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REVIEW 4 major objections 5 minor 1 cited by

EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Adding motion embeddings sharpens street-scene Gaussian rendering

desk verdict A useful plug-and-play motion module for street Gaussian splatting, but the headline gains are partly confounded by an unablated change in the training recipe. read the letter →

arxiv 2411.15582 v2 pith:JFUI46KS submitted 2024-11-23 cs.CV

Xiaobao Wei , Qingpo Wuwu , Zhongyu Zhao , Zhuangzhe Wu , Nan Huang , Ming Lu , Ningning MA , Shanghang Zhang This is my paper · ORCID
classification cs.CV
keywords Gaussiansplattingstreetscenereconstructionself-supervisedlearningmotiondecompositionnovelviewsynthesisautonomousdrivingdeformationfield
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that street-scene Gaussian splatting reconstructions look blurry because existing methods treat dynamic objects as one binary class and ignore the continuous range of motion speeds: a pedestrian and a vehicle are modeled with the same deformation machinery. To fix this, it introduces Explicit Motion Decomposition (EMD), a plug-and-play module that attaches a learnable motion embedding to each Gaussian primitive and splits deformation into a coarse branch for large-scale motions and a fine branch for local details. Adding EMD to two self-supervised street-splatting baselines raises reconstruction quality on Waymo and KITTI benchmarks, and the same module extends to box-supervised pipelines by refining tracked poses and SMPL body parameters. If the claim holds, motion modeling can be improved without changing the underlying scene representation or requiring new annotations.

What carries the argument

The load-bearing object is the dual-scale deformation network combined with per-Gaussian learnable embeddings. Each Gaussian carries a latent vector $z_k \in \mathbb{R}^{32}$; a shared temporal embedding matrix $W$ is sampled through progressive bilinear interpolation so that early training sees coarse time samples and later training sees finer ones; the aggregated feature $\mathcal{F}_{aggr}(\mu,t) = [\mathcal{F}_{pos}(\mu), \mathcal{F}_{temp}(t), z_k]$ feeds a coarse multi-head decoder and a fine multi-head decoder in series. The coarse decoder handles large displacements such as vehicle translation, the fine decoder receives $\mu + \Delta\mu_{coarse}$ and handles local detail such as articulated motion. Ablations show removing the coarse branch drops full-image PSNR by 3.10 dB, while removing the fine branch worsens perceptual quality.

What would settle it

A direct test: give both deformation branches identical capacity and input features, or swap their order; if rendering quality stays essentially unchanged, the claimed coarse-fast versus fine-slow decomposition is not what drives the gains. A second check: read out the learned embedding $z_k$ for Gaussians on pedestrians versus vehicles and test whether the embedding space separates by measured object speed.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that explicit motion modeling is the missing ingredient in current street Gaussian splatting. The proposed EMD module gives each Gaussian a learnable embedding $z_k$ that encodes its individual motion characteristics, combines spatial, temporal, and per-Gaussian features into a motion-aware encoding, and predicts deformations in two stages: $\mathcal{D}_{coarse}$ for global, fast motions such as vehicle translation and $\mathcal{D}_{fine}$ for local, articulated deformation, with the final deformed position computed as $\mu_t = \mu + \Delta\mu_{coarse} + \Delta\mu_{fine}$. Integrated into S3Gaussian and DeSiRe-GS, EMD improves PSNR, SSIM, and LPIPS on Waymo and KITTI; integrated into StreetGaussian and OmniRe, it improves supervised novel-view synthesis. The paper further reports that EMD substantially lowers FID on novel trajectory synthesis, arguing that better motion modeling directly benefits the lane-change simulation use case.

Load-bearing premise

The argument assumes that the coarse/fine split actually separates fast global motion from slow local deformation and that the learned per-Gaussian embedding encodes motion speed; nothing in the training loss forces either property, since the loss only sums the two deformation outputs and encourages neighboring Gaussians to have similar embeddings.

Editorial extensions

If this is right

  • Any self-supervised street Gaussian splatting method can adopt EMD as a drop-in module and expect better reconstruction, especially on fast-moving vehicles.
  • Novel trajectory synthesis for lane-change simulation improves, with FID dropping from 83.48 to 45.11 at 0.5 m offset on Waymo.
  • Supervised pipelines with tracked boxes and SMPL bodies also benefit: StreetGaussian + EMD and OmniRe + EMD improve novel-view PSNR and SSIM.
  • The progressive temporal embedding schedule suggests a general coarse-to-fine training strategy for deformation networks, not limited to street scenes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the learned embeddings genuinely encode motion speed, they might transfer across scenes: an embedding space pretrained to separate vehicle-like from pedestrian-like motion could accelerate training on new street datasets.
  • The paper does not enforce that the coarse branch handles fast motion and the fine branch slow motion; a plausible alternative explanation for the gains is simply that two-stage deformation adds network capacity. Swapping or equalizing the two branches would test this.
  • The FID improvement on shifted trajectories implies that motion modeling quality can be measured without ground-truth novel views, which could make FID on shifted trajectories a standard evaluation for driving simulators.
  • The limitation section notes lighting is unmodeled; the same embedding-plus-dual-scale idea could naturally extend to lighting or weather-dependent appearance.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes Explicit Motion Decomposition (EMD), a module for street Gaussian Splatting that augments Gaussian primitives with per-Gaussian learnable motion embeddings, uses an adaptive temporal embedding matrix, and introduces a dual-scale deformation framework with coarse and fine decoders. EMD is integrated into self-supervised baselines S3Gaussian and DeSiRe-GS, and extended to supervised methods StreetGaussian and OmniRe. Experiments on Waymo and KITTI report improved scene reconstruction and novel view synthesis in several settings, along with novel trajectory synthesis evaluated by FID. The paper claims that EMD is a plug-and-play module that achieves state-of-the-art self-supervised novel view synthesis performance.

Significance. If the attribution were clean, the contribution would be practically valuable: a drop-in module that improves multiple existing street-Gaussian pipelines, with code release, comparisons across four base methods and two datasets, ablation studies, and parameter sensitivity analysis. The manuscript also introduces a useful evaluation component in novel trajectory synthesis. However, the central attribution is not established as reported, because the S3Gaussian+Ours comparison changes more than EMD, and the headline claim of state-of-the-art novel view synthesis is contradicted by the paper's own Table 1. These issues are fixable with matched baselines and corrected claims, so the work has potential but needs substantial revision.

major comments (4)
  1. [§4.2.1, Supplementary C.1, Tables 1–2] The central comparison between S3Gaussian and S3Gaussian+Ours does not isolate EMD. Supplementary C.1 states that the proposed method replaces the original SFM initialization with LiDAR-prior initialization and adds a sky supervision loss Lsky into the original S3Gaussian loss. Because these changes are applied only to the EMD-augmented model and not to the reported baseline, the +1.81 PSNR improvement in Table 1 (and the gains in Table 2) cannot be attributed to EMD. The authors should re-run the original S3Gaussian and DeSiRe-GS with the same LiDAR initialization, sky loss, and any other shared training-recipe changes, and then add EMD on top; this matched comparison is essential to support the claim that 'previous methods with EMD exhibit better reconstruction quality.'
  2. [§5.2.1, Table 1] The full-image novel view synthesis PSNR for S3Gaussian+Ours is 26.55, which is lower than S3Gaussian's 26.62, while the abstract and contributions claim state-of-the-art novel view synthesis performance in self-supervised settings. This directly undercuts the blanket NVS claim. The authors should either report an appropriate aggregate or scenario-specific metric that supports the claim, restrict the claim to scene reconstruction and selected object regions, or provide additional experiments on novel view synthesis where the improvement is consistent.
  3. [§4.1.2, Eq. (11), §4.2.1, Eq. (15), Table 6] The mechanism story that coarse deformation captures fast global motion while fine deformation captures slow local deformation, and that per-Gaussian embeddings encode motion speed, is not enforced or tested. Eq. (11) simply sums the two deformation outputs, and Eq. (15) only encourages neighboring Gaussians to have similar embeddings; no loss separates motion scales or ties embeddings to speed. The ablations in Table 6 show that removing the coarse branch causes a large drop and removing the fine branch hurts LPIPS, but this does not demonstrate specialization. The authors should add a direct test, such as measuring displacement magnitudes of coarse vs fine branches per object class or visualizing embedding clustering by speed, or else soften the claims to describe a general increase in deformation network capacity.
  4. [Tables 1–2, 4–6] Several reported improvements are small (e.g., DeSiRe-GS+Ours vs DeSiRe-GS is +0.54 PSNR on Waymo scene reconstruction, +0.16 on novel view synthesis, and +0.18/+0.17 on KITTI), and no error bars, per-scene standard deviations, or significance tests are provided. Given that the comparisons may include training-recipe confounds, the numerical claims are not yet robust; reporting per-scene variance across the small scene subsets would materially strengthen the paper.
minor comments (5)
  1. [Table 3] The table header contains a typo: 'StreetGaussin' should be 'StreetGaussian'.
  2. [Figure 6] The caption contains placeholder question marks ('?????') in place of variant descriptions; these should be replaced with explicit labels.
  3. [§5.1] The text says 'We also report FPS to access inference speed'; 'access' should be 'assess'.
  4. [Eq. (15)] The notation KNN_i;d is not defined precisely; the text says 'KNN means the k-nearest-neighbors algorithm' but does not clarify how the hyperparameter d in Eq. (15) relates to the number of neighbors k.
  5. [References] The reference labeled DeformGS [63] is cited in Table 4 but the bibliography entry describes 'Deformable 3D Gaussians'; please unify the name and citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the EMD module is evaluated empirically against external baselines and component ablations, and no fitted quantity is renamed as a prediction.

full rationale

EMD's derivation chain is empirical rather than analytic: the paper proposes a deformation module (Eq. 11 combines coarse and fine outputs; Eq. 15 regularizes per-Gaussian embeddings) and evaluates it by adding it to existing pipelines (S3Gaussian, DeSiRe-GS, StreetGaussian, OmniRe) and comparing against external baselines. No prediction in the paper is a fitted constant renamed as a result: the learned embeddings and deformation outputs are optimized on training views and then used for novel-view and novel-trajectory rendering, which is the standard empirical loop for scene representation networks. The only self-citation to the authors' own S3Gaussian is not load-bearing, because EMD is also validated on DeSiRe-GS and supervised baselines, and the central comparison is a module-level addition rather than an imported theorem. The supplementary change to LiDAR initialization and the added sky loss (Sec. C.1) is a potential experimental confound for attributing the S3Gaussian gains specifically to EMD, but it is not circular: those recipe changes do not encode EMD's outputs into its inputs, and the component ablations in Table 6 still remove EMD parts from an otherwise fixed full model. Similarly, the absence of a loss enforcing the coarse/fine speed split (Eq. 11 only sums the two outputs) is an interpretation risk, not a circular reduction. Overall, no step reduces by construction to its own input, so the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 3 invented entities

The central claim rests on standard deformable-Gaussian machinery plus hand-chosen hyperparameters; the most fragile assumption is the unenforced coarse and fine scale separation. The invented entities are internal representational devices without external falsifiable handles, so they do not add physical commitments.

free parameters (5)
  • Gaussian embedding dimension M = 32
    Hand-chosen latent dimension per Gaussian; Table 8 shows lower dimensions hurt PSNR and higher dimensions add parameters with no gain.
  • Temporal embedding dimension D = 4
    Hand-chosen width of the learnable temporal embedding matrix W; Table 8 shows D=1 hurts and D=16 adds 14.4M parameters with marginal effect.
  • Progressive temporal sampling schedule Nmin, Nmax, T = 30, 150, 25000
    Hand-chosen schedule in Eq. (10) for coarse-to-fine temporal embedding sampling.
  • KNN regularization radius d and weight lambda_w = 20, 2000
    Hand-chosen in Eq. (15) for local smoothness of Gaussian embeddings.
  • Loss coefficients = lambda_depth=0.5, lambda_feat=0.1, lambda_tv=0.1, lambda_sky=0.1, lambda_reg=0.01
    Hand-chosen weights for the combined training loss, listed in supplementary Table 7.
assumptions (4)
  • domain assumption Canonical-space Gaussians plus a deformation field can represent all dynamic street content.
    Standard deformable-Gaussian assumption; invoked in Eq. (5)-(6). Not verified for topology changes or articulated humans in self-supervised mode.
  • domain assumption Photometric and auxiliary losses are sufficient to learn meaningful per-Gaussian motion embeddings without masks or 3D boxes.
    Self-supervised setting has no dynamic supervision; Sec. C.1 defines the loss used to learn z_k and W.
  • ad hoc to paper Coarse and fine deformation decoders specialize to fast global motion and slow local deformation.
    Eq. (11) sums their outputs and no loss assigns motion scales; the specialization is asserted in Sec. 4.1.2 and assumed in the ablation discussion.
  • domain assumption LiDAR point clouds are available and accurate for initialization and depth supervision.
    Training scheme in supplementary C.1 relies on LiDAR initialization and depth loss; not applicable to camera-only datasets.
invented entities (3)
  • Per-Gaussian latent code z_k
    purpose: Captures individual motion characteristics of each Gaussian.
    Learned from reconstruction loss only; no external handle or direct measurement that it encodes motion speed.
  • Adaptive temporal embedding matrix W
    purpose: Provides time-dependent features through progressive grid sampling.
    Internal lookup table; no independent evidence its rows correspond to physical time or speed.
  • Dual-scale deformation split D_coarse and D_fine
    purpose: Separates large-scale global motion from small-scale local deformation.
    Architectural division without enforced scale separation; ablation shows coarse removal hurts most, but no direct measure of scale specialization.

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Cite this review

Pith. "Pith review of EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting." pith.science (2026). https://pith.science/paper/JFUI46KS

@misc{pith2026241115582,
  author       = {Pith},
  title        = {Pith review of: EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JFUI46KS}},
  note         = {Machine review of arXiv:2411.15582}
}
read the original abstract

Photorealistic reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. While recent methods based on 3D/4D Gaussian Splatting (GS) have demonstrated promising results, they still encounter challenges in complex street scenes due to the unpredictable motion of dynamic objects. Current methods typically decompose street scenes into static and dynamic objects, learning the Gaussians in either a supervised manner (e.g., w/ 3D bounding-box) or a self-supervised manner (e.g., w/o 3D bounding-box). However, these approaches do not effectively model the motions of dynamic objects (e.g., the motion speed of pedestrians is clearly different from that of vehicles), resulting in suboptimal scene decomposition. To address this, we propose Explicit Motion Decomposition (EMD), which models the motions of dynamic objects by introducing learnable motion embeddings to the Gaussians, enhancing the decomposition in street scenes. The proposed plug-and-play EMD module compensates for the lack of motion modeling in self-supervised street Gaussian splatting methods. We also introduce tailored training strategies to extend EMD to supervised approaches. Comprehensive experiments demonstrate the effectiveness of our method, achieving state-of-the-art novel view synthesis performance in self-supervised settings. The code is available at: https://qingpowuwu.github.io/emd.

Figures

Figures reproduced from arXiv: 2411.15582 by the authors.

Figure 1
Figure 1. Previous street Gaussian splatting methods find it challenging to accurately model the motion patterns of dynamic objects, which [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of our Explicit Motion Decomposition (EMD) framework. Given input Gaussian primitives, our method processes them [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Visualization comparison on the self-supervised setting between S3Gaussian and S3Gaussian+ours EMD. We also visualize the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Visualization for novel trajectory synthesis (0.5m offset). [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Qualitative ablation study results across three camera views from the Waymo dataset. (a) Our complete model achieves sharp and [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.