Textured Gaussians improve local appearance capacity, but assigning the same texture resolution to every primitive wastes storage on low-detail or weakly visible regions. We introduce AdaTex4D, an adaptive texture-capacity module for deformation-based 4D Gaussian Splatting. Each Gaussian carries packed RGBA triplanes whose two axes grow independently according to visibility normalized screen-space gradients and deformed local scales. Experiments on N3DV and PanopticSports show that AdaTex4D reduces texture storage by more than half while preserving reconstruction quality. Under fixed memory budgets, adaptive allocation also improves quality over uniform texture assignment and reduces overall model and peak memory. These results show that dynamic, anisotropic texture allocation provides a more efficient way to distribute local appearance capacity in 4D Gaussian representations.
Figures & tables
Figure 1: Dynamic evidence aggregation in AdaTex4D. Only visible dynamic observations contribute to each Gaussian’s normalized gradient demand and deformed-scale statistics; these aggregated signals determine whether local texture capacity grows along one or both axes.
Figure 2: Overview of AdaTex4D. Starting from a frozen 4DGS backbone, we aggregate visibility-normalized image-space gradient demand and deformed Gaussian scales over dynamic observations. These statistics drive anisotropic texture growth along one or both local axes, while each axis remains capped at resolution 4. The resulting heterogeneous RGBA tri-planes are stored in packed tensors and act as bounded residual appearance features, so the geometry, deformation, opacity, SH appearance, and Gaussian count of the backbone remain unchanged.
Figure 3: Qualitative comparison. Each column shows one method. For each scene, the first row is the full frame and the second row is the enlarged ROI. Top: Tennis. Bottom: Sear Steak.
N3DV
PanopticSports
Method
PSNR ↑
DSSIM1 ↓
DSSIM2 ↓
LPIPS ↓
Mem ↓
PSNR ↑
SSIM ↑
LPIPS ↓
Mem ↓
Memory budget = 80 MiB
4DGaussians [ 20 ]
26.257
0.0654
0.0380
0.1741
79.8
25.813
0.8590
0.2606
79.9
Uniform Textured 4DGS [ 1 ]
26.235
0.0658
0.0383
0.1698
79.9
25.760
0.8568
0.2487
79.8
Spacetime Gaussian Feature Splatting [ 12 ]
26.330
0.0639
0.0368
0.1675
79.7
20.008
0.7681
0.4622
79.8
AdaTex4D (Ours)
26.410
0.0649
0.0374
0.1589
79.6
25.965
0.8612
0.2358
79.7
Table 1: Quantitative comparison under fixed total-memory budgets. Both datasets use 80 MiB and 150 MiB budgets. Mem is total model storage in MiB; STG is a projected anchor. Colors mark the best three values; Mem is ranked by total storage.
N3DV
PanopticSports
Method
PSNR ↑
DSSIM1 ↓
DSSIM2 ↓
LPIPS ↓
Mem ↓
PSNR ↑
SSIM ↑
LPIPS ↓
Mem ↓
Gaussian count = 25%
4DGaussians [ 20 ]
21.917
0.1127
0.0762
0.3024
22.2
23.949
0.8211
0.3444
40.0
Uniform Textured 4DGS [ 1 ]
23.505
0.1006
0.0647
0.2753
38.4
24.130
0.8103
0.3183
74.9
Spacetime Gaussian Feature Splatting [ 12 ]
22.367
0.1057
0.0714
0.2870
49.1
17.199
0.7011
0.6044
106.3
AdaTex4D (Ours)
22.412
0.1131
0.0734
0.3032
31.9
23.980
0.8079
0.3264
60.4
Table 2: Quantitative comparison under fixed Gaussian counts. Six-scene means at 25% or 50% of converged 4DGaussians. DSSIM1/2 use ranges 1.0/2.0. Mem is total active-model storage with A2TG-style count normalization. Colors mark the best three values.
N3DV
Variant
PSNR ↑
dssim1 ↓
dssim2 ↓
LPIPS ↓
Mem ↓
Ours
27.188
0.0589
0.0326
0.1540
122.4/30.9
w/o Temporal Peak
26.857
0.0608
0.0335
0.1577
123.5/30.9
w/o Anisotropic Allocation
26.727
0.0607
0.0335
0.1571
123.4/32.4
w/o Visibility Normalization
26.543
0.0605
0.0335
0.1596
123.6/31.0
Table 3: Ablation at 50% Gaussian count (Mem: model/texture MiB).
Recent advances in 4D Gaussian Splatting (4DGS) enable high-fidelity, real-time spatiotemporal rendering, but expose a fundamental trade-off between motion expressiveness and storage efficiency. While anchor-based designs achieve compactness through anchor-level parameter sharing, their rigid uniform parametrization enforces fixed Neural Gaussian counts and feature budgets per anchor. Consequently, insufficient fidelity is addressed by excessive anchor density, rather than lightweight, targeted increases in Neural Gaussian count or feature capacity, resulting in memory waste. To overcome this rigidity, we introduce an adaptive-capacity anchor-based framework that dynamically allocates the representational capacity based on local spatiotemporal demands. Adaptive Anchor Cardinality varies the number of Neural Gaussians per anchor, concentrating primitives in regions of high geometric or motion complexity while suppressing redundancy. In parallel, Adaptive Anchor Feature Masking modulates anchor-level feature channels, assigning rich features to complex regions and lightweight representations to simpler ones. Experiments on MPEG, Panoptic Sports, and N3DV datasets demonstrate substantial storage reduction without degrading visual quality. Notably, on challenging MPEG sequences with complex motion, our method achieves up to 1.5x higher compression than state-of-the-art anchor-based methods while preserving comparable quality.
Seunghyeon Song, Joo Chan Lee, Chanung Park +4
Sungkyunkwan University Suwon, Gyeonggi-do Republic of Korea · Electronics and Telecommunications Research Institute Daejeon, Republic of Korea · Yonsei University Seoul, Republic of Korea
Gaussian Splatting has emerged as a powerful representation for high-quality, real-time 3D scene rendering. While recent works extend Gaussians with learnable textures to enrich visual appearance, existing approaches allocate a fixed square texture per primitive, leading to inefficient memory usage and limited adaptability to scene variability. In this paper, we introduce adaptive anisotropic textured Gaussians (A2TG), a novel representation that generalizes textured Gaussians by equipping each primitive with an anisotropic texture. Our method employs a gradient-guided adaptive rule to jointly determine texture resolution and aspect ratio, enabling non-uniform, detail-aware allocation that aligns with the anisotropic nature of Gaussian splats. This design significantly improves texture efficiency, reducing memory consumption while enhancing image quality. Experiments on multiple benchmark datasets demonstrate that A TG consistently outperforms fixed-texture Gaussian Splatting methods, achieving comparable rendering fidelity with substantially lower memory requirements. Project page: http://github.com/Rickyeeeeee/A2TG.
Dynamic 3D Gaussian splatting faces a fundamental tension between motion consistency and visual fidelity. Deformation-based approaches preserve temporal correspondence but suffer from motion over-factorization, oversmoothing high-frequency dynamics. In contrast, 4D-primitive methods capture fine visual details yet incur temporal overparameterization, breaking object identity and leading to severe storage overhead. To resolve this, we introduce Multi4D, a framework for high-fidelity dynamic Gaussian Splatting based on multi-level competitive allocation. Instead of a monolithic representation, we distribute modeling capacity across three structured levels: static structure, persistent dynamic geometry, and transient appearance primitives. Through shared rasterization and residual-driven optimization, these levels dynamically compete to explain photometric error, enabling adaptive specialization without pre-assigned decomposition. This allocation preserves long-term motion consistency while capturing fine dynamic detail, achieving state-of-the-art rendering quality and real-time performance with significantly fewer dynamic primitives. Furthermore, because our representation explicitly tracks compact persistent Gaussians over time, semantic features can be embedded afterward, enabling Multi4D to achieve state-of-the-art 4D segmentation accuracy with an order-of-magnitude speedup. Project page: https://batfacewayne.github.io/Multi4D.io/