ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
Authors: Michal Nazarczuk
Sibi Catley-Chandar
Thomas Tanay
Zhensong Zhang
Gregory Slabaugh
Eduardo Pérez-Pellitero
Venue: NeurIPS 2025 poster ()
Keywords: monocular reconstruction
diffusion guidance
Primary area: applications

Abstract: Dynamic Novel View Synthesis aims to generate photorealistic views of moving subjects from arbitrary viewpoints. This task is particularly challenging when relying on monocular video, where disentangling structure from motion is ill-posed and supervision is scarce. We introduce Video Diffusion-Aware Reconstruction (ViDAR), a novel 4D reconstruction framework that leverages personalised diffusion models to synthesise a pseudo multi-view supervision signal for training a Gaussian splatting representation. By conditioning on scene-specific features, ViDAR recovers fine-grained appearance details while mitigating artefacts introduced by monocular ambiguity. To address the spatio-temporal inconsistency of diffusion-based supervision, we propose a diffusion-aware loss function and a camera pose optimisation strategy that aligns synthetic views with the underlying scene geometry. Experiments on DyCheck, a challenging benchmark with extreme viewpoint variation, show that ViDAR outperforms all state-of-the-art baselines in visual quality and geometric consistency. We further highlight ViDAR’s strong improvement over baselines on dynamic regions and provide a new benchmark to compare performance in reconstructing motion-rich parts of the scene.

ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
Michal !azarezuk
Sibi Catley-Chandar
Thomas Tanay' 1
Zhensong Zhang!
Gregory Slabaugh²
Eduardo Pérez-Pellitero
Huawei Noah's ' Ark Lab
Queen Mary University of London
MoSca
ViDAR
Input
Novel View Synthesis
MoSca
VIDAR
Figure 1: ViDAR provides novel framework for Monocular Novel View Synthesis utilising diffusion-awan reconstruction framework.
Abstract
Dynamic Novel View Synthesis aims to generate photorealistic views of moving subjects from arbitrary viewpoints. This task is particularly challenging when relying on monocular video, where disentangling structure from motion is ill-posed and supervision is scarce. We introduce Video Diffusion-Aware Reconstruction (ViDAR), a novel 4D reconstruction framework that leverages personalised image diffusion models to synthesise pseudo multi-view supervision signals for training a Gaussian splatting representation. By conditioning on scene-specific features, ViDAR recovers fine-grained appearance details while mitigating artefacts intro- duced by monocular ambiguity. To address the spatio-tempore inconsistency of diffusion-based supervision, we propose diffusion-awee loss function and a camera pose optimisation strategy that aligns synthetic views with the underlying scene geometry. Experiments on DyCheck, challenging benchmark with extreme viewpoint variation, show that ViDAR outperforms all state-of-the-art baselines in visual quality and geometric consistency. We further highlight ViDAR's strong improvement over baselines dynamic regions and provide new benchmark to compare performance in reconstructing motion-rich parts of the scene. Project page: httts://vidar.. github. io/
"qqual contribution
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
4D reconstruction from monocular inputs is a challenging problem where the goal is to recover a 3D representation of a dynamic scene, It is increasingly important for modelling, comprehending, and interacting with the physical world and supports wide range of downstream applications, ranging from augmented reality to generating data for training robust AI models [57].
Casually captured monocular videos are ubiquitous, however reconstructing 3D structure from them remains an inherently ill-posed problem. Static regions of the scene can typically be reconstructed well due to effective multi-view capture [3)- However for dynamic regions, depth information is not directly observable from a single viewpoint; in other words, it is difficult to disentangle the motion of the camera from motion within the scene. To mitigate this ambiguity many existing approaches impose strong regularisation 7 30; 58) in the form of geometric assumptions, such as
the object's rigidity, that constrains the dynamics of the scene. Others 13 25 46 65 66] leverage learned priors, particularly those derived from large-scale models (e.g. monocular depth), to guide the achieve geometrically compact scene representations, they often fall short in rendering high-quality, photorealistic appearances. Conversely recent generative approaches utilise powerful diffusion models to achieve higher visual quality in 67 and monocular reconstruction [51] but struggle to maintain spatio-temporal coherence, limiting their applicability in scenarios that demand accurate spatial reconstruction and temporal consistency, particularly in dynamic, real-world settings.
reconstruction. While regularization based methods [33 46
tasks such as single image to 3D (21 23 24
53
54
To tackle these challenges, we present Video Diffusion-Aware 4D Reconstruction (ViDAR), monocular video reconstruction approach that leverages diffusion models as powerful appearance priors through a novel diffusion -affus aware reconstruction framework, which allows for improving visual fidelity without the loss of spatio-ter opatio-temporal consistency. We first train a monocular reconstruction baseline and generate a set of typically degraded multi-view images by sampling diverse camera poses and rendering the novel viewpoints. We then adopt DreamBooth-styyle personalisation strategy [37], and tailor a pretrained diffusion model the input video, which we use as generative enhancer to inject rich visual information back into the degraded renders. This effectively generates a set of high-fidelity pseudo-multt multi-view observations for our scene, although due to the nature of the diffusion process, the resulting images are not necessarily spatially consistent. We observe that naively using these views as supervision leads to reconstructions degraded by artefacts and geometric inconsistencies. To mitigate this, we propose a method of diffusion-aware reconstruction, which selectively applies cittusion-based guidance to dynamic regions of the scene while jointly optimising the camera poses associated with the diffused views.
To the best of our knowledge, ViDAR is the first approach to incorporate a diffusion prior into monocular video reconstruction in a geometrically consistent manner. We demonstrate substan- tially improved qualitative and quantitative results compared to existing techniques (see Tabs. D Figs. 4. highlighting the effectiveness of diffusion-guidee supervision when integrated with reconstruction pipeline that accounts for geometric consistency.
2
We summarise our contributions as follows:
1. A personalised diffusion enhancement strategy that improves appearance quality by refining newly sampled renderings using a greeamootth-adapeee model.
2. A diffusion-aware reconstruction framework that combines synamic-region focused dif- fusion guidance with joint optimisation of the sampled camera poses for geometrically consistent reconstruction.
foguon-
3. An extensive experimental evaluation, including both quantitative and qualitative compar- isons with prior work, the introduction of a dynamic-region specific benchmark, as well as ablation studies isolating the impact of each component.
2 Related work
4D reconstruction Advances in novel view synthesis include the introduction of two seminal
reconstruction paradigms, namely Neural Radiance Fields (NeRF) (2 and 3D Gaussian Splatting (3DGS) [1I]. These developments in static scene reconstruction were quickly followed by several works on dynamic content. NeRF-based methods for video reconstruction include D-NeRF (35), StreamRF (1)), HexPlane
(2) K-Planes [3) Tensor4D (38), Mix Voxels [45). Similarly, Gaussian

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Diffusion-Aware Reconstruction
Honotular
Parsonalised Diffusion Model
Sampled camera posa optimisation
Monocular 4D Reconstruction
Soem "> Casmmple
Novel View Enhancement
Dynamic reconstruction optimisttoo
Lignn &Cuu
Novel camera samping
Figure 2: A high-level overview of ViDAR. The input video used to create a 4D reconstruction with monocular approach. Further, novel camera views are sampled and enhanced with personalised diffusion model for each scene. This constitutes a set of pseudo-mults multi-view supervision examples. Finally, our approach optimises the 4D representation with the use of original video and new multi- view cues, in a diffusion-awarr manner.
Splatting developments enabled research on dynamic novel view synthesis. Multi-view videos were reconstructed by: GaussianFlow [22] 4DGS [49], STG [18), SWinGS [3)] Ex4DGS [2].
Monocular reconstruction The task of 4D monocular video reconstruction can seen as a special case of 4D reconstruction under substantially more challenging conditions. This is due to the problem often being ill-posed: many of the target object surfaces may be seen only from one viewpoint through- out the video. Notably, among NeRF-based approaches, NSFF [6] proposes time varying flow field, whereas Nerfies (3.. HyperNeRF [32), DyCheck (T-NeRF) [5], DyBluRF [. RoDynRF [26).
CTNeRF (8 use canonical representation with time-dependent deformation. DynIBaR 17 uses
Image Based Rendering for reconstruction. With Gaussian Splatting advancements, Dynamic 3D
Gaussians 27 learn explicit motion of every Gaussian, whereas 4DGS 49]. Deformable 3DGS [58].
SC-GS 7 use a deformation field for transformation from canonical space. SplineGS 30 constrains
the motion of Gaussians to splines ensure temporal smoothness. DynPoint (65) and MotionGS (66)
use an optical flow estimator for additional supervision. PGDVS (64) and BTTimer [19 propose transformer-based approach for generalisable reconstruction. Dynamic Gaussian Marbles 42
strategy to merge sets of Gaussians and create long trajectories, and restrict representation isotropic Gaussians. MoDGS (25) improves the supervision from depth priors. D-NPC [9] proposes the use of neural implicit point cloud as the representation for monocular reconstruction. MoSca [33 and Shape of Motion [46) both utilise priors from pretrained foundational models (depth, optical flow, 2D tracking). Similarly, they both reconstruct static and dynamic content separately, and describe the motion of the Gaussians with lower dimensionality basis functions.
adopt divide-and-conge
Diffusion enhanced reconstruction Several recent approaches explore the use of diffusion models guide the reconstruction. ReconFusion 52 trains diffusion model on set of object images, and uses it to score the quality of sparse reconstruction, guiding it with RGB loss. DpDy [44) uses Score Distillation Sampling (SDS) [34] supervise reconstruction with the use of image and depth diffusion model. CAT4D [51]. uses mideo-diffusin model to generate additional static cameras for the input video, followed by the reconstruction process. Difix3D+ [50) proposes generalisable enhancement diffusion model to improve reconstruction quality. MVGD (6) proposes a direct rendering of novel views and depth as a conditional generative task. Other diffusion- approaches include text or a
Notably, our approach uses a monocular video as an input, and uses a personalised diffusion model along with diffusion-aware reconstruction for accurate geometry modelling.
single image to 3D generation
14 20 21 23
48
53
54
55
59
61
62
67
3 Method
Our method incorporates several stages which can be seen in Figure Firstly, we use a monocular reconstruction baseline to obtain 4D representation of the scene (Sec. 3.1 and generate set of
3

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degraded multi-view renders from sampled novel camera poses. Next, we personalise a diffusion model using the input video (S2c. which is used enhance the degraded renders (Scc...2.1). Finally, we use the new set of enhanced pseudo-multi-vee -ulti-view images to supervise and refine the 41) representation of the scene (Sec. 3.3). diffusion-awar manner.
3.1 Monocular Reconstruction
Given a casual monocular video of a dynamic scene with T frames I [11, I2....... we perform the initial reconstruction of the scene using an off-the-shelf 4D monocular reconstruction method, specifically, we use MoSca [3] in our implementation. The method reconstructs two sets of Gaussians for the given scene, namely static Gaussians Gs, and dynamic Gaussians Ga, that together create the scene representation: g = Ga U g.. MoSca leverages several priors in the reconstruction process: depth, optical flow, 2D tracking. Firstly, the optical flow is used to estimate the epipolar error and to determine the likelihood of image regions belonging to dynamic static content. This is followed by the joint reconstruction of the static part of the scene G. and fine-tuning of the input camera pose Comp. With that, scaffold, a low dimensionality motion representation, is built through lifting 2D tracklets belonging to dynamic regions into 3D using depth information. Finally, a photometric reconstruction is performed to optimise the scene G, enabling rendering of novel views R. of the scene.
3.1.1 Track Anything Gaussian Classification
We note that the epipolar error analysis introduced by MoSca for classification of dynamic parts of the image leads to occurrences of floater artefacts due to the inclusion of background among dynamic Gaussians. This may not be reflected heavily in quantitative performance, but leads to a decrease in the quality of generated pseudo-multi-view samples /Sec.332.1I.. To improve the constraint on dynamic Gaussians' locations, we use dynamic masks D, obtained from Track Anything [56] to reconstruct the static part of the scene G. and generate motion scaffolds (as in MoSca [3]
3.2 Diffusion Enhancement
We utilise a Stable Diffusion (36] model, specifically the pretrained Stable Diffusion XI. (SDXL) 33 to improve the quality of rendered images and guide the reconstruction process. Following the observations of ReconFusion [52], we decide to use a multistep denoising process, in contrast to Score Distillation Sampling 34] Conversely, given sampled image Rm, from camera Cm. the time f, we follow a standard text-to-image (36) process and encode the image into latent space: To E E (Rm.). Further, instead of generating the noisy latent for image generation, we introduce k steps of noise into the image-sourced latent zo - Th using the original noise scheduler (here, Discrete Euler [0)). We then follow the denoising process for & steps to achieve a denoised latent do which is then decoded to an enhanced version of the input image: Em,t= D (fo).
Personalisation Similarly to some of the recent reconstruction approaches, e.g. Wang et al. 44 we apply the Dreambooth [37] fine-tuning approach to the SDXL model. To this end, we treat an input video collection of images and fine-tune the diffusion model for a given scene such that a specific text token triggers the model to follow the appearance of the scene.
3.2.1 View Sampling and Rendering Enhancement
Given the scene-personalised diffusion model, we utilise the previously trained monocular recon- struction to generate set of gecud-muuti--ieet ground truth images. Firstly, we sample M sets of images for each timestep E [0, T]. effectively adding M new cameras with parameters om where E [0, M]] and Cm Csample. where Csample constitutes a set of new camera trajectories. To this end, we select two existing views (as a camera position and rotation): a random one, and challenging view (with the furthest distance from the mcan), and sample a new view as their weighted linear combination. To introduce variety in the difficulty of the sampled views, we gradually increase the blending weight of the views towards the most challenging ones from the input trajectory. Simultaneously, we introduce noise of an increasing amplitude to the new camera poses.
Thereafter, we use our trained monocular reconstruction to render set of M new camera views Rm, for each timestep. Further, we use our personalised diffusion model to enhance the rendered images Rm, - Em,t- This constitutes set of supervision images in multi-view setting. We have

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chosen to generate whole multi-view dataset in single step instead of performing the enhancement on-the-fly. This enables the samples to be reused and reduces the computational demands (especially on GPU memory).
3.3 Diffusion-Aware Reconstruction
We use our generated dataset {Em.e} as additional supervision to re-train our 4D monocular recon- struction method to predict higher quality output L.... However using these sampled views for training is challenging. The outputs E of our personalised video diffusion models are high-fidelity and also preserve structure and coarse geometry, but due the nature of the diffusion process and random noise schedule, they are not ppatio-temporall and texture. This manifests flickering and shifts of textures between consecutive frames. In some cases, coarse geometry may also be hallucinated, e.g. in novel viewpoints not seen during training. If naively used these outputs supervise monocular 4D) reconstruction, the lack of spatio-temporal consistency in the training data would cause the model to either converge to a mean radiance value and cause blurry renderings, or overfit individual frames and learn a temporally inconsistent reconstruction. We propose the following mechanisms to overcome these challenges.
consistent at the level of fine-grained detail
3.3.1 Dynamic Reconstruction
While dynamic regions of scene are oodeerdbsserved static regions may be captured from multiple viewpoints across time, effectively creating multi-view supervision. Hence supervision is unnecessary in static regions and in fact could cause the quality to decrease, particularly if spatially inconsistent. We compute a mask of the dynamic regions of the scene Dın,t using Track Anything [56], and
apply this mask to our data to mask out the static regions E = Em, 3 Dm,t, where C denotes
element-wiss multiplication, and also to our predicted output idym im,t D,,,t- This ensures that only the dynamic regions of the scene are supervised by our generated data, which reduces the convergence to the mean effect in the static reconstruction and reduces floaters. For dynamic regions, we introduce perceptual loss [63] to encourage our reconstruction to be texturally rich and reduce blur caused by training on spatially misaligned pseudo-GTss During training we compute the loss as Ldgn = |E 1dd idym 1 + Xpl Edun jdgn logg + A₂ Eddn idym
Issim where |-| is the L1 loss, Hegg is the perceptual loss using a pretrained VGG network (41). Heeim is the SSIM [47] loss and Xp and X are hyperparameters set to 0.1. The dynamic loss, Laym. is applied in addition to the default losses from the monocular reconstruction method, and is backpropagated to update G and Cimp-
3.3.2 Sampled Camera Pose Optimisation
Camera poses of casually captured monocular videos are typically noisy due to the difficulty of disentangling scene motion from camera motion, thus the need to optimise Ciop in many monocular reconstruction methods [13 46). Our sampled camera poses Csample
interpolated from Coup and so are also noisy. As our pseudo-Gil corresponding to Sampte are not always spatially consistent, it is even more difficult to disentangle scene motion from camera motion. To compensate for this, is necessary to optimise our sampled camera poses during training to ensure the pseudo-GTTs are aligned with the underlying scene geometry. However unlike dynamic reconstruction (Sec. (.... where we only use the dynamic masked region for supervision, we use the entire image Er, as supervision for sampled camera pose optimisation. Despite fine-grained textural flickering, the coarse structure present in static regions provides a more consistent supervision signal for localisation than using only dynamic regions. We compute the loss as Leam Em, - Im,el + Xp Em, mm,el - X₂ Am|Em, - Im,,lesim- The camera loss, Leam, is backpropagated separately other losses and only updates Cyampie:
4 Results
4.1
Datasets
We evaluate the performance of ViDAR on the DyCheck dataset [5]. DyCheck was introduced as a real world benchmark for evaluating monocular to 4D methods and is extremely challenging: the
5

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Table 1: Quantitative results co-visibility masked regions of scenes from the DyCheck (iPhone) dataset. Best, second and third results are highlighted in red, orange and yellow respectively. SoM-5 is full-res with wheel and space-out excluded.
Method
PSNR-m
SSIM-mt
LPIPS-m +
T-NeRF 5
16.96
0.5772
0.3789
NSFF [66
15.46
0.5510
0,3960
Nerfies B1
16.45
0.5699
0.3389
HyperNeRF
16.81
0.5693
0.3319
4DGS [4
13.64
0,4280
Half-Res
PGDVS 64
15.88
0.5480
0,3400
DynPoint 65
16.89
0,5730
DyBluRF
17.37
0,5910
0,3730
D-NPC p
16.41
0,5820
0.3190
RoDynRF [26
17.10
0,5340
0.5170
Gaussian Marbles 42
16.02
0,5416
0.3398
SoM [46
18.62
0.6820
0,2382
MoSca 13
19.32
0.7060
0.2640
Ours
19.69
0.7126
0.2231
Gaussian Marbles 42
15.84
0.5434
0.5681
Full-Res
SoM [6
17.98
0.6422
0.3718
MoSca
B
18.44
0.6560
0,4193
Ours
19.00
0.6672
0.3623
T-NeRF 5
15.60
0.5500
0.5500
HyperNeRF
15.99
0,5900
0.5
DynlBaR 17
13.41
0.4800
0.5500
SoM.5
Gaussian Marbles
16.03
0.5425
0.5807
SoM [46
16.72
0.6300
0,4500
CAT4D
17.39
0.6070
0.3410
MoSca 13
18.34
0.6636
0,4321
Ours
18.76
0.6751
0.3774
Table Quantitative results on dynamic regions scenes from the DyCheck (iPhone) dataset. Best, second and third results are highlighted in red, orange and yellow respectively. SoM-5 is full-res with wheel and space-out excluded.
Method
PSNR-D1
SSIM-D+
LPIPS-D +
T-NeRF
13.86
0.8546
0.3491
Half-Res
Nerfies
31
12.89
0.8425
0.3811
HyperNeRF
13.27
0.8484
0.3558
Gaussian Marbles
9.99
0.8175
0.3926
SoM [46]
14.80
0.8582
0.3008
MoSca
13
15.63
0.8755
0.2904
Ours
16.46
0.8850
0.2793
Gaussian Marbles 42
12.75
0.8607
0.5058
Full-Res
SoM 46
14.82
0.8709
0.4347
MoSca 13
15.39
0.8821
0,4413
Ours
16.32
0.8893
0.3921
Gaussian Marbles 42
13.66
0.8658
0.4919
SoM-5
SoM [46]
12.50
0.8648
0,4890
MoSca
15.83
0.8872
0.4404
Ours
16.69
0.8941
0.3778

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Table 3: Quantitative results on NVIDIA dataset, full image and dynamic region included. Best, second and third results are highlighted in red, orange and yellow respectively.
Method
PSNR
SSIM
LPIPS
PSNR-Dt
SSIM-D+
LPIPS-D4
DynNeRF 4
26.10
0,8370
0.0798
19.90
0,9536
0.1268
RoDynRF 26
25.89
0.8538
0.0648
19.59
0,9544
0.1280
NSFF 16
24.33
0.7511
0.1939
19.51
0,9522
0.1928
MoSca
26.72
0.8507
0.0708
21.16
0,9405
0.1238
Ours
27.20
0.8610
0.0613
21.35
0.9613
0.1059
Table 4: Intersection of co-visibility mask with dynamic regions with respect to co-visibility mask area
Scene
Dyn/Co-vis Intersection (%)
apple
4.42
block
27.46
paper-windmill
3.58
space-out
20.63
spin
19.76
teddy
81.33
Co-visibility mask
Dynamic mask
wheel
24.65
Figure 3: An example of co-visibility and dynamic mask comparison.
mean
25.97
test views are far away from training views, camera poses are often inaccurate, depths are noisy and training views have issues such as overexposure and autofocus. The dataset consists of 14 casually captured scenes,
which have no ground truth test views and are used for qualitative evaluation only and with test views available. Due the difficulty of obtaining accurate camera poses for all scenes, some methods choose to quantitatively evaluate on only discard 'space-out" and 'wheel'. To our knowledge, this is currently the widely used benchmark which is the most appropriate for evaluating our method. As described in DyCheck [5], other datasets such Nerfies [31] HyperNeRF (32) and NSFF [16] suffer from teleporting cameras which makes them effectively multi- view. We quantitatively and qualitatively evaluate our method and other state-of-the-an baselines across all 14 scenes. In addition, provide evaluation of ViDAR the NVIDIA dataset [60]. Notably, is a forward-facin capture with small-baseline static cameras, thus significantly easier than DyCheck.
available scenes and
4.2 Metrics
compute PSNR, SSIM and LPIPS the co-visibility masked regions of the test views, which we denote with an -m addendum to each metric. We compute metrics both half-resolution and full-resolution, and following [46). also report results subset of scenes which we label SoM-5. For the NVIDIA dataset we follow the setup of MoSca proposed in RoDynRF (26).
Following previous works 5 13
4.2.1 Limitations of Metrics and A New Benchmark
We note that the static regions of scene are often observed from several viewpoints across different time steps in the captured monocular video. This effectively provides multi-view supervision for these regions, and although we are interested reconstructing the entire scene which includes the static regions, the dynamic regions are arguably the area of most interest and also the most under-observed. In order to better evaluate performance in the dynamic regions of the scene, we compute set of dynamic masks for each scene using Track Anything [56]. We compute the intersection between the co- co-visibility masks and the dynamic regions of the scene and present results Table 4 We find that on average only 26% of the co-visibility masked pixels correspond to the dynamic region. Some scenes such as apple and paper- windmill have an intersection as low as 4%. We show an example of this in Figure 3 The covvibibilyty masked metrics are heavily weighted towards the static regions of

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T-NeRF S
Gaussian Marbles
Shape Of Motion (46
MoSca [B]
Ours
GT
Figure 4: Qualitative evaluation of method against benchmark methods on the DyCheck test set.

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DynNeRF 4
NSFF 16
RoDynRF 26
MoSca 13
Ours
GT
Figure 5: Qualitative evaluation our method against benchmark methods on the NVIDIA test set.
Table Quantitative results of ablation study of the components of ViDAR.
Method
PSNR-m m
SSIM-m
LPIPS-m m
Ours
19.00
0.6672
0.3623
W/o Tracking Based Gaussian Classification (TGS)
18.88
0.6651
0.3693
W/o Sampled Camera Optimisation (SO)
18.39
0.6514
0.4040
W/o Dynamic Reconstruction (DR)
18.93
0.6274
0.4497
W/o SO DR TGS
18.46
0.6075
0.4656
W/o diffusion (TGS only)
18.65
0.6596
0,4075
MoSca
18.44
0.6560
0,4193
scene. Although this useful for evaluating overall reconstruction performance, underweights the reconstruction performance of methods in the most difficult dynamic regions. We provide complementary new benchmark for the evaluation monocular to 4D reconstruction methods, where computed dynamic masks be used in place of the commonly used co- co-visibility masks. We use these masks to compute the PSNR, SSIM and LPIPS, which we denote with a addendum, for range of baseline methods in Table 2
4.3 Evaluation
Baselines We compare against a wide range of baselines, including a number of recent state-of the- art methods such MoSca [13], CAT4D [51], Shape Of Motion [46), Dynamic Gaussian Marbles
[2 and 4DGS 44), which are based upon Gaussian Splatting [1]. We also compare against NeRF.
based approaches T-NeRF (5), Nerfies [1], HyperNeRF (32. DyBluRF [1] and RoDynRF (26).
neural point clouds approaches DynPoint (65 and D-NPC 19. generalized pre-trained transformer
PGDVS (64), neural scene flow NSFF [6 and volumetric image-based rendering DynlBaR [17]
Quantitative and Qualitative Evaluation We present quantitative results of our method in Tables I and 2 Our method outperforms all state-of-the-art basclines in PSNR and SSIM and all but one in LPIPS, across all settings and resolutions. We typically improve PSNR by large margin, achieving minimum of 1dB improvement over all methods, except for MoSca where we average 0.94dB and 0.56dB higher in dynamic and co-visibility masked regions respectively. This indicates our method particularly improves dynamic region reconstruction. We note that CAT4D achieves
W/o SO/DR/TGS
W/o DR
W/o SO
W/o TGS
Ours
GT
Figure 6: Qualitative evaluation of our ablation study with settings corresponding to Tab.

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Table Quantitative results of an ablation study of the number of input frames used to personalise the diffusion model.
Method
PSNR-m
SSIM-m
LPIPS-m
PSNR-D
SSIM-D
LPIPS-D
3 frames
18.40
0.6577
0.3731
15,40
0.8832
0,4165
frames
18.51
0.6590
0.3714
15.59
0.8849
0,4132
Full sequence
19.00
0.6672
0.3623
16.32
0.8941
0.3921
lower LPIPS score than our method, but the improved perceptual quality comes at the cost of reduced spatio-temporal consistency, which is reflected in the PSNR and SSIM scores, and also clearly shown in our supplementary video. In Table we present quantitative results of ViDAR the NVIDIA dataset [60]. Even though our method was designed to tackle highly ill ill-posed settings, it outperforms other methods this simpler, forward-facing dataset. Similarly to DyCheck, observe improvements both in full image and dynamic regions evaluations. We present qualitative evaluation in Figure 4 (DyCheck) and Figure where ViDAR demonstrates consistently superior visual quality and geometric consistency when compared to the best existing approaches. Although 2D image comparisons are indicative of performance, encourage viewing our supplementary video results to appreciate the improvement in spatio-temporal consistency and visual quality over baselines. Moreover, we run ViDAR on the in-the-wild videos. We refer the reader to the Supplementary Material, where we present qualitative examples and no-reference metrics (VBench [8]) regarding this experiment. Similarly, the Supplementary Material shows the no-reference metrics evaluated on DyCheck dataset, indicating high temporal consistency of ViDAR outputs when compared to the state of the
Ablations Contributions We quantitatively evaluate each of our contributions in ablation study presented in T The penultimate row, w/o diffusion isolates the contribution of Tracking Based Gaussian Classification. The row w/o SO + DR TGS shows a naive approach of using the diffused novel views directly as supervision for our monocular baseline without diffusion-aware reconstruction. Due to the spatio-temporal inconsistencies of the diffused outputs, this leads poor quality reconstruction, as shown in Figure We show that removing dynamic reconstruction leads to blurry reconstruction static regions, while removing sampled camera optimization leads to geometric inconsistencies. We also show that using our tracking based Gaussian classification reduces floaters. This ablation emphasises that all of the proposed contributions are important in achieving the final rendering quality.
Ablations Input Video We also evaluate ViDAR in a scarce input setting. Instead of using a full input video sequence, we select 3 or 6 equally spaced frames in time (mimicking sparse static approaches) to train the personalised diffusion model. We use such enhancement on the full video sequence keeping all the other experiment settings unchanged from our full pipeline. We present the quantitative analysis in Tablelo] We observe an increase in performance across all metrics with the increased number of frames saturating when using the whole video. Notably, both 3 and 6 frame setting provide an improvement upon baseline MoSca. With a lower number of frames, fine-tuning the personalised diffusion model can be performed quicker, thus providing tradeoff between training speed and rendering quality.
5 Conclusion
We present ViDAR, novel method for 4D reconstruction of scenes from monocular inputs. ViDAR leverages image diffusion models by conditioning on scene-specific features to recover fine-grained appearance details of novel viewpoints. ViDAR overcomes the spatio-temporal inconsistency of diffusion-basee supervision via a diffusion-awee strategy. We show that ViDAR outperforms all state-of of-e-of-the-art baselines on the challening DyCheck dataset as well as the NVIDIA dataset, and we present a new benchmark to evaluate performance in dynamic regions.
loss function and a camera pose optimisation
Limitations: ViDAR limits the scope of diffusion to enhancing rendered images, which are limited by the initial accuracy of the 4D reconstruction, thus, cannot repair major geometrical artefacts.
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21

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Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
ViDAR:
Supplementary Material
A Additional Results
In this section, we include additional qualitative and quantitative evaluation of ViDAR.
A.1 Further Qualitative Evaluation
We present additional qualitaative evaluation of ViDAR compared to MoSca and Shape Of Motion on the qualitative example scenes from the DyCheck dataset in Fig. 7 Our results show consistently greater geometric consistency and visual quality compared to the other approaches.
A.2 Per-Scene Results
We provide a detailed quantitative evaluation for every scene of the DyCheck dataset in Tables 7 8 in half and full resolution respectively. As in Tables[]] LPIPS on the co-visibility masked regions of the test views, which we denote with an -m addendum each metric, as well as on the dynamic masked regions the test views which denote with a -D. With a few exceptions (e.g. Apple, co-visibility), ViDAR is consistently the best performing method. Similarly, we show detailed results of the NVIDIA dataset in Table
compute PSNR, SSIM and
A.3 In-the-wild videos
Finally, we evaluated ViDAR on set of in-the-wild videos proposed by MoSca. Table 10 presents selection of non-reference video quality metrics comparing the input sequence against MoSca and
ViDAR. The metrics were proposed by a comprehensive video quality assessment suite VBench and consist of Subject Consistency, Background Consistency, Motion Smoothness, Aesthetic Quality, Imaging Quality. Further, the qualitative examples the scenes are shown in Figure
A.4 Ablation generalisable enhancement
In addition, we test our pipeline with the use of generalisable diffusion model for enhancement. To this end, we substitute the personalised diffusion model with a generalisable one proposed in Difix3D++ Further, we enhance the mutli-view supervision images in the original experiment and keep the rest of the pipeline intact. We use two available settings of Difix3D+, no-reference enhancement, and reference one, where we provide a frame from the input video as the enhancement guidance. We show the results of this ablation in Table This indicates that the use personalised diffusion provides samples more suitable for the reconstruction in the proposed pipeline.
A.5 DyCheck video consistency metrics
In Table 12
provide no-reference video consistency metrics the DyCheck dataset. The results indicate that while raw diffusion output is characterised by low temporal consistency, ViDAR is capable of utilising strong support for ambiguous geometry from diffusion outputs in order produce high-quality and temporally consistent results.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Table 7: Per-scene quantitative evaluation of ViDAR against state-of-the-art methods the DyCheck dataset at half resolution. Best, second and third results are highlighted in red, orange and yellow respectively.
Method
PSNR m
SSIM-mt
LPIPS-m4 m
PSNR-D1
SSIM-D+
LPIPS-D4
T-NeRF
5
17.43
0.7285
0.5081
13.63
0,9433
0.3108
Nerfies
17.54
0.7505
0.4785
13.63
0.9437
0.3321
Apple
HyperNeRF
32
17.64
0.7433
0,4775
13.36
0,9417
0.3362
Gaussian Marbles
17.90
0.7328
0.4716
14.56
0,9370
0.3229
SoM 46
18.95
0.8111
0.2917
14.88
0,9419
0.3016
MoSca 13
19.40
0.8074
0.3392
16.97
0.9580
0.2176
Ours
19.18
0.8008
0.3149
17.75
0.9616
0.1893
T-NeRF [5]
17.52
0.6688
0.3460
14.07
0.8591
0.3474
Nerfies 33
16.61
0,6393
0.3893
13.31
0,8433
0.4068
Block
HyperNeRF
32
17.54
0.6702
0.3312
13.73
0,8525
0.3483
Gaussian Marbles
42
16.95
0.6509
0.3788
13.81
0.8480
0.3750
SoM |6
17.99
0.6634
0.2608
15.23
0.8596
0.3378
MoSca
R3
18.11
0.6801
0.3416
15.32
0.8699
0.3499
Ours
18.91
0.6901
0.2168
15.93
0.8864
0.3052
T-NeRF 5
17.55
0,3672
0,2577
12.50
0.9730
0.3149
Nerfies
17.34
0.3783
0.2111
10.84
0.9710
0.3929
HyperNeRF
32
17.38
0.3819
0,2086
10.64
0.9706
0.4040
Paper
Gaussian Marbles
42
16.62
0.3219
0.3517
11.68
0.9710
0.2680
So. [6
20.85
0.6725
0.1536
12.90
0.9738
0.2634
MoSca
13
22.24
0.7450
0.1617
14.32
0.9789
0.2832
Ours
22.48
0.7516
0.1287
15.58
0.9807
0,3080
T-NeRF 5
17.71
0.5914
0.3768
14.52
0.8620
0.3649
Nerfies
17.79
0.6217
0.3032
13.85
0.8578
0.3407
Space-oul
HyperNeRF
32
17.93
0.6054
0.3203
14.26
0.8597
0.3379
Gaussian Marbles
15.32
0.5512
0.4235
11.15
0.8488
0.4278
SoM 46
19.64
0.6313
0.2374
15.72
0.8805
0.2429
MoSca
13
20.35
0.6582
0,2702
17.57
0.8985
0.2073
Ours
21.58
0.6890
0,2010
18.77
0.9091
0.2306
T-NeRF 5
19.16
0.5672
0.4427
15.65
0.9090
0.3394
Nerfies 31
18.38
0,5846
0.3087
14.11
0.8899
0.3868
HyperNeRF
32
19.20
0.5614
0.3254
15.65
0.9060
0.3230
Spin
Gaussian Marbles
42
18.31
0.5461
0.3558
15.68
0.8963
0.3950
SoM 46
21.05
0.7798
0.1698
18.44
0.9180
0.2815
MoSca
13
21.06
0.7100
0.2084
18.85
0.9240
0.2688
Ours
21.28
0.7209
0.1853
19.37
0.9327
0.2566
T-NeRF
13.71
0.5695
0.4286
13.49
0.6198
0,3730
Nerfies
31
13.65
0.5572
0.3716
13.33
0,6050
0.3280
HyperNeRF
32
13.97
0,5678
0.3498
13.65
0.6168
0.3028
Teddy
Gaussian Marbles
22
13.65
0.5432
0.4369
13.29
0.5972
0.3961
SoM [6
14.00
0.5462
0.3399
13.68
0.5998
0,3335
MoSca
13
15.09
0.6133
0.3587
14.84
0.6679
0.3146
Ours
15.97
0.6416
0.3096
15.62
0.6844
0.2901
T-NeRF 5
15.65
0.5481
0.2925
13.19
0.8162
0.3937
Nerfies
13.82
0.4580
0.3097
11.15
0.7870
0.4805
Wheel
HyperNeRF
32
13.99
0,4550
0.3102
11.60
0.7913
0.4385
Gaussian Marbles
42
16.02
0.5416
0.3398
9.99
0.8175
0,3926
SoM 46
17.86
0.6695
0.2144
12.75
0,8338
0,3446
MoSca
17.95
0.6852
0.2309
11.57
0.8310
0.3918
Ours
18.46
0.6943
0.2058
12.23
0.8402
0.3754
23

---

Input
Shape Of Motion [46]
MoSca 13
Ours
Figure Qualitative evaluation of our method against benchmark methods on the DyCheck qualitative example set.

---

Table 8: Per-scene quantitative evaluation of ViDAR against state-of th--th--rttt methods on the DyCheck dataset at full resolution. Best, second and third results are highlighted in red. orange and yellow respectively.
Method
PSNR-m f
SSIM-mt
LPIPS-m+
PSNR-D1
SSIM-D+
LPIPS-D4
Gaussian Marbles 42
16.84
0.7022
0.6849
14.65
0.9495
0.4281
Apple
So.M [46]
17.74
0.7535
0.4946
14.88
0.9540
0.4036
MoSca
13
18.19
0.7486
0.5651
17.21
0.9681
0.3073
Ours
18.02
0.7466
0.5359
18.07
0.9694
0.2559
Gaussian Marbles
42
16.50
0.6492
0.5065
13.49
0.8660
0.4987
Block
SoM [46
17.42
0.6566
0.3879
14.60
0.8759
0.4667
MoSca
13
17.56
0.6710
0.4658
14.97
0.8848
0.4808
Ours
18.43
0.6722
0,3932
15.54
0.8898
0.4100
Gaussian Marbles
15.96
0.2959
0,5778
11.30
0.9722
0.5094
Paper
SoM 46
19.65
0.5518
0.2035
13.03
0.9737
0.4861
MoSca
13
20.82
0.6289
0.2412
14.04
0.9770
0.5498
Ours
21.06
0.6477
0.1923
14.99
0.9777
0.5003
Space-out
Gaussian Marbles
42
15.19
0.5603
0.5434
11.07
0.8671
0.5334
SoM 46
19.54
0.6178
0.3667
16.11
0,8939
0.3363
MoSca
19.93
0.6280
0.4060
17.17
0.8988
0.3207
Ours
21.15
0.6443
0.3278
18.56
0.9079
0.3356
Gaussian Marbles
42
17.84
0.5154
0.4784
15.79
0,9143
0.4420
Spin
So. 46
20.57
0.7323
0,2663
18.65
0.9339
0.3535
MoSca 11
20.61
0.6769
0.3108
18.50
0.9371
0.3657
Ours
20.69
0.6851
0.2868
19.37
0.9445
0.3094
Gaussian Marbles
13.01
0.5496
0.6559
13.05
0.6269
0.5815
Teddy
SoM 46
13.58
0.5518
0.5377
13.40
0.6251
0.4928
MoSca 13
14.52
0.5925
0.5777
14.41
0.6692
0.4986
Ours
15.63
0.6238
0.4786
15.46
0.6892
0.4133
Gaussian Marbles
15.55
0.5310
0.5295
9.89
0.8291
0.5473
Wheel
SoM 46
17.38
0.6318
0.3456
13.05
0.8393
0.5037
MoSca 13
17.49
0.6460
0.3684
11.42
0.8395
0.5665
Ours
18.00
0.6505
0.3215
12.21
0.8469
0.5203
B
Implementation Details
In this section, we provide any implementation details not included in the main manuscript.
B.1 Monocular Reconstruction
We implement the monocular reconstruction step directly as MoSca 13. keeping the original hyperparameters intact. We substitute dynamic masks estimated from epipolar eттоr by masks obtained from Track Anything (56).
B.2 Personalised Diffusion Model
We train our personalised diffusion model with a Dreambooth [37 approach implemented in the diffusere librarry as LoRA fine-tuning process. We use the default implementation the SDXL model with default parameters. We change the resolution to match our input resolution (720x960). Similarly, we change the number of training iterations from the default 500 5000, in response to the default model being suitable for personalisation with smaller number of images (5-40), as opposed our inputs (ranging above 400).
25

---

Table 9: Per-scene quantitative evaluation of ViDAR against state-of-the-art methods on the NVIDIA dataset full resolution. Best, second and third results are highlighted in red, orange and yellow respectively.
Method
PSNR-m1
SSIM-m
LPIPS-m+
PSNR-Df
SSIM-D1
LPIPS-D4
DynNeRF
22.36
0.7748
0.1015
18.68
0.8805
0.1950
Balloon
RoDynRF
26
22.37
0.7818
0.0998
18.95
0.8800
0.1876
NSFF 16
21.96
0.7013
0.2119
18.66
0,8858
0.2573
MoSca
13
23.58
0,8008
0.1002
19.80
0.8811
0.2149
Ours
23.98
0.8173
0.0817
20.07
0.8953
0.1686
DynNeRF
4
27.06
0.8591
0.0496
20.73
0.9640
0.0543
Balloon2
RoDynRF
26.19
0.8459
0.0532
19.87
0.9549
0.0654
NSFF 16
24.27
0.7314
0.2144
19.88
0.9528
0.1448
MoSca
27.80
0.8755
0.0538
21.98
0.9696
0.0633
Ours
28.30
0,8857
0.0490
22.14
0.9722
0.0519
DynNeRF
4
24.68
0.8417
0.0863
18.35
0.9372
0.1603
Jumping
RoDynRF
26
25.66
0.8532
0.0691
19.49
0,9450
0.1322
NSFF 16
24.65
0,8125
0.1465
18.27
0.9359
0.2090
MoSca
13
25.02
0,8135
0.0914
18.74
0,9368
0.1452
Ours
25.74
0.8355
0.0715
19.02
0.9449
0.1422
Playground
DynNeRF
24.15
0.8492
0.0767
17.10
0.9775
0.1110
RoDynRF 26
24.96
0.8993
0.0464
16.28
0.9732
0.1136
NSFF 16
21.22
0.7047
0.2168
16.76
0.9790
0.1629
MoSca
13
24.25
0.8872
0.0500
17.13
0.9773
0.1036
Ours
24.79
0.8939
0.0479
17.28
0.9776
0.1023
DynNeRF
32.66
0.9514
0.0328
21.05
0.9908
0.1202
Skating
RoDynRF
26
28.68
0.9394
0.0387
14.88
0.9842
0.2283
NSFF 16
29.29
0.8890
0.1208
20.03
0,9908
0.1768
MoSca
13
33.41
0,9502
0.0305
22.35
0.9927
0.0988
Ours
34.07
0.9554
0.0248
22.34
0,9929
0.0959
DynNeRF 4
28.56
0.8722
0.0811 1
26.32
0.9702
0.0832
Truck
RoDynRF
29.13
0.8996
0.0616
27.85
0.9779
0.0681
NSFF 16
25.96
0.7750
0.1724
26.38
0.9699
0.1362
MoSca
27.77
0.8608
0.0792
27.77
0.8608
0.0802
Ours
28.34
0.8760
0.0724
28.08
0.9793
0.0544
DynNeRF
4
23.26
0.7107
0.1303
17.08
0.9548
0.1638
Umbrella
RoDynRF
24.26
0,7572
0.0850
19.78
0.9653
0.1006
NSFF 16
22.97
0.6439
0.2743
16.57
0.9510
0.2625
MoSca
13
25.18
0.7669
0.0907
20.36
0,9656
0.1609
Ours
25.19
0.7630
0.0817
20.51
0.9672
0.1260
26

---

Table 10: No-reference evaluation (VBench) of ViDAR against MoSca and the imput video. The provided metrics are part of VBench suite.
Method
Subj. cons.
Bg. cons. f
Motion t
Aesthetic
Imaging
Input
0,9199
0.9577 0,9506
0,9931
0.4507
0.7052
breakdance
MoSca
0,8795
0,9924
0,4259
0.5565
Ours
0.8963
0.9528
0.9925
0.4313
0.5843
Input
0,9860
0.9447
0,9944
0.6272
0.7236
duck
MoSca
0,9561
0.9204
0,9937
0.5855
0.7045
Ours
0.9653
0,9221
0.9939
0,5870
0.6966
Input
0.8907
0.9690
0,9976
0.4913
0.7470
shiba
MoSca
0.8724
0.9596
0.9959
0.4363
0.6846
Ours
0.8891
0.9657
0.9966
0.4842
0.6910
Input
0.9585
0.9790
0.9968
0.6858
0.7319
train
MoSea
0,9464
0,9709
0.9953
0.6386
0.6730
Ours
0.9521
0.9772
0.9955
0.6782
0.6835
Input
0.9388
0.9626
0.9955
0.5637
0.7269
Average
MoSca
0.9136
0.9504
0.9943
0.5216
0.6547
Ours
0.9257
0.9544
0.9947
0.5452
0.6638
Input
MoSca 1B
Ours
Figure 8: Qualitative evaluation of our method against MoSca on in-the-wild videos.
Table 11: Quantitative results of an ablation study with the use of generalisable enhancement model Difix3D++.
Method
PSNR-m
SSIM-m
LPIPS-m
PSNR-D
SSIM-D
LPIPS-D
Difix3D+ no ref
18.30
0.6558
0.3600
15.26
0.8837
0.3917
Difix3D+ ref
18.27
0.6555
0,3625
15.25
0.8883
0.3923
Ours
19.00
0.6672
0.3623
16.32
0.8941
0.3921
27

---

we utilise the Image2Image translation approach as Table 12: No-reference evaluation (VBench) of ViDAR against MoSca, CAT4D and raw diffusion output. The provided metrics are part of VBench suite. Method
Subj. cons. f
Bg. cons. t
Motion
Aesthetic
Imaging
Raw diffusion
0.9333
0.9273
0,9630
0,4020
0.5720
MoSea
0.9375
0.9323
0.9911
0.4197
0.5535
CAT4D
0.9440
0.9394
0,9939
0.3962
0.5521
Ours
0.9460
0.9439
0.9941
0.4262
0.5588
GT
0.9477
0.9489
0.9969
0.4577
0.7534
B.3
Camera Sampling
To obtain set of varying samples for multi-view supervision, we propose a camera sampling strategy based on extreme poses within the input trajectory.
Given the set of input camera poses (position and orientation), we calculate a mean camera pose. Then, establish a sphere approximating the surface established by the input trajectory, assuming that a target dynamic object is being tracked by the recording. Finally, we select two views in the input trajectory that, when projected on the sphere, are characterised by the largest longitudinal displacement. These constitute the extreme camera poses.
Thereafter, for each time step spanning the whole time range of the input video, we sample the following new cameras:
Two random camera poses from the input trajectory are selected, and a new camera pose calculated as their mean, and random noise is added. Total cameras: 4
For each of the two extreme views, random camera pose from the input trajectory is selected, and a new camera pose calculated as their weighted average, and random noise is added, with the weight increasing towards the extreme views. Total cameras: 12
The extreme camera views. Total cameras: 2
This constitutes our set of 18 new training cameras for each timestep of the input video cm E Crample.
B.4 Multi-View Sample Enhancement
Having sampled set of new trajectories, we render them with the previously trained monocular reconstruction model, in such way we obtain set of degraded images {Rm.r). To perform the enhancement as described in Section 3.2 implemented in diffusers.
B.5 Diffusion-Aware Reconstruction
We increase the total number of iterations from 8000 to 40000 order to train on the additional generated data. During optimisation, sampled camera pose optimisation and the second for optimising the Gaussians and input camera poses. At each iteration, we randomly select two the sampled camera poses which correspond to the same time step as the input camera. During the first pass, we render the images and compute the mean of the camera losses Leam for both of the sampled cameras and update only the sampled camera poses Csample- During the second pass, we re-render the images using the updated camera poses and compute the dynamic loss Ldyn using the dynamic region masks. This loss is added the existing monocular losses and is used to update the input camera poses Cinp and the Gaussians G.
separate forward and backward passes, the first for
C Limitations
As mentioned in Section 5 the main limitation of our approach lies in the dependency the underlying monocular reconstruction model starting point. the monocular reconstruction model (in our work, MoSca) fails catastrophically, ViDAR will not be able to enhance the reconstruction.
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e.g. if MoSca reconstructs a human with a detached arm, ViDAR cannot reattach the arm if it is far away, This is due the conditioning of the diffusion model on the rendering from initial reconstruction. Similarly, our work does not explore the extrapolation of the scene reconstruction through diffusion outpainting the unseen areas.
D Broader Impacts
Our work focuses improving the reconstruction of dynamic scenes in monocular camera setting. We believe this limits the potential negative socictal impacts, namely, ViDAR does not hallucinate in the regions outside the observed scene and requires monocular prior the geometry. This reduces the potential for misuse via the generation of fake content.
E
Computational Resources
Our approach does not require a large amount of computational resources, as we a single graphies card characterised by 60 TFLOPS at fp32. Our diffusion enhancement stage requires 2 hours to finetune and generate the multi-view images and we perform diffusion- reconstruction for further 2 hours, bringing the total compute required to 4 GPU hours.
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Reviews:
[{"review_id": "by1xTBkKhZ", "reviewer": "Reviewer_aMhC", "summary": "The paper presents ViDAR, an algorithm for 4D reconstruction from monocular videos, addressing the challenge of generating high-quality novel views of dynamic scenes with spatio-temporal consistency. ViDAR employs MoSca to create a 4D scene representation and samples novel camera poses to produce degraded multi-view renderings. Leveraging a DreamBooth-adapted diffusion model, it refines these renders into pseudo-supervision signals. To resolve diffusion-induced inconsistencies, the approach incorporates a diffusion-aware loss and camera pose optimization.", "questions": "As noted in the Weaknesses section, a more detailed explanation of how misaligned diffusion outputs nonetheless enhance result consistency would make the proposed method far more convincing.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The paper features high readability and a well-structured logical flow.\n2. Experiments on the DyCheck dataset demonstrate significant advantages over state-of-the-art methods.\n\nWeaknesses:\n1. The methodological novelty is constrained, resembling a combination of per-scene optimization (MoSca) and a DreamBooth-adapted diffusion model.\n2. There is a lack of theoretical support or prior evidence indicating that DreamBooth or SDXL can ensure view-time consistency. Relying on such pseudo-GTs to achieve spatio-temporal consistency appears tenuous.\n3. The computation is excessively time-consuming, with the diffusion enhancement stage alone taking up 12 hours.", "was_revised": true, "final_justification": "I would like to thank the authors for their thorough and detailed rebuttal. It has successfully resolved the issues I raised, and I am now happy to raise my score to \"Accept\". I eagerly await the open-sourcing of the code.", "rebuttal": "# General Response\nWe thank the reviewers for their positive feedback and constructive comments. We are encouraged that reviewers: \n1. found our paper to be well-written and easy to follow (aMhC, wqBG, coKt) \n2. agree that our method outperforms state-of-the-art and is effective and robust (aMhC, coKt)\n3. agree that our proposed dynamic region benchmark is valuable and well motivated (wqBG, coKt, wkEa)\n4. agree that our proposed pipeline is a logical way to incorporate diffusion priors into 4D reconstruction from monocular inputs (aMhC, wqBG, wkEa) and that it tackles a challenging and useful problem (coKt)\n\nWe also appreciate constructive comments, which help to improve our work, and we will incorporate all such feedback into our paper. In this header, we summarise the new insights we present in this rebuttal, along with a reference to a particular response. We encourage reviewers to read all our responses.  \n\n&nbsp;\n## Key insights:\n1. **Additional Dataset**: *Response wqBG*\n\n    We run our method on an additional monocular dataset (NVIDIA) and achieve state-of-the-art results. We present the full image and dynamic region results in Table R1 and Table R2, respectively.\n    \n2. **Additional Ablations**: *Response coKt, wkEa*\n\n    We present additional ablation results to further clarify the impact of the components of our pipeline - see Table R3.\n    \n3. **New Temporal Consistency Metrics**: *Response  aMhC, coKt*\n\n    In Table R4, we report video consistency metrics proposed in VBench on DyCheck dataset to compare the temporal aspects of the generated results.\n    \n4. **Personalised vs General Diffusion**: *Response coKt*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting our personalised diffusion model with a general restorative diffusion approach (Difix3D+). We report the results in Table R5.\n    \n5. **Performance with Sparse Inputs**: *Response wqBG*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting a personalised diffusion model trained on the whole input sequence with 3 or 6 equally spanned frames from the input to investigate the potential of the method in the sparse setting (Table R6).\n    \n6. **In-The-Wild Videos**: *Response wkEa*\n\n    We run our approach on in-the-wild videos. In Table R7, we report video consistency metrics of our approach against MoSca and the input video.\n    \n7. **Time-Complexity Measurements**: *Response aMhC*\n\n    We analyse and report time-complexity measurements of our method on modern hardware.\n&nbsp;\n### Requests for Qualitative Results\nPlease note that we are keen to show our new qualitative results on additional datasets, different training configurations, in-the-wild videos and failure modes. However, it is not possible to showcase new media in this rebuttal due to the revision in rules. We describe qualitative results in the rebuttal and use video quality metrics. We will make these materials available through appropriate channels (e.g. suppl., project page).\n\n&nbsp;\n# Response to Reviewer aMhC\n\nWe thank the reviewer for their positive comments and constructive feedback and address key concerns below.\n\n&nbsp;\n\n### The computation time is slow. \nOur previously reported time-complexity figures were measured when running on a fairly dated GPU (released in 2017, with approx. 7 TFLOPS fp32). Current GPU hardware is substantially faster than that (e.g. 60 - 80 TFLOPS fp32). To prove this, we run our pipeline on a more current GPU (released in 2024, 60 TFLOPS fp32), and report an average total training time of 4 hours per scene, with 2 hours for diffusion and 2 hours for reconstruction. This is 6x faster than our previously reported times and is thus in line with other per-scene optimisation approaches.\n\n&nbsp;\n\n### Our pipeline is a simple combination of per-scene optimisation and diffusion, novelty is constrained.\nWe agree that our pipeline is a combination of per-scene optimisation and a DreamBooth-adapted diffusion model; however we note that it is non-trivial to combine these components together effectively. We show in our ablation study that naively combining the two (Table 4, Ours w/o SO + DR + TGS, 18.46dB) does not lead to much improvement in PSNR compared to MoSca alone (18.44dB) due to the spatio-temporal inconsistency of diffusion outputs. Our *diffusion-aware* pipeline is novel because we demonstrate that it is possible to use inconsistent diffusion outputs to improve reconstruction quality (19.00dB) while still maintaining consistency. To our knowledge, we're the first to incorporate publicly available 2D diffusion models into 4D reconstruction of real-world monocular videos in a geometrically consistent manner. \n\n&nbsp;\n\n### Pseudo-GTs from SDXL/DreamBooth are not view-time consistent. Relying on these is tenuous.\nWe agree that the outputs of our personalised diffusion model are not view-time consistent. We show this in our supplementary video *(2:34-3:28)* and also highlight this issue in our main manuscript *(Sec 3.3 Diffusion-Aware Reconstruction)*. It is not trivial to use inconsistent supervision effectively for 4D reconstruction. However, we design our diffusion-aware reconstruction pipeline to take this *feature* of 2D diffusion models into account. The key observations in our work suggest that monocular reconstruction methods struggle with reconstructing the dynamic content due to shape-pose ambiguity, which often presents as high-frequency artefacts (e.g. supplementary *spin.mp4* - *0:02-0:03* - MoSca). To mitigate that we employ a personalised diffusion model to create a pseudo-multi-view capture of the scene. Notably, diffusion output is not perfect and fully consistent, yet it yields strong support for ambiguous geometry. Thus, we employ such a supervision signal specifically for dynamic regions, resolving potential misalignments with additional camera optimisation. We successfully mitigate the effects of training on inconsistent outputs, and we show this in our supplementary video, where our results demonstrate much greater view-time consistency than the diffusion outputs and other diffusion-based methods such as CAT4D. Additionally, we run video quality estimation on the results of our method, as well as MoSca, CAT4D and raw diffusion outputs (and ground truth as the upper bound) to quantify the temporal quality of the produced videos. We use the VBench suite (CVPR24) which offers the following metrics for non-reference quality estimation: *subject consistency*, *background consistency*, *motion smoothness*, *aesthetic quality*, *imaging quality*.\n\n&nbsp;\n\n*Table R4. Video consistency metrics on DyCheck dataset.*\n\n|Method|subject consistency|background consistency|motion smoothness|aesthetic quality|imaging quality|\n|---|---|---|---|---|---|\n|Raw diffusion output|0.9333|0.9273|0.9630|0.4020|**0.5720**|\n|MoSca|0.9375|0.9323|0.9911|0.4197|0.5535|\n|CAT4D|0.9440|0.9394|0.9939|0.3962|0.5521|\n|Ours|**0.9460**|**0.9439**|**0.9941**|**0.4262**|0.5588|\n|*GT*|*0.9477*|*0.9489*|*0.9969*|*0.4577*|*0.7534*|\n\n&nbsp;\n\nNotably, we notice that our method provides the best performance in majority of metrics. Unsurprisingly, personalised diffusion outputs score very high in *imaging quality*, we believe due to very sharp images which are not necessarily consistent temporally.\n\n&nbsp;\n\n### If any part of our response is unclear or raises further questions, we would greatly appreciate the opportunity to clarify during the discussion phase."}, {"review_id": "Sg9qYQIRf3", "reviewer": "Reviewer_wqBG", "summary": "This work aims to reconstruct the dynamic 3D scene from one monocular video and proposes Video Diffusion-Aware Reconstruction (ViDAR), which utilizes diffusion models to generate pseudo-multi-view supervisions for the training of the dynamic Gaussian Splatting. Specifically, this work generates fine-grained details by conditioning the scene-specific features and introduces the diffusion-aware loss function and the strategy of camera pose optimization.", "questions": "See Weakness. Besides, I think the proposed method can also be inserted into a static 3D reconstruction setting (like sparse views, blurring, etc.).", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n  * The core idea of this work includes diffusion enhancement and pseudo-novel-view sampling. The proposed approach sounds like it makes sense.\n  * The dynamic region mask for evaluation is valuable since the co-visibility mask metrics of the DyCheck dataset [C] are largely confounded by good reconstructions of static regions. Extended evaluation of dynamic region masks should be widely adopted.\n  * This paper is well-written and is easy to understand.\n\n* Weakness\n  * Although the method proposed in this work is very easy to understand, it is difficult to tell the difference between 4D reconstruction and 3D reconstruction using the diffusion enhancement method. That is, the proposed method can also be inserted into a static 3D reconstruction setting (like sparse views, blurring, etc.). I think the key to using diffusion enhancement in 4D reconstruction is to explain how to solve the problem of how to generate content and ensure the consistency of geometric structure at the same time.\n  * The rendered images of the proposed method tend to be smooth and blurred. For example, in Figure 4, the sleeve textures of \"teddy\", the clothes textures of \"block\", and the pants textures of \"space-out\", are compared to SoM [A] and MoSca [B]. Is this due to diffusion enhancement?\n  * This work only conducts experiments on one dataset, i.e. the DyCheck dataset [C]. It is not sufficient. Meanwhile, as far as I know, there may exist pose errors in the DyCheck dataset [C] as illustrated in Appendix A.3. of MoDGS [D]. Therefore, this work should be supplemented with more quantitative results on dynamic reconstruction datasets.\n\n[A] Wang Q, Ye V, Gao H, et al. Shape of motion: 4d reconstruction from a single video[J]. arXiv preprint arXiv:2407.13764, 2024.\n\n[B] Lei J, Weng Y, Harley A W, et al. Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 6165-6177.\n\n[C] Gao H, Li R, Tulsiani S, et al. Monocular dynamic view synthesis: A reality check[J]. Advances in Neural Information Processing Systems, 2022, 35: 33768-33780.\n\n[D] Qingming L I U, Liu Y, Wang J, et al. MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors[C]//The Thirteenth International Conference on Learning Representations. 2025.", "was_revised": true, "final_justification": "If the authors can adequately supplement these findings, ensure inclusion in the main paper, and make commitments to open source and detailed information, I'm currently inclined to believe that this is a marginal paper and could be accepted.", "rebuttal": "# General Response\nWe thank the reviewers for their positive feedback and constructive comments. We are encouraged that reviewers: \n1. found our paper to be well-written and easy to follow (aMhC, wqBG, coKt) \n2. agree that our method outperforms state-of-the-art and is effective and robust (aMhC, coKt)\n3. agree that our proposed dynamic region benchmark is valuable and well motivated (wqBG, coKt, wkEa)\n4. agree that our proposed pipeline is a logical way to incorporate diffusion priors into 4D reconstruction from monocular inputs (aMhC, wqBG, wkEa) and that it tackles a challenging and useful problem (coKt)\n\nWe also appreciate constructive comments, which help to improve our work, and we will incorporate all such feedback into our paper. In this header, we summarise the new insights we present in this rebuttal, along with a reference to a particular response. We encourage reviewers to read all our responses.  \n\n&nbsp;\n## Key insights:\n1. **Additional Dataset**: *Response wqBG*\n\n    We run our method on an additional monocular dataset (NVIDIA) and achieve state-of-the-art results. We present the full image and dynamic region results in Table R1 and Table R2, respectively.\n    \n2. **Additional Ablations**: *Response coKt, wkEa*\n\n    We present additional ablation results to further clarify the impact of the components of our pipeline - see Table R3.\n    \n3. **New Temporal Consistency Metrics**: *Response  aMhC, coKt*\n\n    In Table R4, we report video consistency metrics proposed in VBench on DyCheck dataset to compare the temporal aspects of the generated results.\n    \n4. **Personalised vs General Diffusion**: *Response coKt*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting our personalised diffusion model with a general restorative diffusion approach (Difix3D+). We report the results in Table R5.\n    \n5. **Performance with Sparse Inputs**: *Response wqBG*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting a personalised diffusion model trained on the whole input sequence with 3 or 6 equally spanned frames from the input to investigate the potential of the method in the sparse setting (Table R6).\n    \n6. **In-The-Wild Videos**: *Response wkEa*\n\n    We run our approach on in-the-wild videos. In Table R7, we report video consistency metrics of our approach against MoSca and the input video.\n    \n7. **Time-Complexity Measurements**: *Response aMhC*\n\n    We analyse and report time-complexity measurements of our method on modern hardware.\n&nbsp;\n### Requests for Qualitative Results\nPlease note that we are keen to show our new qualitative results on additional datasets, different training configurations, in-the-wild videos and failure modes. However, it is not possible to showcase new media in this rebuttal due to the revision in rules. We describe qualitative results in the rebuttal and use video quality metrics. We will make these materials available through appropriate channels (e.g. suppl., project page).\n\n&nbsp;\n# Response to Reviewer wqBG\n\nWe thank the reviewer for their positive comments and constructive feedback and address key concerns below.\n\n&nbsp;\n\n### Provide results on another dynamic reconstruction dataset\nWe primarily focused on the DyCheck dataset as this was the most challenging setting; however, we agree that results on an additional dataset would be useful to better showcase the performance and applicability of our method. We ran our method on the NVIDIA dataset (CVPR20) following the protocol of RoDynRF and achieve state-of-the-art results, which we present below. We report full-image metrics for a selection of best performing methods (Table R1), and full results on MoSca and Ours (Table R2). We show a 0.48dB improvement in PSNR and a 9\\% reduction in LPIPS compared to the next best methods, MoSca and RoDynRF, respectively. \n\n&nbsp;\n\n*Table R1: Results on the NVIDIA dataset*\n\n|Method|PSNR|LPIPS|\n|---|---|---|\n|DynPoint|26.53|0.068|\n|RoDynRF|25.89|0.067|\n|GaussianMarbles|22.32|0.129|\n|MoSca|26.72|0.070|\n|Ours|**27.20**|**0.061**|\n\n&nbsp;\n\n*Table R2: Detailed results on the NVIDIA dataset*\n\n|Method|PSNR|SSIM|LPIPS|PSNR-D|SSIM-D|LPIPS-D|\n|---|---|---|---|---|---|---|\n|MoSca|26.72|0.851|0.070|21.16|0.940|0.124|\n|Ours|**27.20**|**0.861**|**0.061**|**21.34**|**0.961**|**0.106**|\n\n&nbsp;\n\nWe are prohibited from attaching multimedia. Nevertheless, qualitatively, we observe sharper edges, more consistent geometry and fewer floaters produced by our method compared to MoSca. Notably, in *Balloon1*, we see sharper edges of the balloon and better geometry of a balloon tag. In *Balloon2*, we observe a similar appearance of the balloon and consistent geometry of a man's face. *Jumping* presents fewer floaters around women's limbs. In *Playground*, we preserve more consistent geometry around small parts of the balloon as well as a sharper person's appearance. The performance on *Skating* is very similar between the two methods (we believe due to very small angular changes in camera pose in the input video). In *Truck*, we mainly observe fewer artefacts in the space vacated by the moving truck. Finally, in *Umbrella*, we note better geometry of the umbrella (no holes as opposed to MoSca).\n\n&nbsp;\n\n### The proposed method can also be inserted into a static 3D reconstruction setting (e.g. sparse views)\n\nWe believe that the reviewer's suggestion is a valuable idea for future research. In our approach, we rely on a personalised diffusion model, which typically recommends using at least 20 images. However, human reconstruction literature achieved some success with personalising diffusion with a lower number of images. Therefore, to validate the potential of using sparse supervision, we designed a small experiment in which we replaced the diffusion model trained on the full input video with one trained on 3 or 6 frames (equally spaced in time). Then, we reconstructed one of the DyCheck scenes (teddy) in those settings (notably, preserving the same sampled camera poses). In Table R6, we present the results of the experiment.\n\n&nbsp;\n\n*Table R6: DyCheck (teddy) results with diffusion personalised with different numbers of input images.*\n\n|Method|PSNR-m|SSIM-m|LPIPS-m|PSNR-D|SSIM-D|LPIPS-D|\n|---|---|---|---|---|---|---|\n|MoSca|14.52|0.592|0.578|14.41|0.669|0.499|\n|Ours-Sparse-3|15.16|0.610|0.503|14.92|0.673|0.441|\n|Ours-Sparse-6|15.43|0.617|0.499|15.29|0.677|0.440|\n|Ours|**15.63**|**0.624**|**0.478**|**15.46**|**0.689**|**0.413**|\n\n&nbsp;\n\nWe observe that the higher the number of input examples, the better the performance of our method. Nevertheless, in all scenarios, the results outperform MoSca. Thus, we believe that designing a similar approach to ours in a sparse static scenario is a viable future research direction.\n\n&nbsp;\n\n### Why are the rendered images of the proposed method sometimes smooth and blurred? \nThere are some errors in the DyCheck camera poses and as we generate 16 additional pseudo-GT poses for each time step based on these camera poses, the camera pose errors in our pipeline magnify and accumulate. Our sampled camera pose optimisation strategy partially mitigates this issue; however, this still leads to slightly blurry textures in some scenes. We note that for the examples mentioned e.g. \"teddy\", \"block\" and \"space-out\", although SoM and MoSca may appear to have sharper textures in places, we recommend viewing the main results video (*00:50-01:23, 01:59-02:23*), where the textures are not view-time consistent and are sometimes floating in space. It is easy to achieve sharp textures if you over-fit on the training data, but at the expense of geometric consistency, whereas our pipeline was designed with spatio-temporal consistency in mind. Also we do not observe such blurred textures on the NVIDIA dataset, and our results are sharp and clear, so this is mostly caused by the camera poses errors in DyCheck, rather than the diffusion outputs. We will update our paper to include qualitative examples from the NVIDIA dataset showing this. \n\n&nbsp;\n\n### The key to using diffusion enhancement in 4D reconstruction is to explain how to solve the problem of how to generate content and ensure the consistency of geometric structure at the same time.\nOne of the main observations in our work is that monocular reconstruction methods struggle with reconstructing the dynamic content due to shape-pose ambiguity, which often presents as high-frequency artefacts (e.g. supplementary *spin.mp4* - *0:02-0:03* - MoSca). As the reviewer mentioned, we use the diffusion enhancement to produce a pseudo-multi-view supervision signal. As the question states, diffusion output is not perfect and fully consistent. However, we believe that it provides strong support, especially for ambiguous geometry, i.e. dynamic content. Therefore, we employ several mechanisms to best use the benefits of diffusion enhancement. Firstly, we employ the supervision specifically for dynamic regions, as a monocular capture of a static region can be translated to a multi-view capture with corresponding camera poses. Secondly, we perform additional optimisation of the sampled camera poses, which resolves potentially induced misalignments. We believe this successfully mitigates the effects of training on inconsistent outputs.\n\n&nbsp;\n\n### If any part of our response is unclear or raises further questions, we would greatly appreciate the opportunity to clarify during the discussion phase."}, {"review_id": "cgCIJka0h2", "reviewer": "Reviewer_coKt", "summary": "ViDAR is a 4D reconstruction framework that takes a monocular video as input. ViDAR initializes the 4D representation with a prior 4D monocular video reconstruction work, MoSca, which produces a 4D Gaussian splatting representation. Stable Diffusion XL model is finetuned to be personalized for the input monocular video such that the model captures the distribution of the images from the current video better. This personalized model is used to enhance the quality of novel view samples, which are then used together with the original video to enhance the quality of 4D Gaussians. ViDAR demonstrates superior quality over the prior 4D monocular video reconstruction approaches.", "questions": "- As I believe the significance of the work comes from building a robust pipeline for 4D monocular video reconstruction, it would be great to have the code released such that the community can benefit from it. Would it be a possibility? \n- Regarding the first point in weaknesses, it would be great to have additional ablations demonstrating the effect of each component more clearly. \n  - Minor: Recent works such as DIFIX3D+ (Wu et al. 2025) suggest that training a general diffusion model for enhancement purposes might be better than a personalized model. This is concurrent work, so it is just a suggestion to include some further discussion on the benefit of personalized DM.\n- Why was a video diffusion model not used, and an image diffusion model was used instead? Apart from the confusion mentioned in the weakness section, there is no clear discussion on this choice.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- ViDAR presents a pipeline for a challenging and useful 4D monocular video reconstruction task that has a lot of downstream applications, such as simulation and content creation.  \n- ViDAR outperforms previous approaches both qualitatively and quantitatively, and presents results with comparison with multiple other works. It also points out the limitations of metrics and proposes an enhanced metric that focuses more on the dynamic regions of scenes that are more relevant for the task. On top of the summary above, it further proposes techniques to enhance the quality of 4D representation, namely Tracking-based Gaussian Classification, Sampled Camera Optimization, and Dynamic Reconstruction, whose effectiveness is demonstrated.\n- The paper is clearly written and easy to follow.\n\nWeaknesses:\n- The main part of the proposed pipeline is a combination of popular techniques, such as dataset enhancement with Diffusion Model works (ex. Instruct-Nerf2Nerf (Haque et al. 2023), ReconFusion (Wu et al. 2023)) and personalized DM for view reconstruction (ex. RefFusion, Mirzaei et al. 2024), on top of a strong 4D initialization given by MoSca. I believe the novelty of the work comes from the robustness of the pipeline, and the effect of each component needs to be more clearly demonstrated. \n    - Table 4 shows that removing some of the proposed techniques shows degradation in quality that can be worse than the initial MoSca. This could suggest that the personalized DM do not contribute much to the performance of the pipeline, unless more evidences are supported.\n    - Compared to other state-of-the-art methods, such as CAT4D, ViDAR leverages more information such as depth map and 2D tracks. It is not clear if adding one or two proposed components to CAT4D (especially MoSca initialization) could make it perform better. Note that I do not mean that this wouldn't be okay, but it is not clear which component is adding the most benefit.\n- ViDAR uses a personalized image diffusion model, but the name of the method (Video Diffusion-Aware … ) is misleading which suggests a ‘video diffusion’ model is used. In line 160, it is actually mentioned ‘a personalized video diffusion model’ - I believe this is incorrect. \n\nMinor:\n- In line 94, CAT4D is mentioned as a concurrent work, but it appeared on Arxiv on Nov 27, 2024, which is prior to the date (March 1, 2025) NeurIPS considers as concurrent.", "was_revised": true, "final_justification": "The paper demonstrates a robust pipeline for 4D monocular video reconstruction, and the authors addressed my initial concerns with a new set of experiments. However, I believe the impact of the work, while it proposes a robust pipeline, might be limited as the work is a combination of prior techniques. Therefore, I rate the final rating as 'borderline accept'.", "rebuttal": "# General Response\nWe thank the reviewers for their positive feedback and constructive comments. We are encouraged that reviewers: \n1. found our paper to be well-written and easy to follow (aMhC, wqBG, coKt) \n2. agree that our method outperforms state-of-the-art and is effective and robust (aMhC, coKt)\n3. agree that our proposed dynamic region benchmark is valuable and well motivated (wqBG, coKt, wkEa)\n4. agree that our proposed pipeline is a logical way to incorporate diffusion priors into 4D reconstruction from monocular inputs (aMhC, wqBG, wkEa) and that it tackles a challenging and useful problem (coKt)\n\nWe also appreciate constructive comments, which help to improve our work, and we will incorporate all such feedback into our paper. In this header, we summarise the new insights we present in this rebuttal, along with a reference to a particular response. We encourage reviewers to read all our responses.  \n\n&nbsp;\n## Key insights:\n1. **Additional Dataset**: *Response wqBG*\n\n    We run our method on an additional monocular dataset (NVIDIA) and achieve state-of-the-art results. We present the full image and dynamic region results in Table R1 and Table R2, respectively.\n    \n2. **Additional Ablations**: *Response coKt, wkEa*\n\n    We present additional ablation results to further clarify the impact of the components of our pipeline - see Table R3.\n    \n3. **New Temporal Consistency Metrics**: *Response  aMhC, coKt*\n\n    In Table R4, we report video consistency metrics proposed in VBench on DyCheck dataset to compare the temporal aspects of the generated results.\n    \n4. **Personalised vs General Diffusion**: *Response coKt*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting our personalised diffusion model with a general restorative diffusion approach (Difix3D+). We report the results in Table R5.\n    \n5. **Performance with Sparse Inputs**: *Response wqBG*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting a personalised diffusion model trained on the whole input sequence with 3 or 6 equally spanned frames from the input to investigate the potential of the method in the sparse setting (Table R6).\n    \n6. **In-The-Wild Videos**: *Response wkEa*\n\n    We run our approach on in-the-wild videos. In Table R7, we report video consistency metrics of our approach against MoSca and the input video.\n    \n7. **Time-Complexity Measurements**: *Response aMhC*\n\n    We analyse and report time-complexity measurements of our method on modern hardware.\n&nbsp;\n### Requests for Qualitative Results\nPlease note that we are keen to show our new qualitative results on additional datasets, different training configurations, in-the-wild videos and failure modes. However, it is not possible to showcase new media in this rebuttal due to the revision in rules. We describe qualitative results in the rebuttal and use video quality metrics. We will make these materials available through appropriate channels (e.g. suppl., project page).\n\n&nbsp;\n# Response to Reviewer coKt\n\nWe thank the reviewer for their positive comments and constructive feedback and address key concerns below.\n\n&nbsp;\n### The personalised diffusion models do not contribute much to the performance of the pipeline, unless more evidence is provided.\nWe thank the reviewer for highlighting this. We include two new configurations, *Ours w/o diffusion*, which removes the diffusion completely, and *Ours w/o TA*, where we replace the Track-Anything masks with noisier masks computed simply by thresholding the opacity of the dynamic Gaussians in the baseline reconstruction. In Table R3, we show that personalised diffusion models contribute a 0.35dB improvement in PSNR and an 11% reduction in LPIPS to our pipeline.\n\n&nbsp;\n\n*Table R3: Additional ablation results.*\n\n|Method|PSNR-m|SSIM-m|LPIPS-m|PSNR-D|SSIM-D|LPIPS-D|\n|-|-|-|-|-|-|-|\n|Ours|**19.00**|**0.667**|**0.362**|**16.32**|**0.890**|**0.392**|\n|Ours w/o TA|18.90|0.657|0.372|15.96|0.887|0.417|\n|Ours w/o diffusion|18.65|0.660|0.408|15.54|0.884|0.435|\n\n&nbsp;\n\nThe reviewer validly highlights that Table 4 shows that removing some of the proposed techniques leads to degradation in quality that can be worse than the initial MoSca, especially *Ours w/o SO + DR + TGS*. It was our intention to show this. Attempting to naively integrate diffusion leads to degraded performance due to the view-time inconsistency of the diffusion outputs. Our novel *diffusion-aware reconstruction* is needed to incorporate 2D diffusion models into our pipeline in a view-time consistent manner, and such that they improve performance.\n\n&nbsp;\n### It is not clear if adding one or two proposed components to CAT4D (especially MoSca initialisation) could make it perform better\nThis is a valid point, and ideally, we would do as suggested and run CAT4D with MoSca initialisation, as this would be a valuable comparison. However, as the code for CAT4D is not publicly available, this is not possible. We note that CAT4D could have opted, like us, to use a highly regularised monocular backbone, e.g. Shape-of-motion, which uses very similar tracking and depth priors to MoSca, and which CAT4D compares directly to in Table 4 of their paper. The choice not to adopt this initialisation may suggest that CAT4D is not designed for such setups. CAT4D takes a fundamentally different approach from us to ensuring view-time consistency by training a multi-view diffusion model from scratch on an extensively curated private dataset with significant augmentations using proprietary diffusion models. On the other hand, we use publicly available 2D diffusion models and aim to ensure view-time consistency at the *reconstruction* stage, instead of at the *generation* stage. From the results on the DyCheck dataset, our approach seems to yield more consistent outputs across all test scenes, as shown in the supplementary videos. Additionally, we run video quality estimation on the results of our method, as well as MoSca, CAT4D and raw diffusion outputs (and ground truth as the upper bound) to quantify the temporal quality of the produced videos. We use the VBench suite (CVPR24), which offers a set of video quality metrics, and present results in Table R4.\n\n&nbsp;\n\n*Table R4. Video consistency metrics on DyCheck dataset.*\n\n|Method|subject consistency|background consistency|motion smoothness|aesthetic quality|imaging quality|\n|-|-|-|-|-|-|\n|Raw diffusion output|0.9333|0.9273|0.9630|0.4020|**0.5720**|\n|MoSca|0.9375|0.9323|0.9911|0.4197|0.5535|\n|CAT4D|0.9440|0.9394|0.9939|0.3962|0.5521|\n|Ours|**0.9460**|**0.9439**|**0.9941**|**0.4262**|0.5588|\n|*GT*|*0.9477*|*0.9489*|*0.9969*|*0.4577*|*0.7534*|\n\n&nbsp;\n\nNotably, our method provides an increase in performance with respect to CAT4D in all metrics.\n\n&nbsp;\n### The name of the method Video Diffusion-Aware is misleading\nThank you for highlighting this and the inaccurate references to video diffusion models in our paper. We will disambiguate these. A more accurate title would be Monocular Video Input for Diffusion-Aware 4D Reconstruction. We will update our paper.\n\n&nbsp;\n### CAT4D is mentioned as a concurrent work, which is prior to the date (March 1, 2025)  NeurIPS considers as concurrent.\nThank you for highlighting this. We wrongly assumed the concurrent date policy to be based on the *publication date at a venue*, which in this case would be June 2025 for CVPR. We now understand that it is the date the paper is made available online, and we will remove 'concurrent'. Nevertheless, our work includes a comparison to CAT4D and outperforms it in the provided metrics.\n\n&nbsp;\n### Code release\nWe intend to release code upon acceptance (and after internal evaluation), as well as to release dynamic masks needed for evaluating the new dynamic region benchmark for DyCheck.\n\n&nbsp;\n### Recent works such as DIFIX3D+ (Wu et al. 2025) suggest that training a general diffusion model for enhancement purposes might be better than a personalised model.\n\nWe thank the reviewer for this suggestion. We were equally curious about the DIFIX3D+ restoration diffusion model. Thanks to the recent code release (11/06/2025), we were able to design an experiment directly comparing the two diffusion approaches. We replace the images enhanced with our personalised diffusion with images enhanced by DIFIX3D+ (in two settings: with and without reference image). We train our reconstruction pipeline in all settings. We report the results on a scene from DyCheck (teddy) in Table R5.\n\n&nbsp;\n\n*Table R5: DyCheck (teddy) results with personalised diffusion (Ours) against restoration diffusion (DIFIX3D+).*\n\n|Method|PSNR-m|SSIM-m|LPIPS-m|PSNR-D|SSIM-D|LPIPS-D|\n|-|-|-|-|-|-|-|\n|MoSca|14.52|0.592|0.578|14.41|0.669|0.499|\n|Ours-DIFIX3D+-NoRef|15.22|0.615|0.550|15.00|0.676|0.465|\n|Ours-DIFIX3D+-Ref|15.15|0.615|0.553|14.92|0.675|0.467|\n|Ours|**15.63**|**0.624**|**0.478**|**15.46**|**0.689**|**0.413**|\n\n&nbsp;\n\nNotably, all diffusion approaches outperform MoSca. We observe that images enhanced with personalised diffusion (Ours) provide a better supervision signal in our pipeline. Nevertheless, we believe that there exists a potential use case for both approaches, and both can be used to design a reconstruction pipeline.\n\n&nbsp;\n### Why was a video diffusion model not used, and an image diffusion model was used instead?\nThe core of our idea includes personalisation of diffusion and frame-by-frame reference for enhancement. To the best of our knowledge, during the design phase of ViDAR, personalisation of Video Diffusion was not yet developed. Therefore, we opted to use the image diffusion model. We believe that with the rapid development of video diffusion models, a reliable geometrical grounding will become viable, and the models will become more in line with our pipeline.\n\n&nbsp;\n### If any part of our response is unclear or raises further questions, we would greatly appreciate the opportunity to clarify during the discussion phase."}, {"review_id": "ea7ImO3aaR", "reviewer": "Reviewer_wkEa", "summary": "The authors propose ViDAR, a monocular-video pipeline that (i) personalises an SDXL diffusion model to the target clip (DreamBooth-style), (ii) enhances renders from a monocular Gaussian-splat baseline (MoSca) to create high-fidelity pseudo-multi-view supervision, and (iii) introduces diffusion-aware reconstruction: losses that mask supervision to dynamic pixels and a joint optimisation of the sampled camera poses. On the DyCheck benchmark, ViDAR reports superior PSNR/SSIM—particularly in dynamic regions—across all 14 scenes and supplies an ablation isolating each contribution.", "questions": "Failure Modes – What artefacts remain when baseline geometry is poor (e.g., heavy occlusion)? Examples or quantitative failure analysis would be valuable.\nGeneralisation – Have you tried ViDAR on truly in-the-wild phone clips beyond DyCheck? Qualitative evidence would help.", "limitations": "The paper notes diffusion cannot repair major geometric errors and that training is compute-heavy.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nBridges generative diffusion priors with geometrically-consistent monocular 4D reconstruction; supplies a dynamic-mask benchmark.\n\nWeakness:\n1. Dependency on Baseline Geometry – Authors acknowledge diffusion cannot fix large geometric errors; performance is still gated by MoSca quality.\n2. Ablation Scope – No study on diffusion-mask accuracy; sensitivity to Track-Anything noise is unclear.", "was_revised": true, "final_justification": "The NVIDIA benchmark, the “w/o diffusion” ablation, and the 4 h/scene runtime quantify ViDAR’s uplift in dynamic regions. Although the approach is incremental and still bound to MoSca’s geometric prior, the model is still useful for certain cases and the authors answered my question about failure cases well. So I’m raising my score to 5 (Accept). \n\nI look forward to the promised open-source release of code and dynamic masks for community validation and extension.", "rebuttal": "# General Response\nWe thank the reviewers for their positive feedback and constructive comments. We are encouraged that reviewers: \n1. found our paper to be well-written and easy to follow (aMhC, wqBG, coKt) \n2. agree that our method outperforms state-of-the-art and is effective and robust (aMhC, coKt)\n3. agree that our proposed dynamic region benchmark is valuable and well motivated (wqBG, coKt, wkEa)\n4. agree that our proposed pipeline is a logical way to incorporate diffusion priors into 4D reconstruction from monocular inputs (aMhC, wqBG, wkEa) and that it tackles a challenging and useful problem (coKt)\n\nWe also appreciate constructive comments, which help to improve our work, and we will incorporate all such feedback into our paper. In this header, we summarise the new insights we present in this rebuttal, along with a reference to a particular response. We encourage reviewers to read all our responses.  \n\n&nbsp;\n## Key insights:\n1. **Additional Dataset**: *Response wqBG*\n\n    We run our method on an additional monocular dataset (NVIDIA) and achieve state-of-the-art results. We present the full image and dynamic region results in Table R1 and Table R2, respectively.\n    \n2. **Additional Ablations**: *Response coKt, wkEa*\n\n    We present additional ablation results to further clarify the impact of the components of our pipeline - see Table R3.\n    \n3. **New Temporal Consistency Metrics**: *Response  aMhC, coKt*\n\n    In Table R4, we report video consistency metrics proposed in VBench on DyCheck dataset to compare the temporal aspects of the generated results.\n    \n4. **Personalised vs General Diffusion**: *Response coKt*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting our personalised diffusion model with a general restorative diffusion approach (Difix3D+). We report the results in Table R5.\n    \n5. **Performance with Sparse Inputs**: *Response wqBG*\n\n    We run an experiment on one scene of DyCheck (teddy), substituting a personalised diffusion model trained on the whole input sequence with 3 or 6 equally spanned frames from the input to investigate the potential of the method in the sparse setting (Table R6).\n    \n6. **In-The-Wild Videos**: *Response wkEa*\n\n    We run our approach on in-the-wild videos. In Table R7, we report video consistency metrics of our approach against MoSca and the input video.\n    \n7. **Time-Complexity Measurements**: *Response aMhC*\n\n    We analyse and report time-complexity measurements of our method on modern hardware.\n&nbsp;\n### Requests for Qualitative Results\nPlease note that we are keen to show our new qualitative results on additional datasets, different training configurations, in-the-wild videos and failure modes. However, it is not possible to showcase new media in this rebuttal due to the revision in rules. We describe qualitative results in the rebuttal and use video quality metrics. We will make these materials available through appropriate channels (e.g. suppl., project page).\n\n&nbsp;\n# Response to Reviewer wkEa\n\nWe thank the reviewer for their positive comments and constructive feedback and address key concerns below.\n\n&nbsp;\n### The quality is dependent on baseline geometry\nAs the reviewer has highlighted, we discuss this limitation of our work in the paper. Our work is indeed dependent on a number of priors, e.g. MoSca, Track Anything, Stable Diffusion, but this is essentially due to the extremely ill-posed nature of 4D reconstruction from monocular videos. We expect that as the accuracy of pretrained foundational 3D computer vision models improves, our method will similarly benefit from more accurate priors. We are interested in exploring ways to overcome this limitation in future work.\n\n&nbsp;\n### There is no ablation study on diffusion-mask accuracy - sensitivity to Track-Anything noise is unclear\nThank you for highlighting this gap in our ablation. We replace the Track-Anything masks with masks computed by thresholding the opacity of the dynamic Gaussians in the baseline MoSca reconstruction (these masks are substantially noisier than Track-Anything), and present results in Table R3b. We show that there is a small drop of 0.1dB PSNR-m and a corresponding 2.6\\% increase in LPIPS-m from using the Gaussian opacity masks. More interestingly, we also present results on the dynamic region and show a much larger drop of 0.36dB PSNR-D and a corresponding 6.4\\% increase in LPIPS. This shows that although our method is not sensitive to mask accuracy at the scene-level, it is somewhat sensitive to mask accuracy for dynamic regions. \n\n&nbsp;\n\n*Table R3b: Additional ablation results.*\n\n|Method|PSNR-m|SSIM-m|LPIPS-m|PSNR-D|SSIM-D|LPIPS-D|\n|---|---|---|---|---|---|---|\n|Ours|**19.00**|**0.667**|**0.362**|**16.32** | **0.890**  | **0.392**|\n|Ours w/o TA|18.90|0.657|0.372|15.96|0.887|0.417|\n\n&nbsp;\n\n### What artefacts remain when baseline geometry is poor (e.g., heavy occlusion)? Examples would be valuable. \nWe note that baseline geometry is particularly poor for \"wheel\", and we show a comparison to MoSca in 'wheel.mp4' (supplementary). Quite large random shuddering movements in MoSca are fixed by ViDAR and blurry textures are also corrected; however, when a hand completely disintegrates (*00:04-00:05*), our method is not able to correct this, and the artefact remains. This kind of artefact is too different from the natural distribution of the input video, which our personalised diffusion model is fine-tuned on, and hence we are unable to correct it. \nPlease note that even though we are keen to demonstrate further new examples where baseline geometry is poor, it is not possible to showcase new media in this rebuttal as rules stand. We will do our best to showcase this upon acceptance in the supplementary and project page.\n \n&nbsp;\n### Have you tried ViDAR on truly in-the-wild phone clips beyond DyCheck? Qualitative evidence would help.\nWe have run ViDAR on in-the-wild data. We used the same set of videos as proposed by MoSca for fairness and lack of bias, namely, the following scenes: *breakdance-flare*, *duck*, *shiba*, *train*. We render the videos from a stationary camera subselected from input sequence. We notice reduced floaters and artefacts in high-frequency regions and improved geometry-consistency compared to baselines. We are not able to include qualitative examples in the rebuttal. Nevertheless, in *breakdance-flare* we mainly observe a more consistent geometry, specifically in areas of limbs. In *duck*, we observe fewer floaters around pedestrians and cars. In *shiba*, we notice a more consistent geometry of the dog, and fewer floaters, especially in the timesteps where the camera pose is the farthest from the rendered. Finally, *train* similarly exhibits clearer edges of the train and carriages, as well as fewer floaters, which are present in MoSca on tracks already vacated by the train. We also report video quality metrics in Table 7. These were obtained by VBench - a comprehensive suite for video quality analysis.\n\n&nbsp;\n\n*Table 7: VBench metrics for results of in-the-wild reconstruction.*\n\n|Method|subject consistency|background consistency|motion smoothness|aesthetic quality|imaging quality|\n|---|---|---|---|---|---|\n|MoSca|0.9135|0.9503|0.9943|0.5215|0.6546|\n|Ours|**0.9257**|**0.9544**|**0.9946**|**0.5452**|**0.6638**|\n|*Input*|*0.9387*|*0.9626*|*0.9955*|*0.5637*|*0.7269*|\n\n&nbsp;\n\nWe observe that ViDAR outperforms MoSca in temporal and image-quality metrics. We also provide input video results as guidance for the metric values. We will update our paper and video results, as well as the project page, to include qualitative examples of the in-the-wild videos.\n\n&nbsp;\n### If any part of our response is unclear or raises further questions, we would greatly appreciate the opportunity to clarify during the discussion phase."}]

Author rebuttal:

Meta-review: ViDAR, Video Diffusion-Aware Reconstruction, is a method for dynamic 3D scene reconstruction from monocular videos. Video diffusion models are used to generate pseudo-multi-view supervisions for reconstruction with dynamic Gaussian Splatting. 

The work demonstrates strong improvements on the DyCheck dataset, particularly in dynamic regions, where it also proposes a valuable new evaluation protocol (dynamic region masks). Reviewers appreciated the practical impact and thorough experiments, with two reviewers upgrading their scores after rebuttal. 

Limitations include technical novelty (largely a combination of existing techniques), and high computational cost. 

Nonetheless, the paper provides a robust and useful pipeline that outperforms sensible baselines and bridges diffusion and 4D reconstruction. All reviewers agree that the rebuttal successfully addressed their concerns and that the submission makes a valuable contribution to the community, especially with promised open-sourcing. 

I recommend accepting the paper as a poster because it makes a solid and impactful contribution, agreed upon by all reviewers, though not conceptually novel enough for oral/spotlight.
Decision: Accept (poster)