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arxiv: 2606.26754 · v1 · pith:S4ZU7GGMnew · submitted 2026-06-25 · 💻 cs.CV

Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting

Zhihao Wen , Yixin Yang , Bojian Wu , Yang Zhou , Dani Lischinski , Daniel Cohen-Or , Hui Huang This is my paper

Pith reviewed 2026-06-26 05:19 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingmulti-view stylizationoptimal transportstyle transfercapacity constraintscross-view consistencyneural rendering3D scene editing
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The pith

Reformulating local style matching as a semi-balanced optimal transport problem with explicit column-capacity constraints enables controllable and consistent multi-view stylization of 3D Gaussian Splatting.

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

The paper establishes a capacity-controlled framework for stylizing 3D Gaussian Splatting scenes across multiple views. It reformulates style feature matching as a semi-balanced optimal transport problem to avoid many-to-one assignments common in per-view methods. Explicit constraints on column capacities allow tunable control over how style features are distributed. This setup, plus cross-view guidance and geometric regularizations, aims to produce consistent stylizations that preserve scene semantics. A sympathetic reader would care because it tackles a key barrier to practical 3D content creation with artistic styles.

Core claim

We propose a capacity-controlled framework for multi-view stylization of 3DGS, grounded in optimal transport. Specifically, we reformulate local style matching as a semi-balanced optimal transport problem. By introducing explicit column-capacity constraints with tunable strength, our formulation mitigates many-to-one matching and enables controllable allocation of style features. This transport-based objective provides a principled mechanism for balancing feature coverage and stylistic diversity while maintaining stable correspondences across viewpoints. To further enhance cross-view coherence, we incorporate a novel cross-view matching guidance to constrain correspondences between scene con

What carries the argument

Semi-balanced optimal transport problem with explicit tunable column-capacity constraints, which controls allocation of style features to scene points.

If this is right

  • Mitigates many-to-one matching and enables controllable allocation of style features.
  • Balances feature coverage and stylistic diversity while maintaining stable cross-view correspondences.
  • Enhances cross-view coherence through novel matching guidance between scene content and style patterns.
  • Allows optimized Gaussian primitives to represent finer-grained textures via added geometric regularizations.
  • Produces stable, expressive 3D stylizations that preserve the core semantic structure of the scene.

Where Pith is reading between the lines

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

  • The tunable capacity strength could be adapted as a user control for trading off between stylistic fidelity and scene preservation in editing tools.
  • The transport formulation might reduce temporal flickering if extended to time-varying 3D content such as animated scenes.
  • Cross-view guidance could generalize to other explicit 3D representations that suffer from view-dependent style drift.

Load-bearing premise

That reformulating style matching as a semi-balanced optimal transport problem with explicit column-capacity constraints will inherently provide a principled mechanism for balancing feature coverage, stylistic diversity, and stable cross-view correspondences without introducing new inconsistencies or artifacts.

What would settle it

A side-by-side comparison of rendered stylized views from multiple angles that measures whether the frequency of many-to-one feature reuse drops and cross-view style consistency scores rise compared to independent per-view feature-matching baselines.

Figures

Figures reproduced from arXiv: 2606.26754 by Bojian Wu, Daniel Cohen-Or, Dani Lischinski, Hui Huang, Yang Zhou, Yixin Yang, Zhihao Wen.

Figure 1
Figure 1. Figure 1: Method Overview. Our framework contains two stages: enhanced reconstruc￾tion and capacity-controlled stylization. In the reconstruction stage, we optimize the 3D Gaussians using estimated depth as geometric constraints to achieve more accurate scene reconstruction. Meanwhile, we regularize the scale and shape of the primitives to better represent stylized textures later. Then, in the stylization stage, we … view at source ↗
Figure 2
Figure 2. Figure 2: Capacity-Controlled Feature Transport. The capacity of each style feature is represented by the size of its node, with larger nodes indicating greater capacity. (a) When there’s no capacity constraint, multiple rendering features are mapped to the same style feature, and matching degrades to nearest-neighbor search, resulting in the many-to-one issue. (b) With capacity control, the matching of each style f… view at source ↗
Figure 3
Figure 3. Figure 3: Cross-view Matching Guidance. We introduce a guidance map to improve matching coherence across viewpoints by reaggregating rendered features according to the transport matrix. Here, for example, at viewpoint v − 1, suppose rendered features 1 (v−1) & 2 (v−1) are assigned to style feature C in the optimal transport. Then, the guidance feature g (v) for C is aggregated from 1 (v−1) & 2 (v−1) according to the… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison. Our method demonstrates a significant advantage in reproducing the texture details and brushstrokes in the style references while still preserving the semantic structure of the input scene. images, leading to patterns and colors that are inconsistent with the style im￾age. StyleGaussian utilizes AdaIN for instant stylization, but this alignment of low-order statistics makes it diffi… view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study on loss functions. collected. The final preference ratio is summarized in [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablation study for capacity-controlled feature transport. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation on different strengths of cross-view matching guidance. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation study of geometric regularizations. [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Ablation study of enhanced reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Ablation study of enhanced reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Ablation study of optimizing color only. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Interface of User Study [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: An example of user answers [PITH_FULL_IMAGE:figures/full_fig_p025_13.png] view at source ↗
read the original abstract

While 3D Gaussian Splatting (3DGS) provides an efficient and explicit representation for novel view synthesis, enforcing stylistic coherence across viewpoints remains challenging. Existing 3D stylization methods typically apply 2D feature-matching losses independently per rendered view, which leads to unstable style allocation, many-to-one feature reuse, and limited cross-view consistency. We propose a capacity-controlled framework for multi-view stylization of 3DGS, grounded in optimal transport. Specifically, we reformulate local style matching as a semi-balanced optimal transport problem. By introducing explicit column-capacity constraints with tunable strength, our formulation mitigates many-to-one matching and enables controllable allocation of style features. This transport-based objective provides a principled mechanism for balancing feature coverage and stylistic diversity while maintaining stable correspondences across viewpoints. To further enhance cross-view coherence, we incorporate a novel cross-view matching guidance to constrain correspondences between scene content and style patterns. In addition, we introduce several geometric regularizations to enhance the vanilla 3DGS, thereby enabling optimized Gaussian primitives to represent finer-grained textures during stylization. Extensive experiments demonstrate that our approach significantly improves multi-view stylistic consistency and produces stable, expressive 3D stylizations while preserving the core semantic structure of the scene.

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

1 major / 1 minor

Summary. The paper proposes a capacity-controlled framework for multi-view stylization of 3D Gaussian Splatting (3DGS). It reformulates local style matching as a semi-balanced optimal transport problem with explicit column-capacity constraints of tunable strength to mitigate many-to-one matching and enable controllable style feature allocation. The transport objective is claimed to balance feature coverage, stylistic diversity, and stable cross-view correspondences; this is augmented by a novel cross-view matching guidance term and geometric regularizations on the 3DGS primitives. Extensive experiments are stated to demonstrate improved multi-view stylistic consistency while preserving scene semantics.

Significance. If the experimental validation holds, the work offers a novel application of capacity-constrained optimal transport to 3D stylization, potentially addressing limitations of independent per-view 2D feature matching. The explicit controllability via capacity constraints and the combination with cross-view guidance represent a concrete technical contribution to consistency in explicit 3D representations.

major comments (1)
  1. [Abstract] Abstract: The central claim that the semi-balanced OT objective with column-capacity constraints 'provides a principled mechanism for ... maintaining stable correspondences across viewpoints' is load-bearing yet appears undercut by the immediate follow-on statement that a separate 'novel cross-view matching guidance' is incorporated 'to further enhance cross-view coherence'. This raises a correctness-risk concern that the OT formulation alone may not deliver the attributed cross-view stability, requiring either stronger justification or rephrasing of the attribution in the abstract and §3/§4.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'several geometric regularizations' is introduced without enumeration or reference to the specific equations; this should be expanded with a brief list or pointer to the relevant section for immediate clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed comment on the abstract. We address the concern regarding attribution of cross-view stability below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the semi-balanced OT objective with column-capacity constraints 'provides a principled mechanism for ... maintaining stable correspondences across viewpoints' is load-bearing yet appears undercut by the immediate follow-on statement that a separate 'novel cross-view matching guidance' is incorporated 'to further enhance cross-view coherence'. This raises a correctness-risk concern that the OT formulation alone may not deliver the attributed cross-view stability, requiring either stronger justification or rephrasing of the attribution in the abstract and §3/§4.

    Authors: We agree that the current abstract phrasing risks implying that the semi-balanced OT with column-capacity constraints alone fully delivers cross-view stability, which could be read as overstated given the subsequent addition of the cross-view guidance term. The capacity constraints are intended to reduce many-to-one matching and thereby support more consistent feature allocation across views, but they do not explicitly enforce inter-view correspondence constraints. To resolve this, we will revise the abstract (and the corresponding descriptions in §3 and §4) to more precisely state that the OT objective balances coverage and diversity while contributing to stable allocations, with the novel cross-view matching guidance added as an explicit mechanism to further strengthen coherence. This rephrasing will clarify the complementary roles without changing the technical claims. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on standard OT reformulation with no self-referential equations or load-bearing self-citations shown.

full rationale

The provided abstract and description contain no equations, derivations, or self-citations that reduce any prediction or result to its inputs by construction. The reformulation as semi-balanced OT with column-capacity constraints is presented as a modeling choice, supplemented by separate cross-view guidance and geometric regularizations. No fitted-input-called-prediction, self-definitional, or uniqueness-imported patterns are observable. This matches the default expectation of a self-contained proposal without circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no information is provided on free parameters, axioms, or invented entities used in the method.

pith-pipeline@v0.9.1-grok · 5766 in / 1108 out tokens · 24001 ms · 2026-06-26T05:19:14.574658+00:00 · methodology

discussion (0)

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Reference graph

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