yet

Pith reviewed 2026-05-07 14:22 UTC · model grok-4.3

classification 📡 eess.IV
keywords 3D Gaussian Splattingsemantic codingimmersive videoreal-time communicationsdisparity-guided codeccross-domain generalizationend-to-end framework
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The pith

An end-to-end network fuses semantic coding and 3D Gaussian Splatting to process stereo video directly into renderable 3D scenes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces GS-SCNet, a unified framework that performs semantic video coding and generalizable 3D Gaussian Splatting reconstruction in a single trainable pipeline. A disparity-guided codec exploits stereo geometric priors to enable parallel cross-view processing and better compression. A lightweight predictor then maps the resulting semantic features straight into 3D Gaussian attributes, skipping any pixel-level reconstruction step. This joint design extracts geometric information only once, which the authors show yields improved rate-distortion performance, real-time speed, and the ability to handle previously unseen real-world scenes.

Core claim

GS-SCNet is an end-to-end architecture in which a Disparity-Guided Parallel Semantic Codec processes stereo streams by using epipolar priors for disparity compensation and semantic fusion, while a Lightweight Gaussian Parameter Predictor converts the decoded semantic latents directly into 3D Gaussian attributes; by training the two components together the system captures geometric correlations only once and thereby removes the redundant pixel reconstruction stage present in conventional decoupled pipelines.

What carries the argument

The Disparity-Guided Parallel Semantic Codec together with the Lightweight Gaussian Parameter Predictor, which together project semantic latents directly into 3D Gaussian attributes.

If this is right

  • Stereo video streams can be encoded and rendered in parallel at real-time rates.
  • Rate-distortion performance improves relative to any pipeline that decodes to pixels before 3D reconstruction.
  • The same trained model generalizes to new real-world human scenes without retraining.
  • The framework remains robust when the input has already suffered compression artifacts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Semantic latents may function as a shared intermediate representation usable by both compression and neural rendering tasks.
  • The same direct-mapping idea could be tested on other 3D representations such as neural radiance fields.
  • The approach may scale to wider camera baselines or additional views if the disparity guidance is extended accordingly.

Load-bearing premise

End-to-end training of the codec and predictor will reliably extract geometric correlations without any need for intermediate pixel reconstruction.

What would settle it

Training the codec and the Gaussian predictor separately on identical data, then measuring whether the joint model still produces measurably lower bitrates or higher rendering quality on out-of-domain real-world sequences.

Figures

Figures reproduced from arXiv: 2604.25330 by Dingxi Yang, Jungong Han, Wenqi Guo, Yue Liu, Zhijin Qin.

Figure 1
Figure 1. Figure 1: Overall performance comparison between our proposed end￾to-end GS-SCNet and state-of-the-art decoupled paradigms. Compared to traditional separated pipelines (e.g., DCVC-DC combined with GPS-Gaussian or EVA-Gaussian), our method significantly outperforms them by achieving higher novel-view rendering quality at lower transmission bitrates, while simultaneously delivering superior real-time inference speed (… view at source ↗
Figure 2
Figure 2. Figure 2: Framework of the proposed GS-SCNet. Taking cross-view (left and right) video streams as input, our model employs two fully parallel, weight-shared branches. At the Encoder, each frame undergoes feature extraction and disparity estimation. The estimated disparities are then compressed by the Cross-view Disparity Encoder. Guided by the locally decoded disparities, the semantic features of the two views under… view at source ↗
Figure 3
Figure 3. Figure 3: Framework of the proposed Cross-View Parallel Image Semantic Feature Autoencoder. DepthConv, Q, AE and AD represent depthwise convolution block, quantization, arithmetic encoder and decoder, respectively. p(yˆ V t |zˆ V t , c V t ) = Y i N view at source ↗
Figure 4
Figure 4. Figure 4: The detailed structure of the Gaussian parameter predictor. DConv, PS and Up represent depth-wise convolution block, pixel shuffle and bilinear upsampling, respectively. A linear transformation T from disparity to depth is applied to the decoded disparity features ˆf V t to obtain the depth semantic features dˆV t . The features dˆV t are concatenated with the decoded image semantic features Fˆ V t to pred… view at source ↗
Figure 5
Figure 5. Figure 5: RRD curves in terms of PSNR and SSIM on the 4D-DRESS [43] and X-Humans [44] datasets. view at source ↗
Figure 6
Figure 6. Figure 6: Rendering quality comparison on 4D-DRESS dataset. view at source ↗
Figure 8
Figure 8. Figure 8: RRD curves in terms of LPIPS on the 4D-DRESS dataset. view at source ↗
Figure 9
Figure 9. Figure 9: RRD curves in terms of PSNR, SSIM and LPIPS on the DNA-Rendering [18] datasets. view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative comparison of novel view synthesis on the real-world view at source ↗
Figure 11
Figure 11. Figure 11: Impact of the cross-view parallel semantic feature autoencoder and model optimization strategies on rate–distortion performance. view at source ↗
read the original abstract

Real-time immersive video communications, particularly high-fidelity 3D telepresence, necessitates a synergistic balance between instantaneous dynamic scene reconstruction and high-efficiency data transmission. While recent advancements in feed-forward 3D Gaussian Splatting (3DGS) have enabled real-time rendering, performing multi-view video coding and 3D reconstruction in a decoupled manner leads to suboptimal compression efficiency and high computational complexity. To address this, we propose GS-SCNet, the first unified end-to-end framework that seamlessly integrates generalizable 3DGS reconstruction with a dedicated deep Semantic Coding pipeline. Our architecture is underpinned by two core technical contributions: (i) we introduce a Disparity-Guided Parallel Semantic Codec that exploits epipolar geometric priors to facilitate cross-view contextual interaction via disparity compensation and semantic fusion, thereby enabling real-time parallel processing of stereo streams while significantly enhancing rate-distortion performance, and (ii) we develop a Lightweight Gaussian Parameter Predictor which directly projects decoded semantic latents into 3DGS attributes, obviating the need for intermediate pixel-domain reconstruction. By coupling the codec with the task-specific predictor, our framework extracts geometric correlations only once, effectively eliminating the redundant computational bottleneck inherent in conventional decoupled paradigms. Extensive evaluations on both synthetic and real-world human datasets demonstrate that GS-SCNet achieves a superior trade-off across compression efficiency, rendering quality, and real-time performance. Notably, our framework exhibits strong cross-domain generalization and robustness against compression artifacts when applied to out-of-domain real-world data, significantly outperforming conventional decoupled transmission paradigms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes GS-SCNet, the first unified end-to-end framework integrating generalizable 3D Gaussian Splatting reconstruction with a deep semantic coding pipeline for real-time immersive video communications. It introduces two main components: (i) a Disparity-Guided Parallel Semantic Codec that exploits epipolar geometric priors for cross-view contextual interaction via disparity compensation and semantic fusion, enabling parallel stereo stream processing, and (ii) a Lightweight Gaussian Parameter Predictor that directly maps decoded semantic latents to 3DGS attributes (position, covariance, opacity, SH coefficients) without intermediate pixel-domain reconstruction. The authors claim this coupling extracts geometric correlations only once, yielding superior rate-distortion performance, rendering quality, real-time capability, and strong cross-domain generalization on synthetic and real-world human datasets compared to conventional decoupled transmission paradigms.

Significance. If the empirical claims hold, the work offers a meaningful advance for real-time 3D telepresence by unifying semantic coding and feed-forward 3DGS reconstruction, potentially reducing redundant computation and transmission overhead in immersive communications. The end-to-end design that avoids pixel reconstruction while preserving geometric fidelity through latent-space priors is a conceptually attractive direction, though its practical impact depends on verifiable gains in compression efficiency and robustness.

major comments (3)
  1. [Abstract and §4] Abstract and §4 (Experiments): The central claims of 'superior trade-off across compression efficiency, rendering quality, and real-time performance' plus 'strong cross-domain generalization and robustness against compression artifacts' on out-of-domain real-world data are stated without any reported quantitative metrics (e.g., PSNR, SSIM, bitrate savings, runtime), baselines, or error analysis in the abstract and lack explicit cross-references to supporting tables or figures in the evaluation section, making it impossible to assess whether the data supports the end-to-end advantage over decoupled pipelines.
  2. [§3.2] §3.2 (Lightweight Gaussian Parameter Predictor): The claim that the predictor 'directly projects decoded semantic latents into 3DGS attributes, obviating the need for intermediate pixel-domain reconstruction' is load-bearing for the efficiency and generalization assertions, yet the section provides no analysis or ablation showing how high-frequency disparity details survive the rate-distortion optimized latent bottleneck (especially under compression on out-of-domain data); if such details are lost, the no-reconstruction advantage collapses relative to a decoupled pipeline that reconstructs pixels first.
  3. [§3.1] §3.1 (Disparity-Guided Parallel Semantic Codec): The architecture is described as embedding 'epipolar geometric priors' to enable semantic fusion and cross-view interaction, but no equations or diagrams detail the disparity compensation mechanism or prove that all necessary geometric correlations are preserved in the latent space for the downstream predictor; this leaves the 'extracts geometric correlations only once' claim unverified and risks circularity with the generalization results.
minor comments (2)
  1. [§3] Notation for semantic latents and disparity maps is introduced without a consolidated table of symbols, which would improve readability in the method sections.
  2. [Figures 2 and 5] Figure captions for the overall architecture and qualitative results could more explicitly label the data flow from codec latents to 3DGS attributes.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and insightful comments on our manuscript. We address each major comment point by point below and outline the revisions we will make to strengthen the presentation and supporting analysis.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experiments): The central claims of 'superior trade-off across compression efficiency, rendering quality, and real-time performance' plus 'strong cross-domain generalization and robustness against compression artifacts' on out-of-domain real-world data are stated without any reported quantitative metrics (e.g., PSNR, SSIM, bitrate savings, runtime), baselines, or error analysis in the abstract and lack explicit cross-references to supporting tables or figures in the evaluation section, making it impossible to assess whether the data supports the end-to-end advantage over decoupled pipelines.

    Authors: We agree that adding explicit cross-references will improve readability and verifiability. While abstracts conventionally remain high-level and avoid dense numerical reporting, we will revise the abstract to include targeted references (e.g., 'as demonstrated in Tables 1–3 and Figures 4–6 of Section 4'). All quantitative metrics (PSNR, SSIM, bitrate savings, runtime), baseline comparisons, and error analysis for cross-domain generalization on out-of-domain real-world data are already reported in full in Section 4; the revision simply makes these links explicit in the abstract and introduction. revision: yes

  2. Referee: [§3.2] §3.2 (Lightweight Gaussian Parameter Predictor): The claim that the predictor 'directly projects decoded semantic latents into 3DGS attributes, obviating the need for intermediate pixel-domain reconstruction' is load-bearing for the efficiency and generalization assertions, yet the section provides no analysis or ablation showing how high-frequency disparity details survive the rate-distortion optimized latent bottleneck (especially under compression on out-of-domain data); if such details are lost, the no-reconstruction advantage collapses relative to a decoupled pipeline that reconstructs pixels first.

    Authors: This observation is fair and highlights a point where additional evidence would strengthen the manuscript. The end-to-end rate-distortion optimization is intended to retain task-relevant geometric information, but we acknowledge the absence of targeted ablation. In the revised version we will add an ablation study (new subsection in §3.2 plus supplementary figures) that measures disparity detail preservation (via edge sharpness and depth error metrics) across compression rates on both in-domain and out-of-domain data, directly comparing the direct-prediction path against pixel-reconstruction baselines. These results will quantify that high-frequency details critical for 3DGS attributes are retained sufficiently to preserve the claimed efficiency advantage. revision: yes

  3. Referee: [§3.1] §3.1 (Disparity-Guided Parallel Semantic Codec): The architecture is described as embedding 'epipolar geometric priors' to enable semantic fusion and cross-view interaction, but no equations or diagrams detail the disparity compensation mechanism or prove that all necessary geometric correlations are preserved in the latent space for the downstream predictor; this leaves the 'extracts geometric correlations only once' claim unverified and risks circularity with the generalization results.

    Authors: We appreciate the call for greater technical transparency. In the revised manuscript we will insert the explicit equations governing disparity compensation and semantic fusion (including the epipolar prior integration and cross-view attention formulation) directly into §3.1. A new architectural diagram will also be added to illustrate the latent-space flow. A short paragraph will discuss how the single extraction of geometric correlations in the latent domain supports downstream prediction, thereby removing any potential circularity with the empirical generalization results. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper proposes GS-SCNet as a novel end-to-end architecture with two explicitly designed components: a Disparity-Guided Parallel Semantic Codec that incorporates epipolar priors for cross-view fusion, and a Lightweight Gaussian Parameter Predictor that maps decoded latents directly to 3DGS attributes. All performance claims, including superior rate-distortion trade-offs, real-time capability, cross-domain generalization, and robustness on out-of-domain data, are presented as outcomes of extensive empirical evaluations on synthetic and real-world human datasets. No load-bearing step equates a prediction or result to its own fitted inputs by construction, nor does any central premise reduce to a self-citation chain or renamed ansatz; the architecture is described as an independent design whose benefits are validated externally rather than tautologically derived.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the framework is described at architectural level without mathematical derivations or fitted constants.

pith-pipeline@v0.9.0 · 5589 in / 1146 out tokens · 63214 ms · 2026-05-07T14:22:35.788810+00:00 · methodology

discussion (0)

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Reference graph

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