Title: 4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming
Venue: NeurIPS 2025
Accepted: yes
Keywords: Volumetric Video

Dynamic 3DGS

Progressive Streaming

4D Reconstruction

Progressive Compression
Abstract: Achieving seamless viewing of high-fidelity volumetric video, comparable to 2D video experiences, remains an open challenge. Existing volumetric video compression methods either lack the flexibility to adjust quality and bitrate within a single model for efficient streaming across diverse networks and devices, or struggle with real-time decoding and rendering on lightweight mobile platforms. To address these challenges, we introduce 4DGCPro, a novel hierarchical 4D Gaussian compression framework that facilitates real-time mobile decoding and high-quality rendering via progressive volumetric video streaming in a single bitstream. Specifically, we propose a perceptually-weighted and compression-friendly hierarchical 4D Gaussian representation with motion-aware adaptive grouping to reduce temporal redundancy, preserve coherence, and enable scalable multi-level detail streaming. Furthermore, we present an end-to-end entropy-optimized training scheme, which incorporates layer-wise rate-distortion (RD) supervision and attribute-specific entropy modeling for efficient bitstream generation. Extensive experiments show that 4DGCPro enables flexible quality and variable bitrate within a single model, achieving real-time decoding and rendering on mobile devices while outperforming existing methods in RD performance across multiple datasets.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents 4DGCPro, a novel compression framework for volumetric video streaming that addresses several key challenges in the field. The work tackles the persistent issues of inflexible bitrate adaptation and computational constraints that have limited the practical deployment of high-quality volumetric video on mobile platforms. The proposed solution introduces a hierarchical 4D Gaussian representation combined with motion-aware adaptive grouping, which effectively reduces temporal redundancy while maintaining visual coherence. A significant contribution is the end-to-end entropy-optimized training scheme that incorporates both rate-distortion supervision and attribute-specific entropy modeling. The framework's ability to support quality and bitrate adaptation within a single model represents an important advancement toward making volumetric video more practical for real-world applications.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\n1.This paper presents the first hierarchical 4D Gaussian compression approach for progressive volumetric video streaming, with a well-designed architecture that adapts to the heterogeneous capabilities of client devices. It enables adaptive rendering quality selection, ensuring practical applicability in real-world streaming scenarios.\n\n2.The paper uses adaptive grouping to handle topological changes and long-term dynamics, ensuring compact and consistent temporal representation. Layer-wise RD supervision and attribute-specific entropy modeling optimize volumetric video compression for better storage and transmission.\n\n3.The paper provides a thorough evaluation of 4DGCPro, demonstrating its flexibility in supporting variable bitrate and quality within a single model. The method enables real-time decoding and rendering on mobile devices and outperforms existing approaches in RD performance across multiple datasets, showcasing its efficiency and scalability.\n\nWeaknesses:\n\n1.Unlike methods such as 3DGStream, which employ Gaussian compensation for new areas, 4DGCPro lacks dedicated mechanisms to handle emerging content. This could potentially affect the reconstruction fidelity in dynamic scenes with previously unseen regions.\n\n2.It would be beneficial if the authors could include ablation studies on the QP setting. This would provide additional insights into its influence on the overall performance and help strengthen the evaluation of the method.\n\nQUESTIONS:\n1. Why was H.264 chosen over more efficient standards (e.g., H.265) or learning-based compression methods for encoding the Gaussian attributes?\n\n2. During training, rotation attributes are included in the entropy estimation for keyframes, but are excluded for inter-frames. What is the technical rationale behind this design decision?\n\n3. For the 4K HiFi4G dataset, did 4DGCPro train on the original high-resolution dataset, or was downsampling applied beforehand?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a hierarchical compression approach for progressive volumetric video streaming using the 3D Gaussian Splatting (3DGS) representation. The proposed residual-based representation and adaptive grouping strategy are interesting and could provide useful insights for future research in this area. The experimental results demonstrate its superiority compared with other methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. The proposed residual-based representation and adaptive grouping strategy are interesting and show promise for progressive streaming.\n2. The decision to directly apply an off-the-shelf H.264 codec for compression, rather than relying fully on learned entropy coding, is another interesting design choice. This makes the system more compatible with existing hardware and supports faster decoding.\n\nWeakness:\n1. Clarity is a major concern, and several parts require more detailed explanation:\n(1) In Equation 3, what exactly does “spatial volume $\\mathcal{S}$” refer to?\n(2) Why is Equation 3 formulated as a weighted sum, with $\\lambda_\\Phi$ as a trade-off parameter? The motivation and implications of this formulation are not sufficiently discussed.\n(3) The paper lacks discussion on how Gaussians are partitioned into $L$ layers. Is it done uniformly or based on specific criteria?\n2. Experimental concerns: \n(1) A key ablation study is missing: the case without residual modeling (i.e., equivalent to setting Group=1). This should be included, for example, in Table 4.\n (2) The paper does not compare with QUEEN [A], which is a relevant baseline for compression of Volumetric Video Streaming.\n3. Inconsistency in claims: In line 161, the paper claims the method “… effectively captures large-scale complex motions …”, yet in the limitations section (line 315), it says the method “underperforms in large-scale scenes.” This appears contradictory. A clear analysis is needed to explain why the method performs well or poorly in large-scale scenes, better supported by visual results.\n4. In Figure 4, the qualitative image for 3DGStream appears to be from a different timestamp than the other methods, making the comparison less meaningful.\n5. Minor issues:\n(1) Line 147: duplicated word “are”s.\n(2) Line 202: “a” should be “an”.\n(3) Line 220 & Equation (9): Please unify the terminology—should it be PDF or PMF?\n\n[A] Girish S, Li T, Mazumdar A, et al. Queen: Quantized efficient encoding of dynamic gaussians for streaming free-viewpoint videos[J]. Advances in Neural Information Processing Systems, 2024, 37: 43435-43467.\n\nQUESTIONS:\n1. It would be better to illustrate equation 3 with more context.\n2. Incorporating the suggested baseline will strengthen the paper.\n3. Providing a more in-depth ablation study on the weakness part will enhance the paper quality.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents 4DGCPro, a compact 4D Gaussian representation that requires only a small storage size for streaming volumetric videos. To achieve this, it models the rigid transformation of Gaussians in the key frames to define translation and rotation of Gaussians for subsequent frames. Moreover, it also models temporal residuals for remaining attributes, such as scale, opacity, and color. To minimize redundancy in these residuals, it conducts entropy-optimized training for each attribute. Furthermore, it can effectively represent the deformation by grouping frames based on the magnitude of the Gaussian translation. Experimental results demonstrate that the proposed method achieves superior rendering quality with minimal storage usage compared to existing efficient 4D Gaussian approaches.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n- The proposed motion-aware adaptive grouping enables dividing entire frames into optimal sub-groups for varying scenes.\n- Experimental results show that 4DGCPro outperforms the rendering quality of V$^3$, SOTA 4D Gaussian representation while the high-bitrate variant of this method requires a smaller storage size than V$^3$.\n\n---\n\n### Weaknesses\n\n- This method is closely similar to V$^3$ which introduces the definition of hash grid-based Gaussian translation, attributes residual modeling, and a grouping strategy for input frames.\n\n- While the authors propose a hierarchical representation, they do not provide any details for optimization and analysis on the hierarchical structure.\n\n- The papers need to improve the overall writing.\n\nQUESTIONS:\n- Although this work highlights a hierarchical representation of 4D Gaussians, this paper does not provide detailed optimization schemes, such as initialization and different supervision for each level, and experiments for validating the effectiveness of the hierarchical structure with varying numbers of levels. Please provide more details for hierarchical representation with analysis on the results.\n\n- The authors have investigated that the proposed significance score enhances the rendering quality. However, there is no comparison with existing importance scores introduced by existing compact 3D Gaussian approaches, LightGaussian [NeurIPS'24], EAGLES [ECCV'24], Mini-Splatting [ECCV'24], and Taiming-3DGS [SIGGRAPH Asia'24].\n\n- Moreover, what is the detailed definition of spatial volume in L145?\n\n- As mentioned in the weaknesses, this framework is closely similar to V$^3$. Can you provide technical contributions different from V$^3$ for better understanding? \n\n- To validate the effectiveness of motion-aware adaptive grouping, it could be helpful to provide an ablation study on the adaptive grouping strategy.\n\n- Minor typo in L147: are are\n\nLIMITATIONS:\n- Despite the superior performance, this framework only shows limited technical advancements compared to V$^3$.\n\n- Also, more detailed experiments and analysis need to be added to validate the effectiveness of the proposed method.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces 4DGCPro, a 4D Gaussian compression framework designed for progressive volumetric video streaming. Specifically, the proposed method comprises three key components: (1) a perceptually-weighted; (2) a motion-aware adaptive grouping strategy; and (3) a joint entropy-optimized training scheme. Experimental results demonstrate that 4DGCPro outperforms the included baselines rate-distortion performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThis paper integrates several established techniques from learned video compression into the domain of 4D Gaussian compression. Notably, it incorporates intra-frame and inter-frame coding, as well as residual motion coding, into a unified framework tailored for volumetric video.\n\nWeaknesses:\n- The proposed ideas appear somewhat incremental, as intra-frame and inter-frame coding are standard practices, and progressive layer coding (also known as scalable coding) is well-established. Additionally, the \"motion-aware adaptive Gaussian grouping\" resembles common strategies in video compression that employ intra-frame coding during scheme changes. It would be helpful if the authors could elaborate more on what specific novel designs or adaptations they introduce for 4D Gaussian compression in this work to clarify its distinct contributions.\n- The authors describe the framework as a variable-rate model. In learned image and video compression, a variable-rate model typically means the ability to encode inputs at arbitrary target bitrates within a continuous range. However, in this work, the multiple bitrates seem to arise solely from discrete multi-layer coding. In other words, the model supports only a few fixed rates rather than truly continuous variability. Therefore, the use of the term \"variable rate\" may need reconsideration.\n- Details regarding progressive rendering are limited. \n- The term “interp” in Equation 5 should be defined.\n- In traditional scalable coding, although multi-rate options exist, coding efficiency is often lower than single-layer coding because information may be redundantly transmitted across layers. This paper does not report a comparison between single-layer and multi-layer coding results, which is important for understanding trade-offs.\n- Table 3 reports complexity measurements, but the hardware or platform used for these measurements is not specified in the main paper and should be included.\n- The paper employs H.264 as a video encoder, but lacks details on its configuration, such as whether P-frames or B-frames are used, the number of reference frames, or whether YUV or RGB color coding is applied.\n\nQUESTIONS:\n- The proposed ideas appear somewhat incremental, as intra-frame and inter-frame coding are standard practices, and progressive layer coding (also known as scalable coding) is well-established. Additionally, the \"motion-aware adaptive Gaussian grouping\" resembles common strategies in video compression that employ intra-frame coding during scheme changes. It would be helpful if the authors could elaborate more on what specific novel designs or adaptations they introduce for 4D Gaussian compression in this work to clarify its distinct contributions.\n\n- The authors describe the framework as a variable-rate model. In learned image and video compression, a variable-rate model typically means the ability to encode inputs at arbitrary target bitrates within a continuous range. However, in this work, the multiple bitrates seem to arise solely from discrete multi-layer coding. In other words, the model supports only a few fixed rates rather than truly continuous variability. Therefore, the use of the term \"variable rate\" may need reconsideration.\n\n- In traditional scalable coding, although multi-rate options exist, coding efficiency is often lower than single-layer coding because information may be redundantly transmitted across layers. This paper does not report a comparison between single-layer and multi-layer coding results, which is important for understanding trade-offs.\n\n- The paper employs H.264 as a video encoder, but lacks details on its configuration, such as whether P-frames or B-frames are used, the number of reference frames, or whether YUV or RGB color coding is applied.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}