Dynamic Novel View Synthesis in High Dynamic Range
Authors: Kaixuan Zhang
Zhipeng Xiong
Minxian Li
Mingwu Ren
Jiankang Deng
Xiatian Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: High Dynamic Range
4D Gaussian Splatting
Dynamic Scene Modeling
Deep Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: High Dynamic Range Novel View Synthesis (HDR NVS) seeks to learn an HDR 3D model from Low Dynamic Range (LDR) training images captured under conventional imaging conditions. Current methods primarily focus on static scenes, implicitly assuming all scene elements remain stationary and non-living. However, real-world scenarios frequently feature dynamic elements, such as moving objects, varying lighting conditions, and other temporal events, thereby presenting a significantly more challenging scenario. 
To address this gap, we propose a more realistic problem named HDR Dynamic Novel View Synthesis (HDR DNVS), where the additional dimension ``Dynamic'' emphasizes the necessity of jointly modeling temporal radiance variations alongside sophisticated 3D translation between LDR and HDR. To tackle this complex, intertwined challenge, we introduce HDR-4DGS, a Gaussian Splatting-based architecture featured with an innovative dynamic tone-mapping module that explicitly connects HDR and LDR domains, maintaining temporal radiance coherence by dynamically adapting tone-mapping functions according to the evolving radiance distributions across the temporal dimension. As a result, HDR-4DGS achieves both temporal radiance consistency and spatially accurate color translation, enabling photorealistic HDR renderings from arbitrary viewpoints and time instances.
Extensive experiments demonstrate that HDR-4DGS surpasses existing state-of-the-art methods in both quantitative performance and visual fidelity. Source code is available at \url{https://github.com/prinasi/HDR-4DGS}.

Published as a conference paper at ICLR 2026
DYNAMIC NOVEL VIEW SYNTHESIS IN HIGH DY- NAMIC RANGE
Kaixuan Zhang
Zhipeng Xiong*
Minxian Li
Mingwu Ren 2.2
Jiankang Deng²
Xiatian Zhu
Naajing University of Science and Technology
2 State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery Imperial College London
University Surrey
ABSTRACT
High Dynamic Range Novel View Synthesis (HDR NVS) seeks to learn an HDR 3D model from Low Dynamic Range (LDR) training images captured under con- ventional imaging conditions. Current methods primarily focus on static scenes, implicitly assuming all scene elements remain stationary and non-living. How- ever, real-world scenarios frequently feature dynamic elements, such moving objects, varying lighting conditions, and other temporal events, thereby present- ing a significantly more challenging scenario. To address this gap, we propose a more realistic problem named HDR Dynamic Novel View Synthesis (HDR DNVS), where the additional dimension "Dynamic" emphasizes the necessity of jointly modeling temporal radiance variations alongside sophisticated 3D trans- lation between LDR and HDR. To tackle this complex, intertwined challenge. we introduce HDR-4DGS, a Gaussian Splatting-base architecture featured with an innovative dynamic tone-mapping module that explicitly connects HDR and LDR domains, maintaining temporal radiance coherence by dynamically adapting tone-mapping functions according to the evolving radiance distributions across the temporal dimension. As result, HDR-4DGS achieves both temporal radi- ance consistency and spatially accurate color translation, enabling photorealistic HDR renderings from arbitrary viewpoints and time instances. Extensive exper- iments demonstrate that HDR-4DGS surpasses existing state-of-the-ar methods in both quantitative performance and visual fidelity. Source code is available https:/ /tte:://gtttubu. com wwwppoooo//////t/./..... BaatirasssHHRR-DDDD -HDR-4DGS
INTRODUCTION
Recent years have remarkable progress in Novel View Synthesis (NVS), which reconstructs 3D scene representations from multi-view images to enable photorealistic rendering from arbitrary viewpoints. These advances have fueled applications in gaming (Niedermayr al., 2024), AR / VR (Zhou et al. 2018). and autonomous driving (Wang et al., 2024b). However, most existing NVS methods (Gao al. 2022 Wu al., 2024c) operate under two limiting assumptions: static scenes and low dynamic range inputs. These constraints significantly hinder their applicability in real-world environments, which often exhibit complex motion, time-varying illumination, and sensor-imposed luminance clipping.
Dynamic Novel View Synthesis (DNVS) extends NVS to scenes with temporal dynamics, recon- structing 4D radiance fields coherent in both space and time. Recent efforts in DNVS (Fang et al.) 2022 Gao et al. 2021] Tian et al., 2023 Duan et al. 2024 Yang et al., 2024 Cho al. 2024) have made notable progress in modeling dynamic geometry and appearance. However, they re- main restricted to LDR imagery, which fails to capture the full spectrum of scene radiance. As result, these methods struggle under high-contrast conditions (e.g., direct sunlight or low-light environments) where overexposure and underexposure lead to significant information loss. More
"Minxian Li (minxianli waj.uiiiiiinsttu.uu...m is the corresponding author with School of Computer Science and Engineering, Nanjing University of Science and Technology.

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Published as a conference paper at ICLR 2026
fundamentally, LDR imaging is inherently misaligned with human visual perception, even under moderate lighting conditions. While the human eye can adapt to luminance levels spanning over ten orders of magnitude (Reinhard 2020). typical LDR semsors cover only narrow dynamic range. In addition, LDR images compress radiance through nonlinear camera response functions (CRFs), distorting local contrast, suppressing brightness gradients, and reducing color fidelity. These lim- itations not only impair perceptual quality under extreme lighting but also degrade the realism of ordinary scenes.
High Dynamic Range (HDR) imaging addresses these shortcomings by capturing a significantly broader range of luminance and color. By preserving detail in both highlights and shadows and maintaining fine- ine-grained contrast, HDR techniques provide closer match to the perceptual capa- bilities of the human visual system. Recent works in HDR NVS (Huang et al. 2022 Cai et al., 2024 Liu et al. 2025a) have attempted to reconstruct HDR content from multi-exposure LDR images of calibrated sensors. However, these methods are restricted to static scenes, implicitly assuming that all elements remain fixed over time. In contrast, real-world HDR scenarios are inherently dynamic, often involving moving objects, shifting illumination, and transient phenomena. These dynamics violate the assumptions of existing HDR NVS pipelines and introduce significant challenges: non- rigid motion and temporal variation create complex spatiotemporal inconsistencies, while the lack of reliable luminance priors from sparse LDR observations leads severe photometric ambiguities.
To bridge this gap, we introduce High Dynamic Range Dynamic Novel View Synthesis (HDR DNVS), new, more practical task that seeks to reconstruct temporally coherent HDR radiance fields and dynamic geometry from sparse, time-varying LDR inputs. HDR DNVS demands the joint modeling of evolving scene structure and HDR radiance, posing both geometric and photo- metric challenges absent in prior static or LDR-constrained settings. To tackle this, we propose HDR-4DGS, a novel framework based on Gaussian Splatting (Kerbl et al., 2023) and equipped with a biologically inspired dynamic tone-mapping module. Drawing inspiration from human vi- sual adaptation (Clifford et al.) 2007). where retinal photoreceptors dynamically adjust to ambient brightness, HDR-4DGS includes dynamic radiance context learner that models temporal radiance distributions. This is followed by per-channel tone-mapping functions that connect LDR repre- sentations with HDR space in temporally adaptive and spatially accurate manner. This module explicitly bridges the LDR-HDR gap while maintaining radiance consistency and chromatic fidelity across time and space. Although conceptually straightforward, our proposed model embodies an intuitively elegant and computationally efficient design, which strategically adapts established se- quential modeling techniques address the core challenges of HDR DNVS with precision.
Our contributions are: (I) We introduce the HDR DNVS problem for the first time, which requires learning 4D HDR radiance fields with dynamic geometry and temporally coherent appearance from sparse LDR input. (II) We propose HDR-4DGS, novel Gaussian Splatting architecture featuring dynamic tone-mapping for adaptively bridging the LDR and HDR domains under complex spa- tiotemporal variations. (III) To enable quantitative evaluation of HDR DNVS methods, we intro- duce HDR-4D-Syn and HDR-4D-Real
two novel benchmark datasets comprising 8 high-fidelity synthetic scenes and 4 real-world captured sequences, respectively. Each scene is meticulously an- notated with ground-truth HDR images, time-varying 3D geometry, and synchronized multi-view LDR observations. (IV) Extensive experiments show that HDR-4DGS achieves state-of-the-art per- formance in both quantitative metrics and perceptual quality on challenging dynamic scenes.
RELATED WORK
Novel view synthesis (NVS) has seen transformative progress via neural rendering techniques. Early multi-view geometric methods (Schonberger & Frahm 2016 Waag et al. 1022] face limi-
tations in handling occlusions, textureless regions, and computing efficiency (Jiang) 2023). Modern approaches leverage continuous scene representations via deep networks, such as Neural Radiance
Fields (NeRF) (Mildenhall et al. 2021 and the variants (Deng et al. 2022 Roessle et al. 2022 Wei
et al. 2021 Xu et al. (202)) establishing coordinate-space implicit modeling for photorealistic syn- thesis under spatial smoothness constraints. However, NeRF's ' ray- ma-mahchiig paradigm suffers from high computational costs (Luo et al. 2024). In contrast, 3DGS (Kerbl et al. 2023) introduces an efficient point- bastd representation by parameterizing scenes as anisotropic 3D Gaussians, decou- pling geometry and appearance while enabling real-time rendering with complex visual effects (e.g.,
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specular highlights). Recent advances enhance 3DGS through frequency-domainn supervision
LLanng
et al. 2024). depth-regularized optimization (Chung et al. 2024; Kung et al. 2024 Li et al.
2024
and memorr--effiiien designs (Wang et al. 2024c; Chen et al. 20024 Fan et al. [2024]]] Lu et al.
2024
Yang et al. 2025), achieving superior rendering quality. Nevertheless, these methods predominantly focus on static LDR scenes, limiting their applicability to real-world dynamic scenarios.
HDR novel view synthesis (HDR NVS) aims to reconstruct HDR scenes from multi-view LDR images. Early works like HDR-NeRF (Huang et al.) 2022) extend neural radiance fields by incor- porating an MLP-based tone-mapping module to bridge physical radiance and digital color spaces. To realize real-time rendering, HDR- GS (Cai et al.
2024) introduces a 3DGS framework with neural Kone-r that explicitly models HDR-to- to-R-to-DR radiance transformations, achieving real- time HDR rendering while surpassing NeRF-based quality. Recently, GaussHDR (Liu et al. 2025a) proposes to unify 3D and 2D tone mapping in 3D Gaussian Splatting to facilitate HDR render- ing. However, a critical limitation of these methods is their exclusive focus on static scenes; none explicitly model temporal dynamics, both in spatial and color space. Consequently, they are funda- mentally unable to address the core challenges posed by real-world scenarios involving time-varying geometry, non-rigid motion, or temporally evolving illumination.
Indeed, Wu et al. (2024a) preliminarily investigated dynamic HDR reconstruction from LDR se- quences. Interestingly, its has never carefully evaluated the HDR output, verifies its perfor- mance on real-world scenes, leaving the HDR DNVS problem largely under-explored. To address these issues, we introduce purposed benchmark by carefully re-engineering its dataset and capture a real-world dataset additionally, including LDR/HDR paired training imagery and HDR quanti- tative assessment. We further propose novel HDR DNVS model, HDR-4DGS, which delivers significantly more accurate HDR renderings while operating an order of magnitude faster.
Dynamic novel view synthesis (DNVS) focuses on modeling dynamic scenes with time-varying geometry and radiance. The key challenge lies in capturing intrinsic spatiotemporal correlations. Building upon NeRF (Mildenhall et al., (02T), two primary approaches have emerged: i) implicit / explicit spatiotemporal representations decompose scenes into time-aware feature grids to learn
6D plenoptic functions (Cao & Johnson 2023 Li al., 2022b] -Fidovvcch-Kil al. 2023: Wang al., 2023). and ii) deformation-awan
fields model motion through deformable transformations (Pumarola et al. 2021, Song et al.) 2023; Abou-Chakra et al. 2024). Recent advances lever-
age 3DGS for dynamic rendering along two directions: 7) deformation-baseel models (Wu et al.) 2024bb Kratimenos et al.) 2024 Bae et al. 2024]] [Shan et al. 2025) maintain canonical Gaussians deformed via time- varying fields, trading precise motion tracking for temporal continuity; ii) hyper- dimensional representations (Yang et al. 2024 Duan et al. 2024) extend Gaussians to 4D by in- troducing temporal centers and spatiotemporal rotations. However, all these methods are limited to LDR outputs, and we extend this to high-fidelity HDR DNVS problem.
METHOD
Problem In HDR DNVS, we aim to learn a HDR 3D model Fn for target dynamic scene G, FR : (t', V') + L vr. that would render an HDR image 12 vr for any timestamp e' and viewpoint V.. To that end, we capture a set of multi-exposure LDR training images I' = (1) I.,.. 1r ). each I` associated with the exposure time sampled from the choices F. {ei, ,er,, and the camera viewpoint V/ selected from Q distinct viewpoints V= (Vi,... Vo} where T denotes the total timesteps and P the number of exposure time choices. We may have optional access to coupled HDR training data Is {I .I, .11)..
3.1 HDR-4DGS OVERVIEW
The HDR DNVS problem comes with additional complexity of time-evolving structures and il- lumination as compared to HDR NVS. To overcome that, we introduce HDR-4DGS, a Gaussian Splatting based framework that reconstructs 4D spatiotemporal HDR radiance fields. HDR-4DGS is composed of a generic dynamic scene representation model and a novel dynamic tone tone-mapping mapping mechanism. An overview is depicted in Fig. [
3

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Average pooling
Radiance bank
time
... 1-
HDR GS model (t(()
cfE R
R
GT LDR Ig
(HIV
Rendering
Dynamic radiance context learner f Rd
(L.V).)))
1 ++
HDR 1220
Scaling
LDR E.
Exposure time 6
Lidr
Rendering
ce R
(14, vT)
Per-channel cone-mapping
LDR images
GT HDR r
Dynamic tone mapper
LDR GS model (t)
(b)
(c)
Figure 1:
Overview of HDR-4DGS. (a) Input data and scene representation; (b) Our proposed
Dynamic Tone Mapper (DTM) for temporally adaptive HDR-LDR translation; (c) Loss formulation
for joint optimization of geometry, radiance, and tone mapping. & Dot product. @: Concatenation.
3.2 DYNAMIC SCENE REPRESENTATION
HDR-4DGS can integrate with existing dynamic scene representation to better capture spatiotem- poral variations. Specifically, we adopt the 4D Gaussian Splatting (4DGS) framework (Yang et al. 2024) due its conceptual elegance and coherent formulation of dynamic scenes.
4DGS extends the formulation of 3DGS (Kerbl et al. 2023) by introducing temporal dimension, allowing pixel observations I to depend not only on spatial coordinates (u, v) in the image plane but also on an explicit timestamp t. This reformulates the original 3DGS framework as:
v, Σ P.())riu, of))a 1=1
II p,(0), ((,())))) ==1
(1)
where pt) the marginal probability over time t. (u, ult) given f, such that p; (u, t) = Ps (u, o pt)(). and N denotes the number of Gaussian points. Time and space are treated equally to form a unified 4D Gaussian model, where each Gaussian's 's mean is denoted by μ (Mar Myn Mazi Hr), and its covariance matrix is defined by Σ RSS R with appropriately extended rotation matrix R and scaling matrix S. The marginal p(t) follows a one- dimensional Gaussian distribution: p(() = -[t pt, Σ,), where AV(:) denotes a normal distribution.
the conditional spatial probability
Additionally, 4DGS incorporates 4D extension of spherical harmonics (SH) to represent the tem- poral evolution of appearance. The view-dependent color c; is modeled using combination of 4D spherical harmonics, constructed by integrating traditional SH with Fourier series. This design en- ables dynamic modeling of radiance variations across time, supporting the construction of a radiance bank that facilitates HDR-LDR translation.
In our context, extend the original color representation space of 4DGS from LDR colors HDR high- high-fidelity radiance fields that capture a broader range of luminance variations inherent in real-world dynamic scenes.
colors, enabling the accurate synthesis of
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3.3 DYNAMIC TONE MAPPER
To address the challenge of maintaining temporal radiance consistency in dynamic scenes, we pro- pose a novel dynamic tone mapper (DTM), as shown in Fig. [b). DTM explicitly connects the HDR and LDR domains by dynamically adapting per-channel tone-mapping functions in response evolving temporal radiance patterns. Given timestamp exposure time ec, and current HDR color attributes c E R N33
DTM translates HDR colors into their LDR counterparts:
= DTM ec. t)
where of E IR Nx3 denotes the resulting tone-mapped LDR colors.
Leveraging 4DGS's explicit radiance modeling through 4DSH, DTM first constructs radiance bank by storing per-timestamp mean HDR color statistics. The radiance signature r for each timestamp calculated the average over all N Gaussian points:
r
E
(3)
1=1
sliding window of k previous frames collects the sequence fri-kch which is processed by a Dynamic Radiance Context Learner (DRCL) generate a radiance context embedding:
fr = DRCL(T E R",
(4)
where d denotes the dimension of this context embedding. DRCL can be generally realized by any existing sequence model such as RNN (Salchinejad et al.) 2017). LSTM (Staudemeyer Morris 2019). GRU (Cho al. 2014) or Transformer Vaswani al. 2017).
To perform adaptive tone mapping, we concatenate the scaled HDR colors (converted to the loga- rithmic domain) with the exposure time and radiance context embedding:
c/ = go cf + log ct, fz));
(5)
where is the per-channel tone- cone-mapiing function and [-] denotes concatenation. Scaling HDR col- ors by exposure time aligns with the principles of the CRF, which models the mapping between scene radiance and observed intensity as peoosure-eeeendett This normalization ensures consistent ap- pearance modeling across varying exposure settings and facilitates stable learning. By incorporating f, DTM enables radiance context-aware HDR-to-LDR translation, improving radiance consistency across time in dynamic scenes.
3.4
MODEL OPTIMIZATION
The overall objective function used to optimize HDR-4DGS is defined as:
Lisual = Lue + a.
(6)
where Lide denotes the loss computed in the LDR domain, and Lade refers to the HDR reconstruction loss. The weighting factor is set zero if HDR ground truth is unavailable during training.
To mitigate overfitting associated with directly applying 3D tone mapping on HDR Gaussian fields (Liu et al. 2025a), adopt extra pixel-level supervision in addition existing ray-level pervision over the dynamic tone mapper, which introduces additional constraints to facilitate robust 11,2 go (log Ii + log e,, fel), where 12 is the HDR image rasterized by the HDR Gaussian model, and 12,20 is the tone-mapped LDR image at time t. During training, we supervise both the moneed LDR image 11.2D and the LDR image 1/,DD rendered directly from the LDR model. This dual supervision improves the learned tone mapper's generalization capability, validated in our ablation (Tab. 5).
CRF learning:
We define the image reconstruction loss as a weighted combination of L1 and D-SSIM loss HHang ct al. 2022 Nilsson & Akenine Akeline-Mööler 2020):
C(I,, 12) (1 A)C (L) + ACD.SSI((II I2),
where I are paired and balances their contributions. Accordingly, the losses are defined as:
Lia C(I IT) L(T) It), Lutt L(t)22: 11))
(8)
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Table Results on HDR-4D-Syn. ": HDR only supervision; 1; LDR+HDR supervision.
Row
Method
Supervision
HDR SSIM
LDR SSIMI
Training time (min)
Inference
PSNR1
LPIPS)
PSNR1
L.IPS.
speed (fps)
HcxPlane
LDR
14.20
0.564
0,551
28.45
1.60
HexPlane'
HDR
24.89
0.771
0.377
59.64
1.94
4DGS
LDR
13.92
0,549
0.281
116.25
75.82
4DGGS
HDR
22.40
0.650
0.345
44.58
172.61
HDR-NeRF
LDR
8.54
0.062
0.552
21.66
0.664
0.553
212.83
0.061
HDR-GS
LDR
4.64
0.158
0.645
6.45
0.272
0.423
13.88
380.38
HDR-GS
LDR
0.360
0.482
10.84
0.372
0.378
255.21
HDR HlexPlane
LDR
14.70
0.649
0.287
32.59
0.912
0.145
37.83
1.61
HEeR-HeePlanee
LDR+HDR
29.30
0.844
0.223
31.09
0.896
0.185
54.31
1.35
HDR-4DGS (Ours)
0.055
HDR-IDGS' (Ours)
LDR+HDR
30.40
0.914
0.097
30.69
0.927
0.097
76.86
48.63
lego
HDR HexPHex
HDR-IDGS
Ground Truth
HDR-HexxPlae
HDR-4DGS
Ground Truth
Figure 2: Visual comparison of HDR DNVS on HDR-4D-Syn
where I; is the ground-truv truth LDR image. The tone mapped HDR images I 12 and i are derived from rendered HDR image 20 and ground-truth HDR image I respectively, using μ-law com-
pression: 7 Iox(1++=nocm(1"1) where μ is a compression factor and norm(-) min-max normal-
log( ++)
ization. This transformation aligns HDR and LDR domains for consistent comparison. Notably, we choose the LDR images 12 rasterized by Gaussian Splatting as our final LDR rendering results.
EXPERIMENTS
Datasets. Due to no benchmarks for HDR DNVS, we introduce two complementary datasets: HDR- 4D-Syn and HDR-4D- -D--eal..
HDR-4D 4D-y-n consists of 8 synthetic dynamic scenes adapted from the dataset by Wu et al. (2024)). It features multi-exposure video sequences captured under varying exposure settings, accompanied by synchronized multi-view LDR video streams. Corresponding HDR ground truth frames are re-synthesized to ensure high-fidelity supervision and evaluation. The real-world dataset, HDR-4D-Real captures 4 dynamic indoor scenes real-world settings. Videos are recorded under three distinct exposure times using six synchronized iPhone 14 Pro devices. Ground truth HDR images are generated UltraFusion (Chen et al. (025). ensuring realistic and high-quality HDR reconstructions. Further details are provided in Appendix A.1
Implementation details. HDR-4DGS trained with the Adam optimizer using the same parameters as 4DGS Yangg et al.) 2024). For our dynamic tone mapper, the learning rate is set to 5 x 10-4. and the dimension temporal radiance context features is set to 2. We adopt the same structure of per-channel mone-mapping functions in our dynamic tone mapper as HDR-GS (Cai et al. 2024) and GRU (Cho et al., 2014) is adopted to implement the dynamic radiance context learner by default. For equation 7 X set to 0.2, and a is set to 0.6 if HDR ground truth is available.
Evaluation metrics. We adopt the PSNR and SSIM as quantitative metrics, LPIPS as an additional perceptual metric. Following prior works (Cai al. 2024 Huang al. 2022 Liu al. 2025a), apply Photomatix Pro (HDRsoft Team 2025) to convert HDR images into displayable LDR images for qualitative visualization and fair comparison. Futhermore, training time and inference speed (fps) are reported. Results are averaged over all scenes.
4.1 QUANTITATIVE EVALUATION
Competitors. We compare HDR-4DGS with latest alternatives: HexPlane Cao & Johnson
2023
4DGS YYnng et al. 2024) HDR-HexPlane (Wu et al. 2024a), HDR-NeRF (Huang al. 2022

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Table 2: Results on HDR-4D-Real. ": HDR only supervision; 1; LDR+HDR supervision.
HDR SSIM
LDR SSIM
Training time (min)
Inference
Row
Method
Supervision
PSNRT
LPIPS]
PSNRT
LPIPS,
speed (fps)
HexPlame
LDR
13.82
0.551
0.576
25,50
0.44
HexPlane"
HDR
32.76
0,893
0.242
28.24
0.49
4DGS
LDR
7.99
0.072
0.620
42.50
290.57
4DGS'
HDR
7.85
0.220
0.534
40.75
307.85
HDR NeRF
LDR
14,60
0,711
0.411
8,326
0.029
0.943
212.25
0.17
HDRR-G
13.27
20.52
38.25
73.30
HDRR-GE SORR-GST
LDR+HDR
29.40
0.936
0.097
20.85
0.834
0.182
56.50
64.88
HIR PlaxPlane
LDR
9.306
0.672
0.353
27.44
0.748
0.353
36.98
0.33
Plaaaee
10
HDR-4DGS (Ours)
LDR
14,50
0,884
0.200
26.88
0.825
0.221
98.75
35,27
HDR-4DGS! (Ours)
LDR+HDR
25.13
0.909
0.162
30.09
0.927
0.097
76.86
48.63
excavator
HDR-GS
HDR-HexPlane
HDDR-4DGS
Ground Truth
Figure 3: Visual comparison of HDR DNVS on HDR-4D-A
and HDR-GS (Cai et al. 2024 HDR-HexPlane preliminarily explores dynamic HDR synthesis by extending HexPlane with exposure conditioning and static Sigmoid tone mapper. However, it lacks HDR supervision and omits explicit evaluation of HDR outputs, all of which have been addressed here for both fair comparison and completeness.
Although HDR-GS and HDR-NeRF were originally designed for static scenes, we adapt them on dynamic scenes to underscore the importance of explicit spatial modeling in dynamic HDR recon- struction. All models tested with their official implementations to ensure optimal performance.
Results. Tab. 1 and Tab. g present the results for both LDR and HDR DNVS on HDR-4D-SSn and HDR-4D-BBB Real respectively. HDR-NeRF encounters numerical instability during training when both LDR and HDR supervision are applied simultaneously, frequently leading failed optimization. Meanwhile, 4DGS struggles to reconstruct HDR scenes on HDR-4D-Real under HDR-only super- vision, ground-truth HDR images generated via 2D methods lack multi-view consistency. We highlight the following key findings, focusing on HDR DNVS performance:
(I) Superior HDR DNVS quality. HDR-4DG consistently outperforms all competing methods, including HDR-NeRE HDR-GS, and HDR-HexPPie for HDR DNVS on HDDR-DD-yrre owing pri- marily to explicit spatiotemporal HDR radiance modeling and the integration of dynamic mapping mechanism that preserves temporal radiance coherence across views and exposures. In contrast, HDR-GS and HDR-NeRF rely on static tone mappers, failing accurately bridge the HDR and LDR domains; while HDR- HexPlee achieves higher PSNR on HDR-4D-Real, we attribute this largely the noise HDR ground truth and the known limitations of PSNR metric, particu- larly its tendency to favor overly smooth or blurred reconstructions (Li et al. 2020). as visually demonstrated in Fig. g As shown Fig. 3 and videos the supplementary material, HDR-4DGS preserves significantly sharper spatial structure and more accurate color details than all competitors. Although HDR-4DGS exhibits structural degradation movable regions, we attribute this limitation to the inherent expressive capacity of our base representation model, 4DGS, rather than instability in HDR-4DGS handling of dynamic ranges. For further visual evidence, please refer to Fig. 10 and Fig. in the Appendix where the vanilla 4DGS YYnng et al., 2024) suffers from even more severe structural distortion and geometry loss. In contrast, our HDR-4DG3 4DGS alleviates these degradations to noticeable extent, supporting the effectiveness of the proposed HDR modeling strategy.
(II) Effectiveness of dynamic tone mapping. Our dynamic tone mapper plays a central role in enhancing HDR synthesis. By modeling exposure-dependent radiance dynamics with temporal con-

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Dynamic Tome Tone-Mapeer Carvess
Dynamic Tene-Mapper Curves
0.030
1-00000
F-0.00
0.004
0.025
1-0.115
1-0.800
0.003
Intematyy
0.0.0
0.015
0.002
0.010
0.001
0.005
0.000
0.00
0.0
0.6
6.8
0,4
HDR Radiance
Radionne
(a) Airplane
(b) Bed
Figure Temporal variation with learned tone mapping patterns by DTM in two scenes.
text, it enables precise HDR-LDR translation and promotes stability during training and inference.
This proves essential not only for high-fidelity HDR rendering but also for generalizing under vary- ing lighting conditions.
(III) Training flexibility and robustness. HDR-4DGS exhibits strong resilience across diverse supervision settings. While both HDR-4DGS and HDR-HexPlane benefit from joint LDR-HDR supervision, HDR-4DGS maintains competitive performance even with LDR-on) supervision. This highlights the ability of our dynamic tone mapper and radiance context learner extract and exploit temporal radiance correlations, enabling accurate HDR reconstruction even without HDR labels.
(IV) Practical efficiency. addition to rendering quality, HDR-4DGS delivers substantial effi ciency improvements. It achieves up to 36x and 200 faster inference compared to LDDR-HexPlane while preserving competitive training times on HDR-4D-Syn and HDR-4D-Real, respectively. This efficiency gain is critical for scaling HDR synthesis to real-world, dynamic scenarios where run- time performance key constraint. Note that adding HDR supervision leads to faster inference for HDR-4DGS (Row 10 v.s. 11 in Tab. [ and Tab. 2 since using HDR signal would effectively compress the model, as there is no need for the model to generate redundant Guassian points to approximate the transformation from LDR HDR.
Overall, the results confirm that HDR-4DGS sets a new state of the art in HDR DNVS by jointly optimizing HDR fidelity, dynamic tone adaptation, and inference efficiency both synthetic and real datasets.
4.2 DYNAMICS IN TONE MAPPING CAPTURED
To verify that our DTM learns dynamic rather than static tone mapping, we analyze scenes with pro- nounced motion-induced brightness changes (e.g., Airplane and Bed). After training HDR -DGS, extract the learned DTM module along with the radiance bank, both jointly define dynamic mapping from HDR radiance to LDR counterpart, with condition on the time. To assess the dynam- ics of this learned mapping, we then sample different timestamps and visualize the corresponding tone-mapping curves over the HDR intensity, ass illustrated in Fig.
We observe that (I) all curves monotonically increasing (consistent with CRF definition (Huang (et al. (2022)) yet exhibit scene-specific patterns, indicating adaptive tone reproduction; (II) In the Airplane scene, the red channel curve consistently lies above green and blue, reflecting the scene's dominant reddish tone. As the airplane moves from shadows to brighter regions, curves shift upward over time, tracking increasing luminance: (III) In the Bed scene, curves gradually descend as the bed unfolds and ambient lighting dims. These temporal dynamics confirm DTM's ability to adapt tone mapping to evolving lighting conditions and scene content.
4.3 QUALITATIVE RESULTS
While PSNR. SSIM, and LPIPS are commonly used metrics, they may not fully capture perceptual quality, necessitating qualitative visual evaluation. As shown in Fig. 2 reconstruct extreme radiance details, while HDR-4DGS successfully captures more intricate struc- tures. From Fig. 3 it can be observed that HDR-GS struggles reconstruct dynamic objects, and
fails to
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Published as a conference paper at ICLR 2026
HDR: HDR-- Mee-HeePllnn Plane
HDR-ADOS
Ground Trush
Time
Figure 5: Comparison of HDR renderings' temporal radiance variations.
HDR-HexPlane tends produce over-smoothed results. In contrast, HDR-4DGS effectively repre- sents the dynamic scene while preserving finer details. Meanwhile, temporal radiance consistency is also challenged by HDR- HexPlane, while HDR-4DGS synthesizes spatiotemporally coherent HDR
results, as illustrated in Fig. 5
Please refer to Appendix A.. for more visualization comparisons.
4.4 ABLATION STUDY
We conduct ablation studies on the HDR-4D-Syn dataset (see Appendix for more experiments).
Effect of joint HDR reconstruction. To validate the effect of our DTM, we train 4DGS on single- exposure LDR images (exposure time 2.0s) and convert the synthesized LDR images of novel views to HDR images with a variety of cxisting Single HDR Image Reconstruction models: EIN (Liu et al.
20256) IntrinsicHDR (Dille et al., 2024), KPNet (Wang et al. 2024a). KUNet (Wang et al. 2022)
Tab. shows that the two-stage pipeline significantly underperforms our HDR-4DGS since converting single-exposure LDR inputs to HDR is inherently ill poo-po where the miss- ing radiance information cannot be reliably re- covered in isolation from the scene reconstruc- tion process. Our integrated approach instead jointly optimizes radiance representation and novel-view synthesis, establishing its necessity and irreplaceability for high-fidelity HDR scene re- construction.
Table 3: Results of 4DGS with independent HDR.
HDR SSIM
Method
PSNR
LPIPS
4DGS EIN (Liner al 2255
17.75
0.770
0.185
4DGS berinceDDR (Dilec 2024
11.63
0.673
0:251
4DGS KPNet (Wane Sr 21240
20.92
0.813
0.197
4DGS KUNer Wang mi 2i223
19.00
0.715
0.172
(Ours)
0.076
Effect of dynamic tone mapper. We conduct comparative experiments against an MLP variant (part of HDR-GS (Cai et al. 2024) and HDR-NeRF (Huang et al. 2022). used for static tone- mapping), two classical tone mappers Durand (Durand & Dorsey 2000) and Reinhard (Reinhard et al. (023) and the static tone mapper adopted by HexRHlexPlane. Additionally, ablation studies are performed assess the contribution of extra pixel-level supervision (see Sec. (.4). Key findings are: (I) Tab. 4 reveals that when replacing our dynamic tone mapper with other existing counterparts, we observe significant performance degradation. This confirms that explicit modeling of temporal radiance variations through our dynamic tone mapper is critical for maintaining HDR fidelity while existing mappers are inferior in doing that. Further, complementary ablation studies with respect to the scene representation verify that the observed performance gains primarily attributable to our dynamic tone mapper, rather than 4DGS alone. (II) Disabling the pixel-level supervision leads performance reduction (PSNR decreases by 1.03 dB), shown in Tab. 5 This demonstrates

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Published as a conference paper at ICLR 2026
Raseline
HDR BDR-
Figure 6: Continuous radiance variations comparison of HDR DNVS. Photomatix Pro HDDssfft
Team 2025 is used to facilitate the comparison of radiance transition.
that joint optimization with both ray-level and pixel-level constraints provides essential supervisory signals for learning physically plausible tone mapping operators.
In addition, to rigorously evaluate the efficiency of our proposed DTM in preserving continuous radiance variations, we conduct ablation studies by substituting the DTM with an MLP baseline. Specifically, we synthesize temporally coherent HDR sequences for a dynamic scene using both architectures, as shown Fig. (also see Fig- [7] where our HDR-4DGS with DTTM achieves superior temporal coherence in radiance transitions compared to baseline approach, validating the effectiveness of our DTM in preserving HDR coherence in dynamic domain.
Table 4: Ablation on dynamic tone mapping.
Table 5: Analysis of pixel-level supervision.
Method
PSNR
SSIM1
LPIPSS
PixeI-level Supervision
HDR SSIM
Reinhard (Reinhard et al 2023
22.10
0.812
0,210
PSNR
LPIPS_
MLP (HDR-GS)
Duramd (Duratn & Dorsey 2000
23.92
22,85
0.84]
0.825
0,142
0.195
No
24.85 25.88
0.853 0.865
0.169
DTM (Ours)
25.88
0.865
0.076
Yes
0.076
Temporal length. As described in Sec. [.3] our proposed dynamic tone mapper (DTM) extracts
radiance cues from the past k timestamps in the radiance bank using a sliding window and dynamic radiance context learner. The hyper-parameter k governs the trade-off between temporal context coverage and computational efficiency: larger & captures extended radi- ance dynamics but risk redundancy and noise sensitivity (e.g., scenes with large, rapid motions or complex illumination dy- namics), while smaller prioritizes immediate temporal cues at the cost of modeling complex patterns (e.g., scenes char- acterized by minor motion or slow illumination variation). Through ablation experiments with k as shown in Tab. 6
Table 6: Analysis of the temporal context length.
k
HDR SSIM
PSNR
LPIPS).
5
24.74
01852
0.092
10
24.76
0.851
0.098
20
25.88
0.856
0.076
30
24.29
0.825
0.094
we find that i = 20 achieves optimal performance, balancing responsiveness to dynamic scenes with practical efficiency constraints.
CONCLUSION
This work introduces High Dynamic Range Dynamic Novel View Synthesis (HDR DNVS), address- ing critical limitation in prior HDR synthesis restricted to static scenes. We present HDR-4DGS, a novel framework designed to reconstruct temporally coherent HDR radiance fields and dynamic geometry from sparse, time-varying LDR observations. The key of HDR-4DGS lies in its dynamic tone mapping module, which leverages radiance bank and a dynamic radiance context learner to drive per-channel tone- mampimg functions that adaptively bridge HDR and LDR domains across time. Extensive experiments demonstrate that HDR-4DGS significantly outperforms prior methods in HDR rendering fidelity, temporal radiance consistency, and computational efficiency.
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ETHICS STATEMENT
This work is focused advancing the technical capabilities of dynamic scene reconstruction and HDR rendering from standard LDR video inputs. The proposed method, HDR-4DGS, is intended for research and non-malicious applications such virtual reality, cinematic content creation, and immersive telepresence.
The synthetic data used in experiments generated in controlled simulation environments, and the real-world scenes in our dataset were captured with the informed consent of all participants and property owners. No personally identifiable information is included in the released data. We do not anticipate direct negative societal impacts from this research; however, as with any novel view synthesis technology, potential misuse generating misleading visual content) could arise if de- ployed without appropriate safeguards. We encourage responsible use and advocate for transparency synthetic media generation.
7 REPRODUCIBILITY STATEMENT
We committed the reproducibility of HDR-4DGS. The complete code will be publicly released upon final acceptance this paper. To facilitate verification prior to code release, we provide a thorough description of our method in Sec. 3 comprehensive implementation details in Sec. and Appendix A.2 Together, these sections cover all essential components of HDR-4DGS, enabling independent replication of our results.
8 ACKNOWLEDGMENTS
This work was supported by the Key Research and Development Program of Jiangsu Province (In- dustry Foresight and Key Core Technologies) under Grant No. BE2023008-2, and by the UKRI- AHRC CoSTAR National Lab for Creative Industries Research and Development (AH/Y001060/1).
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A APPENDIX
A.1 DATASETS
This section elaborates on the HDR-4D-Syn and HDR-4D-Real datasets introduced in this work. As summarized in Tab. 7 the HDR-4D-Syn dataset is synthesized with Blender which comprises four monocular acquisition configurations (Airplane, Deer, Lego and Tank) where distinct exposure times are applied across viewpoints, and four multi-view stereo configurations (Hook, Jump, Mutant and Standup) characterized by multi-exposure LDR images capture per viewpoint. This design en- ables comprehensive evaluation of dynamic scene reconstruction under varying lighting conditions and camera setups. Notably, we explicitly use different exposure times rather than exposure value to render mxloise images, which enables precise control over radiance sampling intervals, aligning with recent paper conventions. Fig. 7sooss some example HDR images and LDR images with different exposure times of different scenes in HDR-4D-Syn.
Table 7: Statistics of HDR-4D-Syn, HDR LDR means the number of HDR / LDR images.
Training HDR
Testing HDR
Exposure Time (s)
Scenes
Cameras
Resolution
Frames
LDR
Frames
LDR
Airplane
280
280
280
70
70
70
800 800
0.125/2/32
Deer
80
20
20
20
800 800
0.125/2/32
Hook
28
280
840
40
120
800 800
0.125/2/32
Jump
21
210
630
30
90
800 800
0.125/2/32
Lego
240
240
240
60
60
60
800 800
0.125/2/32
Mutant
135
405
405
14
42
42
800 800
0.12//2/32
Standup
51
255
765
8
40
120
800 800
0.12//2/32
Tank
136
136
136
34
34
34
800x
0.12//2332
As for the HDR-4D-ReS Real dataset, ensure capture accuracy and stability, employed fixed multi-camera setup with six cameras securely mounted on tripods. Prior to introducing any dynamic elements, captured multi-view static images of the empty scene. These static images were pro- cessed using COLMAP (Schonberger & Frahm 2016) to compute precise intrinsic and extrinsic camera parameters, establishing stable and accurate spatial reference frame. Crucially, all camera positions and optical parameters were rigorously maintained throughout the entire dynamic capture sequence, climinating the need for recalibration and guaranteeing consistent geometric alignment across all timestamps. For each camera and each timestamp within the dynamic sequences, we captured three images with different exposure times (e.g., 1/120s, 1/50s, and 1/20s), enabling the subsequent synthesis of HDR ground truth. Finally, we adopt UltraFusion (Chen et al.) 2025) to synthesize the corresponding HDR ground truth from multi-exposure LDR images. Tab. summa- rizes the statistics of HDR HDDR-D-Real. and Fig. 8 shows some example HDR images and LDR images with different exposure times of different scenes.
Remark. It is noted that using identical synchronized cameras is indeed common practice in dy- namic scene capture, as evidenced by major datasets like the Neural 3D Video Dataset (Li et al.] 2022a) (18-21 synchronized cameras), Google Immersive Dataset (Broxton et al. 2020) (46-camera rig), and Technicolor Light Field Dataset (Sabater et al., (017)) (4x4 synchronized array). This ensures temporal consistency critical for dynamic reconstruction, while identical camera models eliminate radiometric calibration complexities from varying CRFs. Asynchronous capture or mixed camera setups would introduce significant additional challenges, including temporal misalignment artifacts,
approach is both a methodological convention and practical srressity-ssnchhoniizeee
Table 8: Statistics of HDR-4D-Real, HDR / LDR means the number of HDR LDR images.
Training HDR
Testing HDR
Exposure
Scenes
Cameras
Resolution
Frames
LDR
Frames
LDR
Time
Bed
22
132
317
79
79
4022
000/00017
Excavator
40
240
576
40
144
144
6
4022
0.002/0.006/0.05
033
Tank
20
40
96
20
24
24
2
4032x3024
0.007/0.02/0.0
Toys
20
40
20
24
24
4032x3024
0.008/0.02/0.1
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Published as a conference paper at ICLR 2026
0,1255
32s
Airplanc
Lego
Metant
Tank
(
(b)
(c)
(d)
(c)
Figure 7: Comparative visualization of LDR and HDR image sequences across dynamic scenes
(Airplane, Lego, Mutant, Tank). Each row corresponds distinct scene, while columns (a) (c) / (e) present LDR images captured at varying exposure times and temporal intervals. Columns (b) /
(d) (f) illustrate the HDR counterparts. ex: Exposure time.
DODE
Excovator
Figure 8: Dataset composition for dynamic scenes (Bed and Excavator) of HIDR-DD-Real Each row corresponds to a distinct scene, with columns (a)-() displaying LDR images captured at varying exposure settings but synchronized temporal instances. Columns (d) presents the corresponding HDR images at matching temporal frames. e, Exposure time.
inconsistent motion blur, and complex cross-camera radiometric calibration that could obscure the core evaluation of HDR reconstruction capabilities. While robustness such real-world variations represents valuable future work with extra challenges, this work focuses on common practice aligned with established conventions in the field.
A.2 ADDITIONAL EXPERIMENTS
Implementation details. Since Structure-frae cocture--froo-ee from-Motion (SfM) (Schonberger Frahm 2016) strug- gles to perform reliably on dynamic monocular videos, adopt different initialization strategies
17

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Published as a conference paper at ICLR 2026
PSNR 36,36
PSNR
HDDRHHexPlane
HDR-4DGS
(c) Ground Truth
Figure 9: PSNR prefers over-smooth or blurry images. HDR images are tone-mapped by Photomatix Pro (HDRsoft Team 2025
for synthetic and real-world scenes. Specifically, we randomly initialize 5 x 10 Gaussians for each synthetic scene, while for real-world scenes, we initialize the Gaussians using dense reconstructed point cloud and and the input images are downsampled by a factor of 4. In both cases, we maintain the same number of training iterations as 4DGS (Yang et al. 2024). We conducted all the experi- ments single NVIDIA RTX 4090 GPU.
Ablation study of the design of dynamic radiance context learner. The temporal radiance variation modeling constitutes a critical component of our dynamic tone mapper, as the tem- poral coherence radiance decomposition directly influences HDR rendering performance. To systematically evaluate architectural suitability, we implement the dynamic radiance context learner with multiple sequence models and quantitatively compare their effectiveness. As shown in Tab. g while Transformer-bassee modules demonstrate the- oretical advantages in long-term dependency model- ing (Wen al. 2023), our experiments reveal supe- rior performance when employing GRU. This observation aligns with recent findings in sequence
Table 9: Analysis of DRCL design.
Solchreeed 2017
25.61
0.547
0.100
ISTM aniomeeee KniTE 5019
25.51
0.615
0.106
ORU
2888
0.865
IU776
Trandomer M1
25.05
0.817
0.101
feature extraction, where GRU exhibits better efficiency in capturing local temporal patterns (Li
al. 2023).
Additional visualization results. Fig. 10 and Fig. 11 provide additional visual comparisons of
DNVS performance the HDR-4D-Syn and HDR-4D-Res datasets. It is observed that 4DGS Yang et al. 2024) consistently struggles to preserve fine-grained details and frequently exhibits structural degradation in movable regions. In contrast, our HDR-4DGS demonstrably retains perior detail fidelity, even when employing 4DGS as its underlying representation model. This outcome further corroborates the efficacy of our proposed DTM in enhancing detail preservation, as thoroughly discussed in Sec. 3.3
Fig. 12 and Fig. 13 present additional HDR DNVS rendering results on the HDR-4D-Syn and HDR-41 RD--4D-Raal datasets, respectively. Qualitative comparisons reveal that HDR-4DGS consistently preserves fine structural details and generates visually compelling renderings. In contrast, HDR- HexPlane tends to produce spatially blurred regions, while HDR-GS exhibits significant failure in reconstructing dynamic content, resulting in incomplete renderings.
Similarly, Fig. 14 and Fig. 15 illustrate the LDR DNVS results on the same datasets. 4DR-DDS demonstrates superior color fidelity and detail retention under LDR conditions. Conversely, HDR- HexPlane again suffers from noticeable blurring artifacts, and HDR-GS fails to accurately recover scene chromaticity, further compromising its ability render dynamic objects with photometric coherence.
Furthermore, accurate rendering of continuous radiance distributions in HDR DNVS is essential for preserving high-fidelity radiance variations across complex lighting conditions, which often occur in the real-world dynamic scenes. As demonstrated in Fig. 16 our HDR-4DGS with dynamic tone mapper featured by dynamic radiance context learner achieves superior temporal coherence in radiance transitions compared to baseline approaches, effectively capturing smoother details while maintaining photometric consistency. Meanwhile, Fig. 17 provides additional ablation study
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Published as a conference paper at ICLR 2026
4DGS
HDR-4DGS
Ground Truth
4DGS
HDR-4DGS
Ground Truth
Figure 10: Additional visual comparison DNVS on HDR-4D-Syn.
excavator
IGONG
tank
4DGS
HDR-4DGS
Ground Truth
Figure 11: Additional visual comparison of DNVS HDR-4D-Real.
sults of continuous radiance variation comparisons of HDR DNVS, which again, demonstrate the effectiveness of our method.
A.3 LIMITATIONS
While effective, our approach currently builds upon the existing 4DGS representation, which was not specifically designed for HDR content. promising direction for future work is to develop scene representation explicitly tailored to HDR 4D scenes which incorporats physically grounded priors or adaptive radiance bases to better capture extreme illumination variations and enforce long- range temporal coherence. Another key limitation is the use of fixed temporal context window in our dynamic tone mapper; an adaptive mechanism that modulates the receptive field based on motion magnitude radiance variance could significantly improve both computational efficiency
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Published as a conference paper at ICLR 2026
HDR-4DGS
Ground
Figure Additional visual comparison of HDR DNVS on HDR-4D-Syn.
HDR-US
HDR-HexPPlaee
HDR-4DGS
Ground Truth
Figure 13: Additional visual comparison of HDR DNVS on HDR-4D-Ren
airplane
Happlaii
HDM-HexxPlnn
HDR-IDGS
Ground
HDDR-HePPlime
HDR-IDGS
Ground Truth
lego
HDR- HID--HexPlane
Ground Truth
Planee
Ground Truth
Figure 14: Visual comparison of LDR DNVS on HDR-4D-Syn.
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Published as a conference paper at ICLR 2026
Doga
= 0.00es
HDR-G8
HDR-HoxPlane
IDDR-IGGS
Gronn Truth
0.105:
HDR-HexPlane
Figure 15: Visual comparison of LDR DNVS on HDR-4D-Real
Buse lise
HDR DDRR-DDGS
Time
Figure 16: Additional ablation study results of continuous radiance variation comparisons of HDR DNVS on HDR-4D-Syn.
and reconstruction accuracy. Moreover, when foreground and background share similar appear- ance (e.g., scene jump in HDR-4D-Syn), HDR-4DGS may exhibit suboptimal color reproduction or spatial blurring at dynamic boundaries, suggesting that explicit modeling of semantic or motion boundaries could further disambiguate dynamic content and enhance radiometric consistency across complex scene changes.
A.4 THE USAGE OF LARGE LANGUAGE MODELS (LLMs)
We clarify that the use of LLMs in this work is strictly limited to polishing the language and pre- sentation the manuscript. For instance, the original draft description of our datasets in Sec. read:
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Published as a conference paper at ICLR 2026
HDR HeexPlee
HDR 4DGS
Ground Truth
Figure 17: Additional continuous radiance variation comparisons HDR DNVS on HDR-4D-Syn.
"To address the lack of standardized benchmarks for HDR DNVS, we introduce two novel datasets HDR-4D-Syn and HDR-4D-Read The synthetic dataset,
4Dm comprising 8 synthetic dy- namic scenes, is based the dataset proposed by Wu et al. (2024a) and features videos captured under multi-cxposteee exposure settings, accompanied by synchronized multi-view LDR video se- quences, and corresponding HDR ground truth is re-synthesized. The real-world dataset, HDR- 4D-Real, consists real-world indoor dynamic scenes and videos captured under three different exposure times with six iPhone 14 Pro devices, where corresponding HDR images are obtained with UltraFusion (Chen et al. 2025)."
This draft subsequently refined for clarity, grammar, and academic version appearing in the final manuscript reflects only linguistic improvements, with no alteration to technical content factual claims.
the revised
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Reviews:
[{"review_id": "ie2oeCxPbJ", "reviewer": "Reviewer_y3DV", "summary": "This paper proposes the High Dynamic Range Dynamic Novel View Synthesis (HDR DNVS) task, aiming to reconstruct spatiotemporally consistent HDR radiance fields and dynamic geometry from sparse, time-varying LDR inputs. It introduces the HDR-4DGS framework based on Gaussian Splatting, featuring a dynamic tone-mapping module (DTM) to bridge HDR-LDR domains while maintaining temporal radiance coherence. Two datasets (HDR-4D-Syn with 8 synthetic scenes and HDR-4D-Real with 4 real indoor scenes) are constructed.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tThe DTM, with a radiance bank and dynamic radiance context learner (DRCL), maintains spatiotemporal consistency, validated by ablation studies .\n2.\tConstructs dedicated datasets (HDR-4D-Syn/Real) with HDR ground truth and time-varying 3D geometry, enabling standardized evaluation .", "weaknesses": "1.\tGiven that most existing Dynamic Novel View Synthesis (DNVS) methods operate on low dynamic range (LDR) imagery and thus fail under high-contrast or temporally varying lighting, do you consider dynamic HDR modeling essential for achieving photorealistic results in real-world dynamic scenes? In other words, is extending DNVS to HDR space a fundamental requirement to overcome the perceptual and photometric limitations of LDR-based systems, rather than just an optional enhancement?\n2.\tNo comparison with \"dynamic scene reconstruction + independent HDR module\" combinations, lacking proof of irreplaceability.\n3.\tWhen evaluating HDR-4DGS on the HDR-4D-Real dataset, the input LDR images are captured with six synchronized iPhone 14 Pro devices under three exposure times. However, real-world dynamic HDR scenarios may involve more diverse camera models (with different CRFs) or unsynchronized multi-view capture. Why is only a single type of synchronized device used for data collection, and has the method been tested for robustness to camera model differences or capture asynchrony?\n4.\tIn the ablation study on temporal context length (k=20 as optimal), the paper only tests k=5,10,20,30. However, dynamic scenes with varying motion speeds (e.g., fast-moving objects vs. slow illumination changes) may require different k values. Did the authors design experiments to verify whether the optimal k=20 is adaptive to scenes with different dynamic intensities, or is it a fixed hyperparameter that needs manual adjustment for specific scenes?\n5.\tIs the proposed method able to address issues like extreme lighting performance?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4TLRmpv9tf", "reviewer": "Reviewer_snwX", "summary": "This paper addresses the limitations of existing High Dynamic Range Novel View Synthesis (HDR NVS) methods, which are confined to static scenes, and Dynamic Novel View Synthesis (DNVS) approaches that lack HDR support. It formally introduces the task of HDR Dynamic Novel View Synthesis (HDR DNVS) for the first time.\n\nTo tackle the core challenges of spatiotemporal radiance consistency and LDR-HDR domain translation in this task, the authors propose the HDR-4DGS framework. Built on 4D Gaussian Splatting, this framework innovatively integrates a Dynamic Tone-Mapping Module (DTM). The DTM stores temporal radiance statistics via a radiance bank, captures temporal variations using a dynamic radiance context learner, and drives per-channel tone-mapping functions to enable adaptive domain translation.\n\nAdditionally, the paper constructs two benchmark datasets: HDR-4D-Syn (8 synthetic scenes) and HDR-4D-Real (4 real-world scenes), which provide HDR ground truth and multi-view LDR observations. Experiments demonstrate that HDR-4DGS outperforms existing methods in both quantitative metrics (e.g., PSNR, SSIM) and visual quality, while balancing training efficiency and inference speed, offering an effective solution for high-fidelity HDR novel view synthesis of dynamic scenes.", "questions": "same as weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed task of HDR Dynamic Novel View Synthesis (HDR DNVS) is a novel and necessary one. Existing methods either focus on static scenes for HDR Novel View Synthesis (HDR NVS) or are limited to Low Dynamic Range (LDR) inputs for Dynamic Novel View Synthesis (DNVS), failing to handle real-world dynamic scenarios with time-varying geometry, illumination, and high-contrast radiance. This gap significantly restricts practical applicability, making the introduction of HDR DNVS a timely and problem-solving initiative.\n\n2. The newly proposed Dynamic Tone-Mapping (DTM) module shows considerable merit, with a clear and logically coherent presentation in the paper. By constructing a radiance bank to store temporal radiance statistics, integrating a dynamic radiance context learner to model temporal variations, and designing per-channel tone-mapping functions, the module explicitly bridges the HDR-LDR domains while maintaining spatiotemporal radiance consistency. Its design draws reasonable inspiration from human visual adaptation, and the technical details (e.g., sliding window for context extraction, joint use of exposure time) are well-explained, making the module’s working mechanism easy to follow.\n\n3. The experimental design is comprehensive and rigorous. First, the authors build two benchmark datasets (HDR-4D-Syn with 8 synthetic scenes and HDR-4D-Real with 4 real-world scenes) that provide essential HDR ground truth and multi-view LDR data for evaluating HDR DNVS, filling the lack of existing benchmarks. Second, they compare HDR-4DGS with multiple state-of-the-art methods (e.g., HDR-NeRF, HDR-GS, HDR-HexPlane) across quantitative metrics (PSNR, SSIM, LPIPS) and qualitative visualizations. Additionally, ablation studies verify the effectiveness of the DTM module and key components (e.g., pixel-level supervision, GRU-based context learner), ensuring the reliability of the results", "weaknesses": "1.The scale of the constructed datasets appears relatively limited, which may restrict further in-depth analysis and generalization verification. Specifically, HDR-4D-Syn only includes 8 synthetic scenes and HDR-4D-Real has 4 real-world indoor scenes, with a lack of diversity in scene types (e.g., no outdoor dynamic scenes with complex natural lighting changes, or scenes with more diverse object motions). Such a small dataset not only makes it difficult to fully validate the model’s robustness across different dynamic scenarios but also cannot support additional explorations (e.g., analyzing the model’s performance under varying motion intensities or lighting contrasts). This limitation may weaken the persuasiveness of the model’s practical applicability in broader real-world contexts.\n\n2.The proposed Dynamic Tone-Mapping (DTM) module, though conceptually reasonable, seems relatively general as it lacks testing on the dataset associated with HDR-GS (a representative method for static HDR Novel View Synthesis). HDR-GS is a key baseline in the HDR NVS field, and its dataset likely contains rich HDR-LDR paired data suitable for evaluating tone-mapping performance. Without testing the DTM module on HDR-GS’s dataset, it is hard to fully confirm whether the module can effectively adapt to existing HDR-related data distributions, nor can it more comprehensively demonstrate the module’s advantages over tone-mapping components in static HDR NVS methods. This gap makes the module’s comparative advantage in the broader HDR tone-mapping context less fully validated .", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KYMHSU5EC4", "reviewer": "Reviewer_u72U", "summary": "This paper focuses on learning HDR 3D representations of dynamic scenes from LDR images. Unlike previous methods that are limited to static scenes, this work introduces a dynamic tone-mapping module to achieve adaptive color transformation. By integrating this module with the Gaussian splatting representation, the proposed approach enables HDR rendering from arbitrary viewpoints and time instances. The method is evaluated on both real and synthetic datasets, with some experiments confirming its effectiveness.", "questions": "Based on the aforementioned weaknesses, the following issues also require discussion:\n\n- In real-world scenes, as shown in Fig. 2, when using both LDR and HDR images as supervision for HDR rendering, the proposed method performs significantly worse than HDR-HexPlane in quantitative metrics. The authors should provide a clear and thorough analysis to explain this performance gap.\n\n- The paper reports strong results on synthetic data but only limited improvement on real-world datasets. Can you elaborate on whether this discrepancy arises from domain differences, noise in HDR ground truth, or instability in the tone-mapping process?\n\n- The authors could consider providing the output luminance as a function of input intensity for different time frames or varying brightness levels (i.e., a dynamic CRF). This would provide more convincing evidence that the proposed DTM has indeed learned dynamic mapping patterns, rather than relying solely on final HDR rendering metrics.\n\n[Minor]:\n- In Figure 1, the notation for the HDR and LDR models appears to be identical, which may cause confusion regarding their respective roles in the framework. And the equations shown in this figure should be formatted consistently with those in the manuscript to ensure clarity and uniform presentation.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The paper extends HDR modeling from static to dynamic scenes by proposing a novel HDR-4DGS framework.\n- The paper introduces a dynamic tone-mapping module to handle color translation in dynamic scenes, enabling the rendering of HDR images.\n- The method is evaluated on both synthetic and real datasets, and the qualitative and quantitative experiments shown in the paper suggest that it is partially effective in performing color transformations.", "weaknesses": "This work proposes a dynamic HDR scene modeling framework that extends Gaussian Splatting to handle dynamic scenes. While the method achieves improvements on part of the experimental data, it still exhibits several concerns, as summarized below：\n\n- Limited performance. As shown in the local results in Figures 2 and 3, the proposed method struggles to recover fine-texture details, and the synthesized novel views exhibit noticeable noise. The authors are encouraged to provide a more detailed analysis of these experimental results to support the effectiveness of the proposed approach better. Furthermore, based on the demo results, the method appears unable to preserve the geometry of moving objects in real-world scenes, often leading to structural loss during deformation. In addition, the DVS results show temporal flickering artifacts, indicating instability in dynamic rendering.\n\n- Limited novelty. According to the paper, the dynamic scene modeling part is adopted from 4DGS, while the per-channel tone-mapping functions adopt the structure from HDR-GS. Thus, the contribution primarily lies in combining these existing components with an additional temporal HDR learning, resulting in an improvement over prior methods that is relatively incremental.\n\n- Insufficient constraints of dynamic Gaussian deformation. As shown in Section 3.4, the method primarily supervises and optimizes the mapping between LDR and HDR domains. Still, it lacks explicit mechanisms or loss functions to control or regularize the deformation of dynamic Gaussians, which are essential for preserving the geometry and spatiotemporal consistency of moving objects. However, such constraints are crucial for dynamic scene representation and novel-view synthesis, and this aspect appears to be missing in this paper.\n\n- Some comparative experiments are missing. Since the proposed method targets dynamic scenes, whereas the comparison methods in the paper mainly focus on demonstrating HDR visual quality, the authors could first map LDR images to HDR and then compare them with dynamic NeRF and Gaussian Splatting approaches to evaluate performance in terms of geometric accuracy and spatiotemporal consistency for moving objects.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "T4wLbhQUBe", "reviewer": "Reviewer_yRau", "summary": "This paper studies a new problem – high dynamic range dynamic novel view synthesis (HDR DNVS). First of all, the authors collect a 4D synthetic datasets with 8 scenes and a 4D real datasets with 4 scenes. On the set up benchmark, the authors develop a HDR-4DGS, featuring dynamic tone-mapping for adaptively bridging the LDR and HDR domains under complex spatiotemporal variations.", "questions": "What is the difference between the designed method with HDR-GS + the k-planes in 4DGS? How about the performance of HDR-GS + the k-planes in 4DGS.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "(i) This paper studies a brand new problem - high dynamic range dynamic novel view synthesis (HDR DNVS), which has not been explore in the literature. This is novel and interesting. To this end, the authors made a dataset contribution – collecting both synthetic and real datasets to support the research. This is solid, non-trivial, and labor-intensive.\n\n(ii) The proposed biologically inspired dynamic tone-mapping module also shows deep insights. This module draws inspiration from human visual adaptation, where retinal photoreceptors dynamically adjust to ambient brightness. The proposed HDR-4DGS includes a dynamic radiance context learner that models temporal radiance distributions, as shown in Figure 1.\n\n(iii) The writing is good and easy to follow. Especially the method part, all the technical details are very clear. The presentation is also well-dressed. For example, the workflow of the overall pipeline can be clearly shown in Figure 1. In addition, the arrangement of the paper's figures and tables is very neat such as Tables 1, 2 and Figures 2, 3.\n\n(iv) The performance is very solid. As compared in Tables 1 and 2, the proposed HDR-4DGS achieves much better reconstruction quality while keeping a very fast speed. The visual comparisons in figures 2 and 3 also suggest that the proposed HDR-4DGS can render more visually pleasant results with less blur and artifacts.\n\n(v) The ablation study is also very comprehensive. The effects of dynamic tone mapper, dynamic radiance context learner, pixel-level supervision, DRCL design, and the temporal context length are discussed in details.", "weaknesses": "(i) Although the authors claim the datasets and the new algorithms are their contributions, there are no datasets or codes submitted. The reproducibility cannot be checked.\n\n(ii) The authors claim the dataset is also a contribution. But there are very few words in the paper introduce how to construct the datasets. It would better if there is a section to describe the details of data collection.\n\n(iii) As the authors claim, the designed method is a 4D Gaussian Splatting. But I did not see any preliminary or method part introduce how to adapt 4DGS to the HDR-DNVS task.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)