Mono4DGS-HDR: High Dynamic Range 4D Gaussian Splatting from Alternating-exposure Monocular Videos
Authors: Jinfeng Liu
Lingtong Kong
Mi Zhou
Jinwei Chen
Dan Xu
Venue: ICLR 2026 Poster (2026)
Keywords: High Dynamic Range
Gaussian Splatting
Monocular 4D Reconstrution
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: We introduce Mono4DGS-HDR, the first system for reconstructing renderable 4D high dynamic range (HDR) scenes from unposed monocular low dynamic range (LDR) videos captured with alternating exposures. To tackle such a challenging problem, we present a unified framework with two-stage optimization approach based on Gaussian Splatting. The first stage learns a video HDR Gaussian representation in orthographic camera coordinate space, eliminating the need for camera poses and enabling robust initial HDR video reconstruction. The second stage transforms video Gaussians into world space and jointly refines the world Gaussians with camera poses. Furthermore, we propose a temporal luminance regularization strategy to enhance the temporal consistency of the HDR appearance. Since our task has not been studied before, we construct a new evaluation benchmark using publicly available datasets for HDR video reconstruction. Extensive experiments demostrate that Mono4DGS-HDR significantly outperforms alternative solutions adapted from state-of-the-art methods in both rendering quality and speed. The project page for this paper is available at https://liujf1226.github.io/Mono4DGS-HDR.

Published as a conference paper at ICLR 2026
MoNo4DGS-HDRR HIGH DYNAMIC RANGE 4D GAU- SSIAN SPLATTING FROM ALTERNATIING-EXPOURR
MONOCULAR VIDEOS
Jinfeng Liu' Lingtong Kong" Mi Zhou² Jinwei Chen
Dan Xu
The Hong Kong University of Science and Technology (HKUST)
2 vvvo Mobile Communication Co., Ltd (jliugk, danxu) @cse. ust.hk (ltkong, zhoumi, jinwei chen @vivo.
ABSTRACT
We introduce Mono4DGS-HDDR, the first system for reconstructing renderable 4D high dynamic range (HDR) scenes from unposed monocular low dynamic range (LDR) videos captured with alternating exposures. To tackle such a challeng- ing problem, we present a unified framework with two-stage optimization ap- proach based on Gaussian Splatting. The first stage learns video HDR Gaus- sian representation in orthographic camera coordinate space, eliminating the need for camera poses and enabling robust initial HDR video reconstruction. The second stage transforms video Gaussians into world space and jointly refines the world Gaussians with camera poses. Furthermore, we propose tempo- ral luminance regularization strategy to enhance the temporal consistency of the HDR appearance. Since our task has not been studied before, we construct a evaluation benchmark using publicly available datasets for HDR video construction. Extensive experiments demonstrate that Mono4DGS HDD signifi- cantly outperforms alternative solutions adapted from state-of the-atte-rtt methods in both rendering quality and speed. The project page for this paper is available at https://liujf1122 226 http://ffiiii22222.ggigbbbo////e Sittub.io/MonooDGSS--ll
INTRODUCTION
High dynamic range novel view synthesis (HDR NVS) aims to reconstruct and render HDR scenes from multi-view low dynamic range (LDR) images captured at varying exposure levels. By lever- aging advanced scene representation techniques, such as Neural Radiance Field (NeRF) (Milden- hall et al., 2020) and Gaussian Splatting (3DGS) (Kerbl al., 2023), recent HDR NVS meth- ods (Huang al., 2022; Cai et al., 2024: Liu et al., 2025a; Wu et al., 2024a) have achieved realistic reconstruction and real-time rendering. Most of them are designed for static scenes, while HDR- Hexplane (Wu et al., 2024a) is the first investigate only validates its approach on synthetic scenes with known camera poses in a multi-camera setup. In practice, a more applicable scenario involves using single handheld camera capture HDR dynamic scenes in the wild. Therefore, this work focuses on recovering 4D HDR scenes from piternating-exposur monocular LDR videos with unknown camera parameters, demonstrated in Fig. 1(a). To the best of our knowledge, no existing method has yet explored this challenging task.
HDR NVS of dynamic scenes. However, it
Current unposed 4D reconstruction systems (Lei et al., 2025: Wang al., 2025b; Park et al., 2025) for standard monocular videos with unchanged brightness usually leverage 2D priors from vision foundation models, including tracking, depth and optical flow, to provide scene initializations and constraints. In our input case of enernating-exposure video frames, we surprisingly observe that these 2D priors can still effectively extracted (see in supplementary videos). However, simply extending these 4D reconstruction methods to HDR mode can lead to suboptimal results, as shown in Fig. 1(b). First, the 2D prior knowledge is still noisy and incomplete, leading to coarse and inaccurate scene initializations. Second, the varying brightness in the input video frames makes it infeasible to optimize camera poses through photometric reprojection error as Park et al. (2025). Moreover, the unreliable camera motion can further destabilize the recovery of scene dynamics and

---

Published as a conference paper at ICLR 2026
SplineGS-HDR
MoSca-HDR
GFlow-HDR
Altornating oxpossre Monocular
Mono4DGS-HDR
LDR
Ours staggg
Ours 12 stagel
(a) Our 4D HDR Reconstruction System
(b) HDR Visual Comparison on Skateboarder
can reconstruct high-quality 4D HDR scenes from un- posed monocular LDR videos with alternating exposures. (b) Compared to simply extending SplineGS (Park et al., 2025), MoSea (Lei et al., 2025) and GFlow (Wang et al., 2025b) to HDR mode, our approach achieves significantly better reconstruction quality.
Figure 1: (a) Our Mono4DGS-HDD
geometry, resulting in poor reconstruction quality. Finally, since direct HDR supervision is absent, the recovered HDR scene appearance may be temporally inconsistent and contain color artifacts.
To address the above-diseussed issues, we present Mono4DGS-HDD a 4D HDR reconstruction system based Gaussian Splatting, which relies a novel two-stage optimization approach to progressively refine the camera poses and HDR scene representation. In the first stage, inspired by Sa (Sun al., 2024), optimize dynamic HDR Gaussians in 3D canonical space with orthographic camera model, which can eliminate the need for camera poses, making HDR training video reconstruction easier and better. The learned HDR video Gaussian representation gives two advantages: (1) Consistent brightness among reconstructed HDR video frames enables reliable cam- era pose optimization via photometric reprojection error. (2) These video Gaussians provide good initialization for the subsequent world Gaussian optimization. We can transform the learned video Gaussians into world space by initial camera parameters from bundle adjustment (Lei et al., 2025). The world Gaussian scaling is initialized using the invariance of the projected 2D Gaussian covari- ance. the second stage, we jointly optimize camera poses and world Gaussians. The initialization from video Gaussians and the HDR photometric reprojection loss speed up the convergence and benefit the final reconstruction quality. To further enhance the temporal consistency of HDR scene appearance, propose temporal luminance regularization strategy, including flow-guided pho- tometric loss aligning per-pixel HDR irradiance between consecutive frames, which ensures the temporal stability of HDR reconstruction and rendering.
Since our task setup has not been explored before, we build a new evaluation benchmark based on publicly available datasets (Kronander et al., 2014; Froehlich al., 2014: Kalantari et al., 2013; Chen et al., 2021) for HDR video reconstruction. which contain real-world LDR videos with al- ternating exposures and synthetic HDR videos. Experiments on the datasets demonstrate that our Mono4DGS- HDR significantly outperforms alternative solutions adapted from existing 4D recon- struction systems both rendering quality and speed. Our contributions are summarized as follows:
We present Mono4DGS-HDDD the first system for reconstructing 4D HDR scenes from unposed monocular LDR videos captured with alternating exposures.
We propose unified framework with two-stage optimization procedure that learns video Gaussians the first stage, transfers the Gaussians to world space, and then optimizes world Gaussians along with camera poses in the second stage. We also conduct temporal luminance regularization to enhance HDR temporal stability.
We construct a new benchmark for evaluation and show that our approach significantly outperforms adapted alternative solutions.
2

---

Published as a conference paper at ICLR 2026
Precomputing
(b) Two-stage Gausslan Optimization any Timestamp t)
Alcmmaiing arporate Monecuder Vi00
Mideo HDR Gauaslars
2 Stage wa Poass
2
World HDR Gaussiero
gat Poons
Ditte Cam, Space
Worls Space
Transform
All Dynamic
Stadis
Dynomio
Extract Priors with Vision Foundation Models
Resterization Tone Mapping with Exposura
Carreru Puaza
Bundia Adjpeemont
Temporal Luminance
Regulanizatin
Thodenn
Gaussion Motion
Dosin Farshing
Pine Mandlanduingg
Regularizztion
from Priors
Threshoold
(2 Stage) Photomatric Reprojection
Figure 2: Overview of Mono4DGS-HDRR (a) We infer vision foundation models on the input allernating-exposure video to extract 2D priors, which provide scene initialization and regulariza- tion. (b) We propose a novel two-stage Gaussian optimization procedure, which includes video Gaussian training in the first stage, world Gaussian fine-tuning in the second stage, and video- to-world Gaussian transformation strategy. The HDR Gaussians are optimized through 2D prior supervision, Gaussian motion regularization, temporal luminance regularization and HDR photo- metric reprojection loss.
RELATED WORK
Dynamic Reconstruction and View Synthesis. Dynamic scene reconstruction and view synthesis has experienced great breakthrough by the advent of NeRF and 3DGS. Early dynamic NeRF methods usually represent the 4D scenes by time-varying NeRFs (Park et al., 2023; Gao et al., 2021; Li et al., 2022), et al., 2021a;b; Fang et al., 2022; Xian et al., 2021; Pumarola et al., 2021). Recent methods begin dynamic scenes. They model dynamic contents by
canonical space NeRF with deformation field (Guo et al., 2023; Park
to extend the efficient 3DGS representation learning Gaussian deformation (Yang et al., 2024b; Wu et al.. 2024b; Bae et al., 2024), motion trajectories (Li et al., 2024a; Lin et al., 2024; Lee et al., 2024; Yoon et al., 2025), or direct 4D Gaussian primitives (Yang et al., 2024a; Duan et al., 2024). While most of these works take as input synchronized multi-view videos make problem easier, growing body of works (Liu al., 2023; Sun al., 2024; Wang et al., 2025a; Lei et al., 2025; Wang et al., 2025b; Park et al., 2025; Stearns et al., 2024; Liu et al., 2025b) address the more practical and challenging scenario of monocular videos. Among them, RoDynRF (Liu et al., 2023), MoSca (Lei et al., 2025), SplineGS (Park al., 2025) and GFlow (Wang et al., 2025b) are notable for handling unposed monocular videos, utilizing the 2D prior knowledge of tracking, depth and optical flow. However, they are designed for standard videos with consistent brightness and struggle with the challenges posed by alternating exposures. In this work, we introduce a novel framework explicitly solve on such varying-exposiee videos.
HDR Novel View Synthesis. Existing HDR NVS approaches generally fall into two categories. The first leverages noisy RAW sensor data (Mildenhall et al., 2022: Wang et al., 2024; Jin et al., 2024; Singh et al., 2024; Li et al., 2024b), which is particularly suited for low-light or nighttime scenarios. This work is more related to the second category, which utilizes multi-exposure and multi-view LDR images for training (Huang et al.. 2022: Jun-Seong et al., 2022: Wu et al., 2024a; Cai et al.. 2024; Wu et al., 2024c; Liu et al., 2025a). HDR-NeRF (Huang al., 2022) pioneers the idea of learning HDR radiance fields by regressing HDR irradiance rather than LDR color and introduces tomp-ng MLPs to model the camera response function (CRF). Later works, including HDR-PPleoxeel (Jun- Seong et al., 2022) and HDR-GS (Cai et al., 2024), improve the efficiency by adopting alternative scene representations such as Plenoxels (Fridovich- Keil et al., 2022) and 3DGS. GaussHDR (Liu et al., 2025a) learns unified 3D and 2D local tone mapping for stable and high-quality HDR recon- struction. CasDal3DHDR (Gong et al., 2025) applies continuous- time trajectory to jointly optimize camera poses, CRF and exposure times from casually captured videos. However, most of these
3

---

Published as a conference paper at ICLR 2026
(() Video-to-world Gaussian Transformation
Dynamic Goussians
Gaussians
with transformed altributes
Postion:: C2V Transfom
Dynamic/Statio Identification
Camera to world (C2NV) Transformation
Attribute Re-fitting
Rotetion: CSW Transform
Scaling: 20 Gns
Opecity Dieetly Ichwol
Hancla Dctiutionn by Oupth Order
Covetanne Invorianoe
Color: Dineclly frtaet
(b) Transformed World Gaussians
(c) w/o Occlusion Handling
(d) w/o 2D Cov. Inv.
Figure 3: (a) Our video-world Gaussian Transformation Strategy, including dynamic/static identi- fication, attribute transformation and re-fitting. (b) Example of transformed dynamic/static world Gaussians. (c) Without occlusion handling, the dynamic/static separation is inaccurate. (d) Without 2D covariance invariance (directly inherit scaling), the world Gaussians have unreasonable scales.
methods are designed for static scenes. Although HDR-HexPlane (Wu et al., 2024a) extends this task to dynamic scenes with HexPlane (Cao & Johnson, 2023) representation, it requires known camera poses and is validated on a multi-camera setting. Unlike it, our work is the first to tackle the more practical setting of unposed monocular videos.
HDR Video Reconstruction. HDR videos can be directly collected using dedicated hardware like scanline exposure/ISO (Choi et al., 2017; Heide al., 2014) and beam splitters (Tocci al., 2011: I; McGuire et al., 2007). But they are often impractical due to their sophisticated designs and high cost. Hence, reconstructing HDR videos from Aiternative-exposure LDR videos is investigated, which aligns neighboring frames to reference frame and then merging the aligned images to HDR image (Kang et al., 2003; Kalantari et al., 2013). Later deep learning based works (Kalantari & Ramamoorthi, 2019; Chen al., 2021: Chung & Cho. 2023; Xu et al., 2024) mainly a flow network for alignment and a weight network for merging. However, the HDR data for supervised training is rare, limiting their generalization ability. Moreover, these methods training HDR videos. They can neither synthesize LDR images at new exposure levels nor support NVS. In contrast, our method reconstructs the whole 4D HDR scene in self-supervised manner, enabling novel-view rendering of both HDR videos and LDR videos with controllable exposures.
only recover
METHODOLOGY
In our monocular 4D HDR reconstruction setup, the input is an unposed monocular video consisting of alternating-expooure LDR frames ( where N, is the frame number. Consider 2-exposure case, {L1, L.....) } are captured with a short exposure time At, and La.... with long exposure be similarly extended. Our goal is to
time Atj. Other exposure patterns (c.g.. 3-exposure) recover the renderable 4D HDR scene along with unknown camera parameters through Gaussian splatting. The overview of our Mono4DGS-HDD briefly introduce the preliminaries of HDR GS and Gaussian dynamics in Sec. 3.1. Then, we detail each step and component of our system in Sec. 3.2 and Sec. 3.3.
is illustrated in Fig. 2. In the following, first
3.1 PRELIMINARIES
HDR Gaussian Splatting. HDR Gaussian Splatting (Cai et al., 2024; Liu et al., 2025a) represents an HDR scene using set of anisotropic 3D HDR Gaussians, which the same 3DGS (Kerbl et al., 2023) except replacing LDR color E [0, 1] with HDR irradiance E [0, +oo). Besides, there is logarithmic-domain tone mapper @ map the HDR irradiance to LDR color at given exposure time At, denoted as
(efloo((Att). Details about 3DGS are provided in Sec. A.I.I.
Gaussian Dynamics. Unlike those works (Wu et al., 2024b; Yang et al., 2024b; Liu et al., 2025b) that leverage deformation MLPs model temporal changes and thus slow down rendering speed, we choose to explicitly parameterize the motion of dynamic Gaussians with trajectory functions follow- ing Park al. (2025); Li et al. (2024a), which preserves the rendering speed of 3DGS and enables seamless transformation from video Gaussians to world Gaussians. Specifically, we use the cubic Hermite spline function to represent the position trajectory of each Gaussian (see in Sec. A.1.2),
4

---

Published as a conference paper at ICLR 2026
containing N: control points. For Gaussian rotation, we employ cubic polynomial function to rep- resent quaternion as r(t) مド=y where {aylay E IR [R]]] j== are polynomial coefficients. The scaling, opacity, and color of each Gaussian are assumed to be time-invariant for simplicity.
3.2 PRIOR PRECOMPUTE
Since monocular 4D reconstruction is highly ill-posed, we leverage the prior knowledge inferred by vision foundation models Park et al. (2025); Lei et al. (2025) provide scene initialization and regularization. As shown in Fig. 2(a), we use off-the-shelf foundation models to obtain: (1) Video
depth estimations (Hu al., 2025); (2) Sparse long-term 2D pixel trajectories (Xiao et al., 2024); (3) Per-frame epipolar error maps (Liu et al., 2023) computed from dense optical flow predictions (Teed & Deng, 2020), that can identify the dynamic foreground masks by thresholding. Note that in our input of alternating-exposiee the same exposure level. For video depth estimation and long-term tracklet prediction, we just feed the whole frame sequence to the models. With these priors, using static tracklets to obtain initial camera parameters as in MoSca (Lei et al., 2025).
LDR frames, we should compute the optical flow between two frames
also conduct bundle adjustment
3.3 TWO-STAGE GAUSSIAN OPTIMIZATION
Although the 2D priors can be effectively extracted from input alternating-exposurr videos, they are still noisy and insufficient, which results in coarse scene initializations. Therefore, we propose unified framework with a novel two-stage Gaussian optimization procedure, which consists of video Gaussian training in the first stage, world Gaussian fine-tuning in the second stage, and transformation strategy from video Gaussians to world Gaussians.
3.3.1 VIDEO HDR GAUSSTANS
Inspired by SaV (Sun al., 2024), learn set of fully dynamic video HDR Gaussians in ortho- graphic camera coordinate space at the first stage. For 3D point p" = = yy", =²] in this space, (1", ("","") [-1, 1|2 is actually the normalized coordinate of its projected pixel position, while 2" is the depth. In this representation, we should use an orthographic camera model for rasterization and replace the original projection Jacobian in 3DGS with Jortho. We can lift the track/depth priors to initialize video Gaussians. Please refer to Sec. A.1.3 for more details. In a word, video Gaussian representation enables us to treat camera motion and object motion uniformly as the mo- tion of dynamic Gaussians and eliminate the need of camera parameters. Consequently, LDR training frames and recover HDR training video more efficiently. The resulting HDR video Gaussians provide reliable foundation for subsequent world Gaussian and camera pose refining.
can fit
3.3.2 VIDEO-TO-WORLD GAUSSIAN TRANSFORMATION
The learned video Gaussians are in pseduo-33 3D space, which cannot represent the actual 3D geometry in the world. Thus, we design a video-to-world Gaussian transformation strategy (see in Fig. 3()),, to make the initialization from video Gaussians reasonable for further world Gaussian optimization.
Dynamic & Static Identification with Occlusion Handling. First, we need identify whether dynamic video Gaussian is static or dynamic in world space. To this end, we leverage dynamic masks M (M: N, derived from epipolar error maps, where and 0 in M, indicate dynamic and static regions, respectively. Concretely, we project each video Gaussian trajectory g" = {uilpt
N, 1 to image plane and count the occurrences N₂ when it falls into dynamic regions: [x'' yi, +f]
N₂ =
I[M.(>]' vi) (1 0.) = 1], o, = -[= > De(x", w/)),
where Z[.] is the indicator function, and o, 1 means the Gaussian is occluded time t (i.e., its depth larger than the rendered depth D.). If Na/N, is larger than pre-defined threshold (e.g.. 0.1), we consider this video Gaussian as dynamic in the world space and static otherwise.
Gaussian Position & Rotation Transform. Then, we transform the video Gaussian, with per-frame positions {n]] 1 and rotations ( (R² 1==1- into world space using the initial camera intrinsics Æ and extrinsics R1 [.) obtained from bundle adjustment. Let mg g-)) be the projection function from
5

---

Published as a conference paper at ICLR 2026
camera space image space with intrinsics K and T (-) be the inverse. We transform as Mr"" ReT (mz) (1)) + T. and Hy = R. where pe and Ry are the Gaussian position and rotation at time in world space, respectively. For static Gaussian, simply set the time- iimeiindependent world position as n
N, Σx j4t" and rotation as R AvgR( (f R (rr)e Ie=1), where Avga (+) is the rotation quaternion averaging method proposed by Markley et al. (2007). For dynamic Gaussian, we for spline function initialization and re-fit the polynomial coefficients for rotation quaternion trajectory using least squares.
re-sample control points from the world position trajectory {ut²)
J {
Gaussian Opacity& & Color Inheriting. The attributes of opacity and HDR color are directly inher- ited from the video Gaussians, no matter static or dynamic, since they are intuitively space-invariant.
Gaussian Scaling Re-fitting by 2D Covariance Invariance. Note that the scale difference be- tween camera coordinate space and world space should carefully handled. obtain initial world Gaussians with rational scales, we propose to re-fit Gaussian scaling based the invariance of 2D covariance, which is motivated by the fact that the projected 2D Gaussians should have consistent shapes and sizes before and after transformation. The projected 2D covariances of video and world Gaussians at time i, denoted as Ei and ", can be derived as:
"x = -Jorrhho I2 S" (Jortho Wt" R2 5O) J2x2, Ex [JW," " R SHh (JW," R2 5t) 1|22,, (2)
where S and S are the time-invariant scaling matrices of video and world Gaussians, and H22x means skipping the third row and column. For the viewing transformations, we have WT E (identity matrix) and W Ri,1 Now, we can obtain the initial world Gaussian scaling by solving N
the optimization problem ming En -= | E4 We apply it to both static and dynamic Gaussians, and solve it using gradient descent, that can converge within 1000 iterations in minute.
After above video-to-world Gaussian transformation, can step into the second stage to refine the world Gaussians together with camera parameters. Due to the appropriate initialization from video Gaussians, the optimization in world space can converge fast and stably.
3.3.3 OPTIMIZATION STRATEGY
We render from the fully dynamic video Gaussians in the first stage, and from the union of static and dynamic world Gaussians in the second stage. At each training iteration, we randomly sample query timestamp & (1, 21,2,.., N₂ The rendered variables at include the HDR image H,, the depth map D,, and the flow/track map Fi-ees where t' is the destination times- tamp. To obtain Fe-er we can rasterize the 3D movements of all Gaussians in camera space from and t' and project to the image plane. be then converted to LDR image L, with tone-mapping MLPs and exposure time. The following descriptions ap- ply both stages unless otherwise specified.
LDR frammess
weak supervision
effective supervision
inconsistent
The HDR rendering
F it-1
warping
Lu
valid
Supervision from 2D Priors. We supervise the scene geometry. appearance and dynamics with input LDR frames and precomputed 2D pri-
with TLR
ors, including LDR RGB loss Ligh. depth loss Ldep. flow/track loss Ltrack and unit exposure loss Lue. Please refer to Sec. L. for details.
Gaussian Motion Regularization. We also regularize the dynamic Gaussians to ensure smooth and plausible motions Lei et al. (2025); Sun et al. (2024); Wang et al. (2025a), including as eriiid-ss-possiie loss Carap velocity regularization Lyel, and acceleration regularization Lace (see in Sec. A.1.5 for details).
Figure 4: Temporal luminance regularization for temporally consistent HDR appearance.

---

Published as a conference paper at ICLR 2026
Table I: Quantitative comparisons on the test frames of Syn-Exp-3 scenes, Metrics are averaged over all scenes. LDR-OE and LDR-NE denote the LDR results with observed and novel exposures, respectively. HDR denotes the HDR results. FPS is measured at 864 x 480 resolution. f We use our initial camera parameters from bundle adjustment the required camera inputs for GaussHDR (Liu et al., 2025a) and HDR- HexPlane (Wu et al.. 2024a). & We extend SplineGS (Park et al., 2025) and MoSca (Lei et al., 2025) to HDR mode for fair comparison.
LDR-OF
LDR.NE
HDR
Training time
Method
PPS
PSNK1
SSIM
LPPSS]
PSNRT
SSIM
1.PPS
PSNK1
SSIM1
LPISS]
TAE]
GousHDR
29.51
0.858
0.167
28.96
0.363
0.165
31.25
0.891
0.105
0.089
1h
51
HDR-HexPlanef
0.806
0.189
29.60
0.155
SplinGS-HHD HDR!
17.59
0.661
0.195
16.60
0.646
0.517
17.82
0.677
0.447
1.188
1.5h
79
MoSca-HDR
0.898
0.092
0.059
Mono4DGS-BB BDDDDDGS-EDR (Ours)
34.75
0.904
0.086
34.54
0.915
0.081
37.64
0.959
0.042
0.057
1.5h
161
Temporal Luminance Regularization (TLR). The appearance supervision is only from LDR frames, which may cause unstable HDR luminance across time, especially for dynamic scenes, since dynamic Gaussians tend to float around the surfaces of moving objects. 'he supervision is effective the times when the corresponding dynamic contents are properly exposed, but it is weak when over/under-exposeed The former case leads to correctly positioned dynamic Gaussians while the latter can result in floaters above dynamic object surfaces. Consequently, the HDR appearance of dynamic contents may vary significantly at different times, shown in Fig. 4. To address this issue, we propose temporal luminance regularization using flow-guided photometric loss to align per-pixel HDR luminance between consecutive frames. Given adjacent timestamps t 1 and we can warp the HDR rendering H1-1 1 to time using optical flow, generating H1-1- Then, we can compute the loss as:
Cur = -
H1-1-1
H
(3)
H + H
where Vi-ae-1 is a valid mask derived from depth order to exclude occluded pixels. By normalizing with H, + H,, we can eliminate the influence of HDR irradiance scale. At query time I, we choose both - 1 and t + 1 as the destinations to obtain Lur- Since we cannot access optical flow priors between adjacent frames at different exposures, we use the rendered flow maps F and 1 gradients) for warping. To this end, we only apply TLR after the ending of Gaussian densification to ensure reliable flow rendering. With TLR, the learned dynamic contents well- supervised times can propagate to the poorly-supervised times, leading to temporally consistent HDR appearance, as shown in Fig. 4.
HDR Photometric Reprojection Loss. Previous works like SplineGS (Park et al., 2025) employ photometric reprojection loss Lpr (Godard et al., 2019; Liu et al., 2024b;a) in monoeular videos to optimize depth and camera poses together, which is not suitable for alternating expprsure inputs. However, we can leverage the recovered HDR training video from the first stage for this purpose. With Cpr in Sec. A.1.6, jointly refine camera poses and world Gaussians in the second stage.
Overall Loss. The overall objective is L = Argh + Aue + Adep + Atrack Ctrack + Carap + Cvel Aacehace + duteCur Xpehpr. where X's are the corresponding weights.
Gaussian Densification. We follow the same densification strategy as 3DGS for all Gaussians at both stages. In the second stage, we prune dynamic world Gaussians every certain number of itera- tions to remove those densified mistakenly. We can project dynamic Gaussian trajectories to camera coordinate space and apply the dynamic/static identification method in Sec. 3.3.2 for filtering.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets and Evaluation Metrics. Since our task has never been explored before, we create new evaluation benchmark base on publicly available datasets (Kronander et al., 2014; Frochlich et al., 2014: Kalantari et al., 2013; Chen et al., 2021) for HDR video reconstruction, resulting in 25 dy- namic scenes in total. Each scene contains an alternating-expsstie video clip with 50-100 frames. We classify them into three categories: (1) Syn-Exp-3: 9 synthetic scenes with 3 exposure levels and

---

Published as a conference paper at ICLR 2026
Table 2: Quantitative comparisons on the train frames of Real-Exp-2 scenes and the test frames of Real-Ea Real-Exp-3 scenes. Metrics averaged over all scenes. OE denotes the observed-export eepposure results. f We use our initial camera parameters from bundle adjustment as camera inputs for GaussHDR (Liu al., 2025a) and HDR-HHexPlane (Wu et al., 2024a). We extend GFlow (Wang et al., 2025b), SplineGS (Park et al., 2025) and MoSca (Lei et al.. 2025) to HDR mode for fair comparison.
Real-Exp-2 (train frames)
Real-Exp-3 (test frames)
Method
LDR-OE
LDR-OE
HDR-TAEJ
HDR-TARL
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPSL
GausHHR
24.77
0.895
0.106
0,073
24.98
0.858
0.117
0.096
HDR-HexPlame Planel
29.30
0,900
0.106
0,154
18.49
0.627
0.241
0,705
GFlow-HDR!
27.71
0.908
0.094
0.426
SplineGS-HDD
21.46
0.721
0.334
1.282
13.64
0.506
0.505
0.707
MoSca-HDR
30.28
27.23
Mono4DGS-HIDR (Ours)
31.82
0.928
0.052
0.046
27.65
0.876
0,081
0.067
region)
GaussHDR
HDR-HexPlane
MoSca-HDR
Ours
HDR/LDR
Figure 5: HDR visual comparisons on train/test frames. Our method achieves superior quality.
HDR GTs; (2) Real-Exp-3: 8 real-world scenes with 3 exposure levels; and (3) Real-Exp-2: 8 real- world scenes with exposure levels. Details about the datasets are given in Sec. A.2. For quan- titative evaluation, we utilize PSNR (higher is better). SSIM (higher is better), and LPIPS (Zhang et al., 2018) (lower is better) metrics. Following HDR-NeRF (Huang et al., 2022), we quantitatively evaluate HDR results in the μı-law (Kalantari & Ramamoorthi, 2017) domain and qualitatively show HDR results via Photomatix pro. We additionally introduce HDR-TAE (lower is better) metric to measure the temporal stability of rendered HDR videos, which is detailed in Sec. A.2.2.
Implementation Details. For prior precomputing, we utilize DepthCrafter (Hu et al., 2025) for video depth estimation, SpatialTracker (Xiao et al., 2024) for track prediction and RAFT (Teed & Deng, 2020) for optical flow estimation. Models are trained for 4K iterations in the first stage and 11K iterations the second stage. For the camera poses of test frames, we interpolate from the neighboring train frames. Please refer to Sec. A.2.3 for more details.
4.2 PERFORMANCE COMPARISON
Baselines. We compare our method with several baselines, which can be categorized into two groups: (1) HDR NVS methods, including GaussHDR (Liu al., 2025a) for static scenes and HDR- HexPlane (Wu et al., 2024a) for dynamic scenes but in multi-camera setting; (2) standard unposed 4D monocular reconstruction methods, including MoSca (Lei al., 2025), SplineGS (Park al., 2025) and GFlow ( (Wang et al., 2025b). For fair comparison, we extend the second group to HDR mode by employing HDR color and applying the same tone-mapper MLPs as ours. Note that GFlow cannot support for time interpolation of Gaussians, For the first group, since they require known camera poses, we use our initial camera parameters as their camera inputs. We train all baseline methods for the same 15K iterations ours.
we can only evaluate it on the training frames.
Quantitative & Qualitative Comparison. The quantitative results synthetic and real scenes are listed in Table 1 and Table 2, respectively. More comparisons with GFlow on training frames are provided in Appendix (Table 5). We can see that our method outperforms all baseline methods by large margin across all tracks. GaussHDR and capability of handling dynamic scenes and monocular videos, respectively. GFlow fails to recover HDR scenes since it optimizes per-frame Gaussians independently to overfit the LDR observations.
perform poorly owing to their in-
8

---

Published as a conference paper at ICLR 2026
Table Quantitative ablation results on the test frames Real-Exp-3 and Syn-Exp-3 scenes. V2W denotes the video-to- sorrodd Gaussian transformation. All experiments are trained for 15K iteration. All the metrics listed here represent PSNR except HDR-TAF.
Real-Exp-3
Syn-Exp-3
Method
LDR-OE
HDR-TAM TAE
LDR-OE
LDR-NE
HDR
HDR-TAE
(a)
w/ Video Gaussian Initialization
26.47
0.068
33.62
33.38
36.07
0.057
(b)
w/o Occlusion Handling in V2W
27.29
0.069
34.40
34.17
37.22
0.059
(c)
Covariance Invariance in
27.34
34.31
34.21
37.25
(d) HDR Photometric Reprojection Loss
27.26
0.070
34.38
34.17
37.33
0.059
(c)
w/o Temporal Luminance Regularization
27.63
0.082
34.71
34.49
37.58
0.071
(f)
Mono-DGS-HHD (Full model)
27.65
0.067
34.75
34.54
37.64
0.057
SplineGS behaves the worst since it heavily relies on the photometric reprojection loss recover camera motion, which is infeasible in the presence of internating-exposeie LDR frames. MoSca achieves the second best performance due its global Gaussian fusion ability, but still inferior to Mono4DGS-HDD
method. Overall, temporal stability, rendering quality and speed. For qualitative results, we provide HDR visual com- parisons of train/test frames in Fig. 5. More examples exhibited in Appendix (Fig. 8). We also present visual results under fx-viee--change-tine and fix-time-change-iie settings in Appendix (Fig. and Fig. 10). It be observed that our method generates higher-quality HDR renderings with finer details and fewer artifacts than baselines.
demonstrates state-of-the-an of-the-art performance in terms of
4.3 ABLATION STUDY
In this part, we conduct ablation studies to validate the effectiveness of our key designs.
Effect of Video Gaussian Initialization. To evaluate the effectiveness of the initialization from video Gaussians, Gaussian stage and directly train Gaussians in the world space, where initialize the world Gaussians with prior track and depth informa- tion. As listed in Table 3(a), the PSNR perfor- mance drops significantly with more than 1dB. Fig. 6(a) also shows noticeable visual degra- dation. These demonstrate the importance of our video Gaussian initialization. Compared to track/depth lifting, the video Gaussians pro- vide not only accurate position priors but also meaningful rotation, scaling, opacity and color priors, which greatly facilitate the subsequent world Gaussian optimization.
remove the video
Gaussion
win Occlusion Handlingin VOW
(c)
Invartance V2W
Reproj
Effect of Occlusion Handling & 2D Covari- Invariance in V2W. We further explore the role of occlusion handling and 2D covari- invariance in video-to-world Gaussian transformation by ablating them separately. As shown in Fig. ((b), the occlusion handling provides accurate dynamic/staticc separation, while 2D covariance invariance ensures that initial world Gaussians have reasonable sizes. Without the former, some static contents are mistakenly treated as dynamic, leading PSNR performance drop of 0.3dB (Table 3(b)) and blurry background reconstruction especially in the regions that have been occluded (Fig. 6(b)). Without the latter. world Gaussians are not initialized well in size, resulting in sub-optimal results, as indicated in Table 3(c) and Fig. 6(c).
[6] wio Temporal Luminanca Rug.
(f) Mone-4DGB-HDR (Full modal)
Figure 6: Qualitative ablation results.
To verify the efficacy the HDR photometric reprojection loss, we experiment by removing it in the second stage. A decrease in performance can be observed in Table 3(d) and Fig. 6(d), indicating that the dense supervision from photometric reprojection loss is beneficial for refining camera poses and scene geometry.
Effect of HDR Photometric Reprojection Loss.

---

Published as a conference paper at ICLR 2026
Table Ablation results about sampling interval the cubic Hermite spline's control points on the test frames of Real-Exp-3 and Syn-Exp-3 scenes. All experiments are trained for 15K iteration. All the metrics listed here represent PSNR except HDR-TAF.
Real Real-Exp-3
Syn-Exp-3
Sample every N, frames
LDR-OE
HDR-TAR TRR-TAE
LDR-OE
LDR-NE
HDR
HDR-TAE
27.73
0.067
34.86
34.65
37.70
0.057
N 2
27.58
0.067
34.82
34.60
37.66
0.058
N. (Ours)
27.65
0.067
34.75
34.54
37.64
0.057
N
27.26
0.068
34.16
33.95
37.03
0.058
1 16
26.75
0.068
33.67
33.47
36.34
0.058
Tracking 2D image
Tracking in 3D world
Tracking 2D image
Tracking in 3D world
Figure 7: Visualization of induced 2D/3D tracking by dynamic Gaussian motion.
Effect of Temporal Luminance Regularization. We also investigate the influence of the tempo- ral luminance regularization loss Lur by discarding it. We can see in Table 3(e) that although the reconstruction quality (PSNR) is not significantly affected, the temporal stability (TAE) greatly degraded. This means that Lar plays crucial role in stabilizing the appearance variations across frames, leading to more temporally coherent HDR videos. Fig. 6(e) also shows that without Car, the rendered result exhibits noticeable artifacts and noises in dynamic regions.
Effect of the number of control points on the Cubic Hermite Spline Trajectory. We additionally conduct ablation study the number of control points for cubic Hermite spline trajectory. Since different scenes have different video lengths, we explore on the sample interval N, of control points. As listed in Table 4, the performance starts degrade when N, more than 4. We set N, 4 in our experiments as a trade-off between performance and storing memory, since smaller N, means more control points and thus more memory cost.
4.4 VISUALIZATION OF INDUCED 2D/3D TRACKING
We visualize the induced 2D/3D tracking by the dynamic Gaussian motion of Mono4DGS-HDDR in Fig. 7. The 2D tracking is obtained by projecting the dynamic Gaussian trajectory the image plane, while the 3D tracking is directly derived from the dynamic Gaussian trajectory in the world space. The results demonstrate that our method can also induce accurate 2D/3D tracking of dynamic objects in the scene. More results are provided in the supplementary videos.
CONCLUSION
We present Mono4DGS-HDRR, the first system to tackle 4D HDR reconstruction from unposed monocular LDR videos with alternating exposures. Our novel two-stage optimization approach first learns video HDR Gaussian representation in orthographic camera coordinate space, then transforms into world space and refines the world Gaussians along with camera poses. We also propose temporal luminance regularization to ensure the temporal consistency of HDR appearance. Experiments on our new benchmark show superior results over adapted methods in both rendering quality and speed.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENTS
The research is supported in part by Early Career Scheme of the Research Grants Council (RGC) of the Hong Kong SAR under grant No. 26202321, Department of Science Technology of Shandong Province under grant No. SDST26EG01, SAIL Research Project, HKUST-Zeekr Coolaborative Research Fund, HKUST-WeBank Joint Lab Project, and Tencent Rhino-Bird Focused Research Program.
REFERENCES
Jeongmin Bae, Scoha Kim, Youngsik Yun, Hahyun Lee. Gun Bang, and Youngjung Uh. Per- gaussian embedding-basee deformation for deformable 3d gaussian splatting. In ECCV, 2024.
Yuanhao Cai, Zihao Xiao, Yixun Liang, Yulun Zhang, Xiaokang Yang, Yaoyao Liu, and Alan Yuille. Hdr-gs: Efficient high dynamic range novel view synthesis at 1000x speed via gaussian splatting. In NeurlPS, 2024.
Ang Cao and Justin Johnson. Hexplane: A fast representation for dynamic scenes. In CVPR, 2023.
Guanying Chen, Chaofeng Chen, Shi Guo, Zhetong Liang, Kwan- Yee K. Wong, and Lei Zhang. Hdr video reconstruction: A coarsc-to-fine network and a real-world benchmark dataset. In ICCV, 2021.
Inchang Choi, Seung-Hwand Back, and Min H Kim. Reconstructing interlaced high-dynamic-range video using joint learning. IEEE TIP, 26(11):5353-5366 2017.
Hacsoo Chung and Nam Ik Cho. Lan-hdr: Luminance-based alignment network for high dynamic range video reconstruction. In ICCV, 2023.
Yuanxing Duan, Fangyin Wei, Qiyu Dai, Yuhang He, Wenzheng Chen, and Baoquan Chen. 4d-rotor gaussian splatting: Towards efficient novel view synthesis for dynamic scenes. In SIGGRAPH, 2024.
Jiemin Fang, Taoran Yi, Xinggang Wang, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Matthias Nießnner and Qi Tian. Fast dynamic radiance fields with time-aware neural voxels. In SIGGRAPH Asia, 2022.
Sara Fridovich-Keil. Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, 2022.
Jan Frochlich, Stefan Grandinetti, Bernd Eberhardt, Simon Walter, Andreas Schilling, and Harald Brendel. Creating cinematic wide gamut hir-video for the evaluation of tone mapping operators and displass.. In Digital Photography X, 2014.
Chen Gao, Ayush Saraf, Johannes Kopf, and iia-Bin Huang. Dynamic view synthesis from dynamic monocular video. In ICCV, 2021.
Clément Godard, Oisin Mae Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self- supervised monocular depth estimation. CVPR, 2019.
Shucheng Gong, Lingzhe Zhao, Wenpu Li, Hong Xie, Yin Zhang, Shiyu Zhao, and Peidong Liu. Casual3dhdr: High dynamic range 3d gaussian splatting from casually-captured videos. ACM MM, 2025.
Xiang Guo, Jiadai Sun, Yuchao Dai, Guanying Chen, Xiaoqing Ye, Xiao Tan, Errui Ding, Yumeng Zhang, and Jingdong Wang. Forward flow for novel view synthesis of dynamic scenes. In ICCV, 2023.
Felix Heide, Markus Steinberger, Yun-Ta Tsai, Mushfiqur Rouf, Dawid Pajak. Dikpal Reddy, Orazio Gallo, Jing Liu, Wolfgang Heidrich, Karen Egiazarian, Jan Kautz, and Kari Pulli. Flexisp: a flexible camera image processing framework. ACM TOG, 33(6):1-13, 2014.
11

---

Published as a conference paper at ICLR 2026
Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, and Ying Shan. Deptherafter: Generating consistent long depth sequences for open-world videos. In CVPR, 2025.
Letian Huang, Dongwei Ye, Jialin Dan, Chengzhi Tao, Huiwen Liu, Kun Zhou, Bo Ren, Yuanqi Li, Yanwen Guo, and Jie Guo. Transparentgs: Fast inverse rendering of transparent objects with gaussians. In SIGGRAPHI, 2025.
Xin Huang, Qi Zhang, Ying Feng, Hongdong Li, Xuan Wang, and Qing Wang. Hdr-nerf: High dynamic range neural radiance fields. In CVPR, 2022.
Yingwengi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaoxiao Long, Wenping Wang, and Yuexin Ma. Gaussianshader: 3d gaussian splatting with shading functions for reflective surfaces. In CVPR, 2024.
Xin Jin. Pengyi Jiao, Zheng-Peng Duan, Xingchao Yang, Chun-Le Guo, Bo Ren, and Chongyi Li. Lighting every darkness with 3dgs: Fast training and real-time rendering for hdr view synthesis. In NeurlPS, 2024.
Kim Jun-Seong, Kim Yu-Ji, Moon Ye-Bin, and Tae-Hyun Oh. Hdr-plenoxels: Self-calibrating high dynamic range radiance fields. ECCV, 2022.
Nima Khademi Kalantari and Ravi Ramamoorthi. Deep high dynamic range imaging of dynamic scenes. ACM TOG, 36(4):1-12, 2017.
Nima Khademi Kalantari and Ravi Ramamoorthi. Deep hdr video from sequences with alternating exposures. In Computer Graphics Forum, 2019.
Nima Khademi Kalantari, Eli Shechtman, Connelly Barnes, Soheil Darabi, Dan B Goldman, and Pradeep Sen. Patch-based high dynamic range video. ACM TOG, 32(6):1-8, 2013.
Sing Bing Kang, Matthew Uyttendaele, Simon Winder, and Richard Szeliski. High dynamic range video. ACM TOG, 22(3):319 325, 2003.
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splat- ting for real-time radiance field rendering. ACM TOG, 42(4):1-14, 2023.
Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015.
Joel Kronander, Stefan Gustavson, Gerhard Bonnet, Anders Ynnerman, and Jonas Unger. unified framework for multi-sensor hdr video reconstruction. Signal Processing: Image Communication, 2014.
Junoh Lee, Chang Won, Hyunjun Jung, Inhwan Bae, and Hac-Gon Jeon. Fully explicit dynamic guassian splatting. In NeurlPS, 2024.
Jiahui Lei, Yijia Weng, Adam W. Harley, Leonidas Guibas, and Kostas Daniilidis. Mosca: Dynamic gaussian fusion from casual videos 4d motion scaffolds. In CVPR, 2025.
Tianye Li. Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, and Zhaoyang Lv. Dynamic view synthesis from dynamic monocular video. In CVPR, 2022.
Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. Spacetime gaussian feature splatting for real-time dynamic view synthesis. In CVPR, 2024a.
Zhihao Li, Yufei Wang, Alex Kot, and Bihan Wen. From chaos to clarity: 3dgs in the dark. In NeurIPS, 2024b.
Youtian Lin, Zuozhuo Dai, Siyu Zhu, and Yao Yao. Gaussian- floo:: 4d reconstruction with dynamic 3d gaussian particle. In CVPR, 2024.
Jinfeng Liu, Lingtong Kong, Bo Li, Zerong Wang, Hong Gu, and Jinwei Chen. Mono-vifi: A unified learning framework for self-supervised single and multi frame monocular depth estimation. In ECCV, 2024a.
12

---

Published as a conference paper at ICLR 2026
Jinfeng Liu, Lingtong Kong, Jie Yang, and Wei Liu. Towards better data exploitation in self- supervised monocular depth estimation. IEEE Robotics and Automation Letters, 9(1):663-770, 2024b.
Jinfeng Liu, LingTong Kong, Bo Li, and Dan Xu. Gausshdr: High dynamic range gaussian splatting via learning unified 3d and 2d local tone mapping. In CVPR, 2025a.
Qingming Liu, Yuan Liu, Jiepeng Wang, Xianqiang Lyu, Peng Wang, Wenping Wang, and Junhui Hou. Modgs: Dynamic gaussian splatting from casually-captured monocular videos with depth priors. In ICLR, 2025b.
Yu-Lun Liu, Chen Gao, Andreas Meuleman, Hung- Yu Tseng, Ayush Saraf, Changil Kim. Yung-Yi Yu Chuang, Johannes Kopf, and Jia-Bin Huang. Robust dynamic radiance fields. In CVPR, 2023.
Landis Markley, Yang Cheng, John Crassidis, and Yaakov Oshman. Averaging quaternions. Journal of Guidance. Control, and Dynamics, 30:1 193-1196 2007.
Morgan McGuire, Wojciech Matusik, Hanspeter Pfister, Billy Chen, John F Hughes, and Shree K Nayar. Optical splitting trees for high-pun sigh-precssion monocular imaging. IEEE Computer Graphics and Applications, 27(2):32-42, 2007.
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020.
Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualle Pratul Srinivasan, and Jonathan Bar- ron. Nerf in the dark: High dynamic range view synthesis from noisy raw images. In CVPR, 2022.
OpenAI, Josh Achiam, Steven Adler, and et al. Gpt-4 technical report. arXiv:2408.06543, 2024.
Jongmin Park, Minh-Quan Viet Bui, Juan Luis Gonzalez Bello, Jacho Moon, Jihyong Oh, and Munchurl Kim. Splinegs: Robust motion-adaptiv spline for real-time dynamic 3d gaussians from monocular video. In CVPR, 2025.
Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Mruain- Nerfies: Deformable neural radiance fields. In ICCV, 2021a.
Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Gold- man, Ricardo Martin-Brualla, and Steven M. Seitz. Hypernerf: a higher-dimensional representa- tion for topologically varying neural radiance fields. ACM TOG, 40(6):1-12, 2021b.
Sungheon Park. Minjung Son, Seokhwan Jang. Young Chun Ahn, Yeon Kim, and Nahyup Kang. Temporal interpolation is all you need for dynamic neural radiance fields. In CVPR, 2023.
Adam Paszke et al. Pytorch: An imperative style, high-performance deep learning library. In NeurlPS, 2019.
Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer, D-nerf: Neural radiance fields for dynamic scenes. In CVPR, 2021.
Shreyas Singh, Aryan Garg, and Kaushik Mitra. Hdrsplat: Gaussian splatting for high dynamic range 3d scene reconstruction from raw images. In BMVC, 2024.
Colton Stearns, Adam Harley, Mikaela Uy, Florian Dubost, Federico Tombari, Gordon Wetzstein, and Leonidas Guibas. Dynamic gaussian marbles for novel view synthesis of casual monocular videos. In SIGGRAPH Asia, 2024.
Yang-Tian Sun, Yi-Hua Huang, Lin Ma, Xiaoyang Lyu, Yan-Pei Cao, and Xiaojuan Qi. Splatter a video: Video gaussian representation for versatile processing. In NeurlPS, 2024.
Zachary Teed and Jia Deng. Raft: Recurrent all pairs field transforms for optical flow. In ECCV, 2020.
Michael Tocci, Chris Kiser, Nora Tocci, and Pradeep Sen. A versatile hdr video production system. ACM TOG, 30 2011.
13

---

Published as a conference paper at ICLR 2026
Chao Wang, Krzysztof Wolski, Bernhard Kerbl, Ana Serrano, Mojtaba Bemana, Hans-Pete Seidel, Karol Myszkowski, and Thomas Leimkühler. Cinematic gaussians: Real-time hdr radiance fields with depth of field. arXiv:2406.07.329 2024.
Qianqian Wang, Vickie Ye, Hang Gao. Weijaa Zeng, Jake Austin, Zhenggi Li, and Angjoo Kanazawa. Shape of motion: 4d reconstruction from a single video. In ICCV, 2025a.
Shizun Wang, Xingyi Yang, Qiuhong Shen, Zhenxiang Jiang, and Xinchao Wang. Gflow: Recover- ing 4d world from monocular video. In AAAI, 2025b.
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity, JEEE TIP, 13(4):600-612, 2004.
Guanjun Wu, Taoran Yi, Jiemin Fang, Wenyu Liu, and Xinggang Wang. Fast high dynamic range radiance fields for dynamic scenes. In 3DV, 2024a.
Liu, Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 4d gaussian splatting for real-time dynamic scene rendering. In CVPR, 2024b.
Jiahao Wu, Lu Xiao, Rui Peng, Kaiqiang Xiong, and Ronggang Wang. Hdrgs: High dynamic range gaussian splatting, arXiv:2303 08774, 2024c.
Renlong Wu, Zhilu Zhang, Mingyang Chen, Xiaopeng Fan, Zifei Yan, and Wangmeng Zuo. De- blur4dgs: gaussian splatting from blurry monocular video. arXiv preprint arXiv:2412.06424, 2024d.
Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim. Space-time neural irradiance fields for free-viewpoint video. In CVPR, 2021.
Yuxi Xiao, Qianqian Wang, Shangzhan Zhang, Nan Xue, Sida Peng. Yujun Shen, and Xiaowei Zhou. Spatialtracker: Tracking any 2d pixels in space. In CVPR, 2024.
Gangwei Xu, Yujin Wang, Jinwei Gu, Tianfan Xue, and Xin Yang. Hdrflow: Real-time hdr video reconstruction with large motions. In CVPR, 2024.
Honghui Yang. Di Huang, Wei Yin, Chunhua Shen, Haifeng Liu, Xiaofei He, Binbin Lin, Wanli Ouyang, and Tong He. Depth any video with scalable synthetic data. In ICLR, 2025.
Zeyu Yang, Hongye Yang, Zijie Pan, and Li Zhang. Real-time photorealistic dynamic scene repre- sentation and rendering with 4d gaussian splatting. ICLR, 2024a.
Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. In CVPR, 2024b.
Jihwan Yoon, Sangbeom Han, Jaeseok Oh, and Minsik Lee. SplineGS: Learning smooth trajectories in gaussian splatting for dynamic scene reconstruction. In ICLR, 2025.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness deep features as a perceptual metric. In CVPR, 2018
A APPENDIX
A.I ADDITIONAL METHOD DETAILS
A.1.I 3D GAUSSIAN SPLATTING
3DGS (Kerbl al., 2023) represents scene using of anisotropic 3D Gaussians, each parame-
terized by its position E IR3, rotation quaternion r E TRA,, scaling E R23 opacity a E [0, 1]. and color [0, 1}3. The spatial distribution of each Gaussian follows G(z) where x is an arbitrary 3D position and Σ = RSST R is the covariance matrix derived from the rotation matrix R (or r) and the scaling matrix S (or 8). Each 3D Gaussian G(x) is first
{*=m) x (r-r))
14

---

Published as a conference paper at ICLR 2026
Ours (full region)
GaussHDR
HDR-HexPlane
MoSca-HDR
Ours
HDR/LDR GT
Figure 8: More HDR visual comparisons on train/test frames. Our method achieves superior quality.
transformed into a 2D Gaussian G'(x) on the image plane by evaluating the 2D covariance E' [JWEWT JT] [2]22- where J is the Jacobian of the affine approximation of the projective transformation, W is the viewing transformation matrix and [=]2x2 means skipping the third row and column. Then, tile-based rasterizer sorts the 2D Gaussians and performs a-blending:
M i-1
C(u) = Σ GO
(I-1
o, = a,)
(4)
J=1
where denotes the queried pixel position and M is the number of sorted 2D Gaussians related to the queried pixel.
A.1.2 CUBIC HERMITE SPLINE INTERPOLATION
We use cubic Hermite spline function to model the motion of dynamic Gaussian. Given N₂ control points (px No, and their corresponding timestamps [tx k= for cubic Hermite spline, the position of Gaussian at time & [4,, (x+1] can be computed as:
((t)= hun (F)x + A1o (T) (tx41 t ft)mm hoi (f)pk41 + h11 (7) tw)ma+1.
t t
my { Pk+1 Pk
Pk -Pk-1
(5)
= L la
1x+1 Iv
where (t) = 27 3/² + 1, (1) = 21. + 1,, hon = 221 + 31 and h (1) = 13 -t
the Hermite basis functions, and my is the approximated tangent control point pk-
A.1.3 VIDEO GAUSSIAN REPRESENTATION
Video Gaussian representation optimizes in orthographic camera coordinate space where the video's width, height and depth correspond the X, and axes, respectively. this space, we should orthographic camera model for rasterization and replace the original projection Jacobian J
w/2 0 0)
in 3DGS with Jortho =
where w and h are the image resolution. For video
h/2
0
15

---

Published as a conference paper at ICLR 2026
Table 5: Quantitative comparisons with GFlow (Wang et al., 2025b) on the train frames of Real- Exp-3 and Syn-Exp-3 scenes. Metrics are averaged over all scenes. OE denotes goserved-exposure results. $ We extend GFlow to HDR mode for fair comparison.
Exp--Exp- train frames)
Sxp-Exp-3 (train frames)
Mothod
LDR-OE
LDR-OE
HDR
HDR-TAE]
HDR-TAEL
FSNR
SSIM
LPIPS]
PSNR
SSIM?
LPIPS]
PSNR1
SSIM'
LPIPS]
GFlow-HDR*
24.88
30.08
0.115
0.674
Mono4DGS-HBB (Ours)
30,09
0.913
0.067
0
35.18
0,908
0.082
37.97
0.960
0.040
0,057
Gaussian initialization, we can utilize the precomputed tracking and depth priors. Each tracklet will correspond to a video Gaussian. Consider a 2D track T = {Tal Te E RR with prior video depths D (Di) we can lift it to camera coordinate space
video Gaussian trajectory g" = {/4, |,, = [ny ,,,,,"=+1) ="") where (x, y(" are the normalized pixel coordinates of тr, and 2, = (T.) is the depth value pixel T₁. Then, we sample N, initial control points from g" for the spline trajectory of this dynamic video Gaussian. For Gaussian rotation quaternion, we simply initialize all polynomial coefficients with 0,00,
A.1.4 SUPERVISION FROM 2D PRIORS
LDR RGB Loss. Following 3DGS (Kerbl et al., 2023), we use combination of DSSIM (Wang al., 2004) and L1 losses to compute the image reconstruction loss between LDR rendering L, and
GT image Lt, denoted Ligh A&DSSI L.) (1 >a)l[ Lelli, where Aa 0.2.
Depth Loss. We include depth loss on the rendered depth map D, and prior depth map D.. which can be formulated as Loop = D. Dzll1-
Flow/Track Loss. To distill motion information from 2D priors to 3D Gaussians, we compute the flow/track loss (Lei et al., 2025: Wang al.. 2025a) between the rendered flow/track map F, and the prior flow/track map Fi-ee' derived as Luack = Vi 3 |i - Fi-eo Il. where Vi-+ is the valid flow mask or sparse track mask. We randomly choose supervise with flow or track loss at each iteration. For flow supervision, we set E {t Ne} where Ne the number of exposure levels (note that we only extract prior optical flow between frames at the same exposure level). For track supervision, we randomly sample 1' from the entire video sequence (except ().
Unit Exposure Loss. We follow HDR-NeRF (Huang et al., 2022) to incorporate a unit exposure loss on the logarithmic-domain tone mapper & to fix the scale of learned HDR irradiance, enabling HDR quality evaluation, expressed as Lue = [()(0) Colle. where Co 0.5 for real scenes and Co 0.73 (derived from GT CRF) for synthetic scenes.
A.1.5 GAUSSIAN MOTION REGULARIZATION
Rigidity Constraint. We enforce the werriidd-a--possiiee (or distance preserving) loss (Wang et al., 2025a; Sun et al., 2024) between dynamic Gaussians and their k-nearest neighbors to ensure the local rigidity. For each Gaussian, let μ and μe be its positions at time t and t', and Ng(p)) denote the set of k-nearest neighbors of μt, then this loss can be defined as:
Carap [distt(e, (px) dist(ue, N& )l.
(6)
where dist(..)) measures Euclidean distance. We set in our experiments.
Motion Smoothness. We apply the velocity and acceleration smoothness losses Lvel and Lace to encourage smooth motion of dynamic Gaussians. Considering a dynamic Gaussian with position μ4 and rotation matrix R2 time 1, follow MoSea (Lei et al., 2025) to obtain Lvel and Lace
N
Evel
M - pella 2/2 + [og
N₂-2
Lace
M0m 2u4+1 14+2) || log (R, (, log( Re+ 1
where || [log(-)]l= F is the Frobenius norm of rotation logarithm (the axis-angle of the rotation).
16

---

Published as a conference paper at ICLR 2026
t
22
MoSca-HDD
Ours
MoSca-HDR
Ours
Figure 9: HDR visual comparisons under fix-view-change-time setting.
A. 1.6 HDR PHOTOMETTIIC REPROJECTION LOss
The photometric reprojection loss is widely used in self-supervised monocular depth estimation (Go- dard et al., 2019) optimize depths and camera poses together. Given the query timestamp and reference timestamp f', compute the reference pixel coordinate u corresponding to the query pixel coordinate Ut as:
(8) where K is the camera intrinsics, [R [T]] and [Re [T. are the camera extrinsics at time t and t', D2(w)) is the depth value at pixel & (-) and "T (-) are the projection and unprojection func- tions. Then we sample the corresponding HDR color of He(we) in the reference frame by bilinear interpolation, denoted (u(-)))) In this way, from to and compute the HDR photometric reprojection loss as:
Urr = (Re Rt ( (ue, Di(u.) - f;) + fr))
obtain the warped HDR image Hy
Ho 11 H: + Hl
Ep - (1- M,)
where M, is the mask indicating dynamic pixels and the He H. normalization is to climinate the influence of the scale of HDR values. We sample from + 1, + 23 in the experiments. Note that use the recovered HDR training frames in the first stage for the computation of Lpr in the second stage, where we stop gradients of the leveraged HDR frames.
A.2 ADDITIONAL EXPERIMENTAL SETTINGS
A.2.1 DATASETS
Syn-Exp-3. Syn-Exe Syn-Exp-3 contains 9 synthetic HDR videos. Each video has nearly 100 frames of resolution 864 x 480. We adopt alternate frame sampling strategy, where extract every other
17

---

Published as a conference paper at ICLR 2026
view1
view2
view3
MoSca-HDI
Ours
MoSca-HDR
Ours
Figure 10: HDR visual comparisons under fix-time-change-vive setting.
frame from the original sequence. Frames with odd indices are used for training, while frames with indices reserved for testing, resulting in equal 50% split between training and testing data. The test frames are only used for quantitative evaluation and not for training, which are at novel (interpolated) views and times compared training frames. Since the GT HDR videos available, we can use them to create alternating-exposure LDR inputs. Specifically, we leverage a predefined CRF function as HDR-NeRF (Huang et al., 2022) tone-map the HDR frame to LDR frame L with a given exposure time At, which is formulated as:
HAt
- S(HA) HAI +1
(10)
where we can derive the unit exposure loss GT as Co 1(1) 0.73. this way, we generate LDR frames with 3 (observed) exposure levels for training (LDR-OEL For test frames, we additionally create LDR images at another exposure levels for novel exposure evaluation (LDR-NE).
LDR videos with 3 exposure levels. Each video has nearly 50-60 frames of resolution 864 x 480. We follow the same alternate frame sampling strategy Syn-Exp-3 to split training and testing frames. The LDR inputs directly taken from the original sequences. For testing frames, we can only evaluate on the observed exposures (LDR-OF).
Real-Exp-3. Real-Exp-3 contains 8 real-world alternating-expostie
Real-Exp-2. Real-Exp-2 contains 8 real-world alternating-exposure LDR videos with 2 exposure levels. Each video has nearly 50-60 frames of resolution 864 x 480. Since there only 2 exposures, it is inconvenient to split training and testing frames using the alternate frame sampling strategy. Therefore, we directly apply the whole video sequence for training and evaluate on all training frames at the observed exposures (LDR-OE).
A.2.2 EVALUATION METRICS
HDR-TAL TAE. Similar to the temporal alignment enror (TAE) in video depth assessment (Yang et al., 2025), introduce HDR-TAE measure the temporal consistency of rendered HDR videos in
18

---

Published as a conference paper at ICLR 2026
0.00
0.75
0103
(a) Waving Hands
0.455
2088
2M0
750
6000
10230
2000
1840
3000
1003
3000
Exd-00931000
JCC E15-00001003
10 En5-0000022
100LEF 200000
e30
6900
6.003
0.30
0:34
6:00
0:001
20/2
lauugg 3.33339
Dat Crd
Iecaand 0.000
(b) Cars
2006
4505
00/5
150n
3000
2905
180
6000
2012
112ta
205
1000
1005
5000
Exd-0.900-11
Exd-0000024
Exd-0031052
End-0.20002
0.90
1.005
0.300
6.000
0.84
6:50
1.2
6:0
0300
Figure 11: The core loss curves two scenes: WavingHands and Cars.
per-pixel manner, defined as:
N Σ
1
H Hj
H He+1
HDR-TAE
2(N,, - 1) Vi-e+1 ④
+ Vi+1-+ He Hets
H4t1-1 + H,
H W(H F H F(41-s0),
(11)
where :(:.) means backward warping. The optical flows [Fe-e+ Fit and visibility masks {Vi-r+11, V are extracted between the adjacent HDR frames H, and H441 (tone-mapped version) using RAFT (Teed & Deng, 2020) model. For real-world scenes, we utilize the rendered HDR video frames. For synthetic scenes, directly leverage the GT HDR video frames. Lower HDR-TAE indicates better temporal consistency.
A.2.3 IMPLEMENTATION DETAILS
We process on the original resolution of 864 x 480 for all videos. We threshold the epipolar error maps by 0.00001 obtain the dynamic masks. For the cubic Hermite spline trajectory of each dynamic Gaussian, we sample the control points every 4 frames. For HDR Gaussian color, we store 36-dimensional features and compute HDR color via a color MIP instead of spherical harmonics in 3DGS for stability. The color and tone-mapper MLPs consist of one hidden layer with 36 and 128 channels respectively. We follow previous works (Huang et al., 2022: Cai et al., 2024; Liu et al., 2025a) to use three different MLPs to model RGB-channel tone mappers independently. Besides, we simply adopt 2D tone mapping described in GaussHDR (Liu et al., 2025a) for stable HDR reconstruction, where we first render the HDR Gaussians to HDR image and then tone-map it to an LDR image. The loss weights are set as Anhh 1, Ame = 10, Aaep = 1, Arack 0.01, Aamap = 0.01, Xvel 10, Auce 10, Aur 0.1 and Apr 1. We optimize the first stage (fully dynamic video
19

---

Published as a conference paper at ICLR 2026
Bridge2
Fishing
Welding
Figure 12: Wide-view- rendering results on four scenes.
Table 6: Ablation results about other exposure schedules on the test frames of Syn-Exp-3 scenes. Metrics are averaged over all scenes. LDR-OE and LDR-NE denote the LDR results with observed and novel exposures, respectively. HDR denotes the HDR results.
LDR-OE
LDR-NE
HDR
Method
PSNR
SSIM
LPIPS]
PSNR1
SSIMf
LPIPS.
PSNR
SSIM1
LPIPS.
TAEl
random 3 exposure schedule
34.71
34.52
0.914
0.087
37.37
0,956
0.052
0.057
random exposure values
34.59
0.902
0.094
34.44
0.913
0.088
37.42
0.955
0.053
0.157
Ours
34.75
0.904
0.086
34.54
0.915
0.081
37.64
0.959
0.042
0.057
Gaussians) for 4K iterations, with 3K iterations of Gaussian densification where we densify every 200 steps and reset opacities every 1K steps. The second stage (static and dynamic world Gaussians) is optimized for 11K iterations with 8K iterations Gaussian densification. For static Gaussians, densify every 400 steps and reset opacities every 2K steps. For dynamic Gaussians, we densify every 200 steps, reset opacities every 2K steps and remove mistakenly densified Gaussians every 2K steps. In both stages, the temporal luminance regularization loss Lue is applied after the end of Gaussian densification. The learning rates of static Gaussian attributes are same as 3DGS (Kerbl following
al., 2023). The learning rates of dynamic Gaussian attributes and camera parameters MoSca (Lei et al., 2025). The learning rate of tone-mapper and color MLPs is set to 0.0005. All experiments are conducted with Adam optimizer (Kingma & Ba, 2015) using Pytorch (Paszke al., 2019) on single RTX 3090 GPU.
A.3 ADDITIONAL EXPERIMENTAL RESULTS
A.3.1 PERFORMANCE COMPARISONS
We provide more comparison results with GFlow (Wang et al., 2025b) training frames of Real- Exp-3 and Syn-Exp-3 scenes in Table 5. We also present more HDR visual comparisons on train/test frames Fig. 8, and the results under Ex-view-chhane-timg and fix-time-change-iee -ieww settings in Fig. 9 and Fig. 10, respectively. All these results demonstrate again the superiority of our method.
A.3.2 LOSS CURVES
We present the loss curves for two scenes. WavingHands and Cars, as illustrated in Fig. 11. Due to the resetting of Gaussian opacity, certain losses, such as RGB loss, depth loss, and track loss, may exhibit sudden increases at the resetting iterations. For the TLR loss, it is applied only after the completion of Gaussian densification, so it appears during the later training stage. Despite the presence of multiple losses, they function collaboratively and converge effectively. The stability of the optimization process is attributed to several factors. First, we utilize precomputed 2D priors to guide the optimization, providing effective initializations and constraints for geometry and motion. Second, we implement a two-stage optimization strategy with video Gaussian initialization, which enhances the stability and efficiency of world Gaussian optimization.
20

---

Published as a conference paper at ICLR 2026
Bridge
Cars
Hallway
Ours
$2.490
[-0.25, 0.25] EV
120
[-0.5, 0.55 EV
7.8
[-0.75, 0.75]
Figure 13: Recovered CRFs under different random exposure perturbation ranges.
A.3.3 WIDE-VIEW- VIEW--EE- RENDERING
As we do not possess ground truth HDR/LDR images under significant view changes, are unable quantitatively evaluate performance. However, we present visual results under wide view changes in Fig. 12 for four scenes. Please refer to our supplementary videos for more results. Although some artifacts are present, our method maintains reasonable geometric and photometric consistency under wide view changes.
A.3.4 MORE ABLATION RESULTS
Other Exposure Schedules. In our work, we primarily utilize alternating-expe exxoosure video data, as this type of data is convenient to capture and commonly employed in existing HDR video recon- struction studies (Xu et al., 2024; Chung Cho, 2023). Additionally, we explore other exposure schedules, including random (non-periodic) 3-exposure schedule and random exposure values. To facilitate this, we regenerate the training LDR frames from the HDR ground truths the Syn-Exp-3 scenes. For the random 3-exposure schedule, we retain the original three exposure values but ran- domly shuffle their order within the training video sequence. For random exposure values, we assign exposure value each training frame random. In both cases, utilize the original expo- sure values for the testing frames. The results are summarized in Table 6, where we observe that our method continues to perform well under these exposure schedules. The PSNR and SSIM metrics
21

---

Published as a conference paper at ICLR 2026
Table 7: Ablation results about different random exposure perturbation ranges on the test frames of Syn-Exp-3 scenes. Metrics are averaged over all scenes. LDR-OE and LDR-NE denote the LDR results with observed and novel exposures, respectively. HDR denotes the HDR results.
LDR-OB
LDR-NE
HDRR
Random Perturbation
PSNRT
SSIM
LPIPS
PSNRT
SSIM
LPIPS.
PSNR1
SSIMI
LPIPS
TAE.
-0.75, 0.75] EV -0.5, 0.5] EV
31.37
0.896
0.094
31.32
0,908
0.089
34.32
0.953
0.048
0.060
33.31
0.901
0.091
33.21
0.912
0.086
35.55
0.957
0.046
0.058
00225,0255 EV
34.27
0.902
0.083
34.13
0.913
0.083
37.28
0.958
0.044
0.058
Ours
34.75
0.904
0.086
34.54
0.915
0,081
37.64
0.959
0.042
0.057
Table Ablation results about depth and flow/track losses on the test frames of Syn-Exp-3 3 scenes. Metrics are averaged over all scenes. LDRROE and LDR-NE denote the LDR results with observed and novel exposures, respectively. HDR denotes the HDR results.
LDR-OE
LDR-NE
HDR
Method
PSNR1
SSIM
LPIPS!
PSNR1
SSIM
LPIPS
PSNR1
SSIM
LPIPS|
TAEL
w/o depth loss
33.63
0.897
0.095
33.55
0.908
0.090
35.56
0.953
0.054
0.058
w/o track/flow loss
33.43
0.896
0.097
33.23
0.909
0.092
35.05
0,952
0.052
0.058
Ours
34.75
0.904
0.086
34.54
0.915
0.081
37.64
0,959
0.042
0.057
exhibit only slight reductions, while the drop in the LPIPS metric is more pronounced. This larger decline can be attributed to less accurate optical flow estimation under these exposure schedules, as the flow model is applied to two frames with differing exposures. Although the flow model demon- strates some robustness to exposure changes due to brightness augmentation during its pretraining, it may still encounter difficulties with substantial exposure differences, leading less accurate flow priors and, consequently, poorer LPIPS results.
Sensitivity to Inaccuracies of Exposure Values. In our work, read from camera metadata and treat the camera response function (CRF) as unknown and optimiz- able. As depicted in Fig. 13, our learned tone mappers can well approximate the ground truth CRF curves with knowing the exact exposure values. Here, we investigate the sensitivity of our method to the inaccuracies of exposure values. Specifically, we randomly perturb the input exposure val- ues with different perturbation ranges during training. As listed in Table 7, our method is robust to small exposure perturbations (e-g.. [-0.25, 0.25) EV) with a slight performance drop. However, larger perturbations lead to more significant performance degradation, which aligns with intuitive expectations. We can also see the recovered CRF curves under different perturbation ranges in Fig, 13, where larger exposure perturbations result in poorer CRF fitting. Notably, exposure values can typically be accurately extracted from camera metadata in real-world applications, affirming the practicality of our method.
the known exposure values
Influence of Depth/Flow/Tracc Priors. The depth/ftow/track priors are extracted by vision foun- dation models, which may contain some noise inherently. Here, also explore the performance of our method when these priors are absent, i.e., without depth loss or flow/track loss. We provide the ablation results Syn-Exp-3 scenes in Table 8. We can see that the performance drops lot without these losses, demonstrating the importance of the priors for high-quality reconstruction.
A.4 CHALLENGING CONDITIONS
Our experimental datasets encompass challenging scenarios, including scenes with low-light condi- tions, non-Lambertian (refiective/raaspaaent)) surfaces, and fast or complex motions, as illustrated in Fig. 14. Some of these scenes are also presented in our supplementary videos. Although our method is designed for general HDR reconstruction from varying-exposure LDR frames and does not specifically address these difficult conditions, it still produces reasonable results. To further enhance performance in such challenging settings, we can incorporate specialized techniques from existing literature.
In low-light scenes, such as those depicted in Fig. 14(a)(b), which are often characterized by inherent noise, operating in the RAW domain offers significant advantages. This is evidenced by studies such as RawNeRF (Mildenhall et al., 2022) and LE3D (Jin et al., 2024), which are specifically designed for low-light or dark environments. While our current framework processes alternating-
22

---

Published as a conference paper at ICLR 2026
(a) Low-light. Outdoor
(c) Transperent Glass
Reflective Glass
Fast Mation
(b) Low-light, Indoor
(d) Transparent Glass
(f) Reflective Lake Surface
(h) Complex Motion
Figure 14: Several challenging conditions that are involved our experimental datasets.
LDR GT
Prior Optical Flow
Dynamic Mask
Dynamic Caussians
(a) Inaccurate Optical Flow Textureless Regions Causing Erroneous Dynamic/Static Separation
LDR GT
HDR Rendering
LDR GT
LDR Rendering
(b) Motion Blur
(c) Non-rigid Motion
Figure 15: Several typical failure cases of Mono4DGS-HDR.
exposure videos, can easily combine underlying scene representation with RAW-domain color representation to better manage low-light scenarios.
For transparent surfaces in Fig. 14(c)(d) and reflective surfaces in Fig. 14(e)(f), we can integrate reflection/refraction modeling as GaussianShader (Jiang et al., 2024) and TransparentGS (Huang et al., 2025). Since framework adopts Gaussian Splatting the scene representation, such modeling can be readily incorporated to improve the handling of these complex surface properties.
In the fast-motion scene depicted in Fig. 14(g), the captured LDR frames often exhibit motion blur. Consequently, regions affected by blur tend to yield similarly blurry reconstructions due to the pixel- level photometric supervision employed. Nevertheless, the overall reconstruction quality remains acceptable. Although deblurring is not the primary focus of our approach, deblurring techniques such as Deblur4DGS (Wu et al., 2024d) could be seamlessly integrated to enable simultaneous deblurring and HDR reconstruction.
We also include scene, Skateboarder, with complex motion, as shown in Fig. 14(h). This scene con- tains several types of motion, including a skateboarder moving rapidly nearby, pedestrians walking slowly in the distance, and flowers swaying in the wind. Additional results for this scene are avail- able in our supplementary videos, which demonstrate that our method effectively manages complex motion. This capability can be attributed to our dynamic Gaussian representation and video Gaus- sian initialization. The former enables each dynamic Gaussian to possess its own motion trajectory, thereby effectively modeling diverse motion patterns within the scene. The latter facilitates the mo- tion learning of dynamic Gaussians by mitigating the interference of camera motion during the video Gaussian stage.
A.5 LIMITATIONS
Although our method achieves superior performance on monocular 4D HDR reconstruction, it still has some limitations. First, our method relies the quality of 2D priors (depth, flow and track)
23

---

Published as a conference paper at ICLR 2026
to certain extent. Inaccurate priors may lead to suboptimal results. For example, erroneous depth priors can cause incorrect scene geometry, while inaccurate flow/track priors can misguide the mo- tion of dynamic Gaussians. Besides, the dynamic masks (epipolar error maps) derived from optical flow priors may also not be perfect, which can affect the separation of static and dynamic world Gaussians. Second, method cannot handle the image blur caused by fast camera/object motion. Luckily, we observe that coping with blurry monocular videos in Gaussian splatting has been dis- cussed in Deblur4DGS (Wu al., 2024d) and Casual3DHDR (Gong et al., 2025), which provide possibilities to achieve deblurring and HDR reconstruction simultaneously. Third, Our method fails model the complex non-rigid motion due to the as-rigid-as-pon poosssible (ARAP) constraint, where we do not consider non-rigid deformation of dynamic objects.
We also present qualitative examples Fig. 15 that illustrate the typical failure cases mentioned above, including: (a) erroneous dynamic/static separation due to inaccurate optical flow estima- tion; (b) motion blur resulting from rapid camera or object motion; and (c) non-rigid deformation of burning fire. Although these limitations exist, they do not detract from core contribution of introducing Mono4DGS-HDR for general monocular 4D HDR reconstruction. Our method demon- strates superior performance compared to existing approaches and performs well in most scenarios, as evidenced by extensive quantitative and qualitative results. Furthermore, the identified limitations offer valuable directions for future research.
A.6 ETHICS STATEMENT
This research methodology does not raise any ethical issues. No human subjects were involved.
A.7 REPRODUCIBILITY STATEMENT
Our Mono4DGS HDR is built by integrating the publicly available codebase of 3DGS (Kerbl et al., 2023), MoSca (Lei et al., 2025), SaV (Sun et al., 2024), GaussHDR (Liu et al., 2025a), Spatial- Tracker (Xiao et al., 2024), DepthCrafter (Hu et al., 2025). and RAFT (Teed & Deng, 2020). We create our evaluation benchmark based on the publicly available datasets (Kronander et al., 2014; Froehlich et al., 2014; Kalantari et al., 2013; Chen et al., 2021) for HDR video reconstruction. In this paper, we include comprehensive data preprocessing and implementation details, which greatly facilitate reproducing our work. We will release our code and processed data if this work is accepted.
A.8 LLM USAGE
We use the LLMs including GPT-4 (OpenAl et al., 2024) to help polish the writing of the paper.
24


Reviews:
[{"review_id": "Jh6pOCXOmJ", "reviewer": "Reviewer_ww4e", "summary": "HDR-4DGS tackles 4D HDR reconstruction from unposed monocular LDR videos with alternating exposures. This problem hasn't been tackled exactly before. Their temporal regularization and 2 stage training to model the world shows superior performance qualitatively and quantitatively over adapted baselines.", "questions": "1. Just curious as to why GaussHDR tends to remove the foreground object altogether. Any insights?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The problem setting is well defined and motivated.\n2. The paper is well written and clear.\n3. The evaluations done are adequate, both quantitatively and qualitatively.\n4. The paper comprehensively ablates all the design features showing the importance/visual effect of each modification.", "weaknesses": "1. How does the optmization/loss curves look like with that many losses? Would it be possible to show the curves?\n2. Are all scenes at 24-30fps? Have the authors tried any more challenging settings like faster motion (ex - moving cars for autonomous driving applications?), non-lambertian surfaces etc? Those would be nice to haves but not necessary of course.", "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": "6QugcjVlmj", "reviewer": "Reviewer_GHAo", "summary": "HDR-4DGS presents a well-motivated approach for reconstructing renderable 4D HDR scenes from unposed monocular LDR videos with alternating exposures. The authors propose a two-stage Gaussian Splatting optimization framework, where dynamic HDR video Gaussians are first learned in orthographic camera space and then transformed to world space for joint optimization with camera poses. The method is complemented by temporal luminance regularization, ensuring temporal consistency of HDR appearance. The experimental evaluation is thorough, including both synthetic and real-world datasets, and demonstrates that HDR-4DGS outperforms  state-of-the-art methods.", "questions": "I would like to know how the proposed method performs when dealing with scenes that involve extremely fast and complex motion, where motion blur and ambiguity are present.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper presents the first system to address 4D HDR reconstruction from unposed, single-camera, alternating-exposure LDR videos.\n\n2. The proposed two-stage optimization is effective.\n\n3. HDR-4DGS effectively handles varying brightness across frames, which would break conventional photometric reprojection losses.\n\n4. The paper constructs a new benchmark for HDR video reconstruction including real and synthetic scenes.", "weaknesses": "1. Although HDR reconstruction is the core contribution, the paper primarily evaluates PSNR/SSIM on tone-mapped images. No HDR-specific metrics (e.g., PQ-PSNR, HDR-VDP).\n\n2. While quantitative results are extensive, the paper provides limited qualitative discussion on typical failure cases.\n\n3. The approach has not been evaluated on low-light, reflective, or transparent surfaces, which may limit applicability in certain real-world conditions.", "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": "m5lHjQwQe1", "reviewer": "Reviewer_J4n4", "summary": "This paper proposes HDR-4DGS, a two-stage framework for reconstructing HDR scenes from monocular alternating exposure videos. Unlike previous works that handle dynamic Gaussians implicitly, this method explicitly parameterizes the motion of Gaussians, enabling more accurate transformation of video Gaussians into the world coordinate system. Additionally, various optimization losses are introduced to ensure high-quality final rendering.", "questions": "One of my concerns is that if there is a large viewpoint difference between two frames, then when warping from frame t-1 to frame t, some regions may appear black because certain content in frame t-1 is not visible from the viewpoint of frame t. Would these regions affect the supervision of the TLR loss?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Explicitly parameterizing the motion of Gaussians not only improves rendering quality but also maintains a relatively fast rendering speed.\n2. The invariance of 2D Gaussian covariance serves as a simple yet effective tool introduced by the authors, which is validated through ablation studies in the paper.\n3. The entire paper is clear and easy to understand.", "weaknesses": "1. The division between dynamic and static regions relies on epipolar error maps, so the final results are heavily influenced by them.\n2. The selection of dynamic Gaussians depends on threshold settings, which reduces the generalizability of the pipeline, as determining appropriate thresholds for each scene is not straightforward.", "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": "BQE3Lq53Jl", "reviewer": "Reviewer_vqUK", "summary": "This paper introduces a system for reconstructing 4D HDR scenes from alternating-exposure monocular LDR videos without known camera poses. The method builds on 3D Gaussian Splatting and proposes a two-stage optimization framework: \n- A video-space stage, which learns dynamic HDR Gaussians in orthographic camera coordinates, eliminating the need for camera poses.\n- A world-space stage, which transforms and refines these Gaussians jointly with camera poses using HDR photometric reprojection.\nAdditionally, a Temporal Luminance Regularization is introduced to ensure temporal consistency of HDR appearance.", "questions": "- How sensitive is the method to inaccuracies in the exposure timing or camera response function estimation?\n\n- Could the framework generalize to arbitrary (non-periodic) exposure sequences?\n\n- How large is the performance drop if vision priors (depth/flow) are noisy or absent?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "-  it is the first to handle alternating-exposure monocular HDR reconstruction.\n\n- Well-designed two-stage optimization, effectively bridging unposed monocular input to HDR Gaussian representation.\n\n- Good experiment results, outperforming both 3DGS- and NeRF-based HDR methods in quality and speed, and comprehensive ablation studies, validating each component’s contribution.", "weaknesses": "- Dependence on multiple vision foundation models (DepthCrafter, RAFT, etc.) makes the pipeline complex and may limit real-time applicability.\n\n- The approach assumes alternating exposure patterns; performance under random or adaptive exposure schedules is not analyzed.\n\n- Large novel view rendering is not demonstrated. It remains unclear whether the reconstructed HDR scenes maintain geometric and photometric consistency under wide view changes", "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)