Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
Authors: Hanxue Liang
Jiawei Ren
Ashkan Mirzaei
Antonio Torralba
Ziwei Liu
Igor Gilitschenski
Sanja Fidler
Cengiz Oztireli
Huan Ling
Zan Gojcic
Jiahui Huang
Venue: NeurIPS 2025 poster ()
Keywords: Dynamic scene reconstruction
Generalizable Reconstruction
Reconstruction from Monocular Videos
Primary area: applications
TL;DR: Feed-forward dynamic 3DGS scene reconstruction from videos.

Abstract: Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectively handle dynamic content. We present BTimer (short for Bullet Timer), the first motion-aware feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes. Our approach reconstructs the full scene in a 3D Gaussian Splatting representation at a given target (‘bullet’) timestamp by aggregating information from all the context frames. Such a formulation allows BTimer to gain scalability and generalization by leveraging both static and dynamic scene datasets. Given a casual monocular dynamic video, BTimer reconstructs a bullet-time scene within 150ms while reaching state-of-the-art performance on both static and dynamic scene datasets, even compared with optimization-based approaches.

Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
Hanxue Liang" :: Jiawei Ren!:3; Ashkan Mirzaei!.44 Antonio Torralba Ziwei I Liu Igor Gilitschenski", Sanja Fid Cengiz Oztireli², Huan Ling!.4.6 Zan Gojeic' Jiahui Huang"!
Jiahui
NNVIDI, University of Cambridge, 'Nayyang Technological University,
4 "University of Toronto, 'II, Vector Institute
http:://research.nviddd httpa//aaaaa////aaaoaaaaaaaaaaaa
Abstract
Recent advancements in static feed-forward scene reconstruction have demon- strated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectively handle dynamic content. We present BTimmer (short for BulletTimer), the first motion-aware feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes. Our approach reconstructs the full scene in a 3D Gaussian Splatting representation at given target ('bullet") timestamp by aggregating information from all the context frames. Such formulation allows BTimer gain scalability and generalization by leveraging both static and dynamic scene datasets. Given a casual monocular dynamic video, BTimer reconstructs bullet-time scene within 150ms 256 256 resolution while reaching state- of-the-art performance on both static and dynamic scene datasets, even compared with optimization-base -based approaches.
1 Introduction
Multi-view reconstruction and novel-view syn- thesis are long-standing challenges in com- puter vision, with numerous applications rang- ing from AR/VR to simulation and content cre- ation. While significant progress has been made in reconstructing static scenes, dynamic scene reconstruction from monocular videos remains challenging due to the inherently ill-posed na- ture of reasoning about dynamics from limited observations (2).
(Babble
NSFF
LPIPS
PCDVS
Coval-FV
DysuniN
ReQyslt!
0.25
Per-Seme Optinizzden Time (hours)
Rendering quality vs. speed. Our model can reconstruct and render dynamic scenes at much faster speed than existing approaches with a competitive quality. Numbers are reported on NVIDIA Dynamic Scene Dataset [I]
Figure 1:
Current methods for static scene reconstruc- tion can be broadly divided into two cate-
gories: optimization-based 3 4 and feed- forward [5. approaches. However, extending both of these to dynamic scenes is not straightforward. To reduce the ambiguities of scene dynamics, many optimization- based methods aim to constraint the problem with data priors such as depth and
/i: Equal contribution/aadiiingg.
In this paper, we define bullet-time as instantiation 3D scene frozen a given/fixed timestamp
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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8 91 However, balancing these priors with the data remains challenging 10 Moreover, per-scene optimization is time-consuming and thus difficult to scale.
optical flow 7
On the other hand, to avoid the lengthy per-scene-optinizzaion, recent feed-forward approaches 12
B 14
16 5 explored learning generalizable models on large-scale datasets directly perform static scene reconstructions, thereby learning strong priors from data. These inherent priors could help resolve ambiguities due complex motion, but none of previous approaches have yet been extended to dynamic scenes, This limitation stems from both the complexity of modeling dynamic scenes and the lack of 4D supervision data. The only feed-forward dynamic reconstruction model [T7] is thus trained on synthetic object-centric datasets, requires fixed camera viewpoints and multiview supervision, and cannot generalize to real-workd scene scenarios.
15
In this work, we aim to answer the question: How can one build feed-forward reconstruction
model that can handle dynamic scenes effectively? We build upon the recent success of the pixel- aligned 3D Gaussian Splatting (3DGS (88) prediction models [5] and propose a novel bullet-time formulation for feed-forward dynamic reconstruction. The core idea is simple yet effective: we add a bullet-time embedding to the context (input) frames, indicating the desired timestamp for the output 3DGS representation. Our model is trained to aggregate the predictions of context frames to reflect the scenes at the bullet timestamp, yielding a spatially complete 3DGS scene. This design not only naturally unifies the static and dynamic reconstruction scenarios, but also enables our model to become implicitly motion-awar
while learning to capture scene dynamics. In particular, the proposed formulation (i) allows us to pre-train our model on large amounts of static scene data, (ii) scales effectively across datasets, without being constrained by duration and frame rates of input videos, and (iii) outputs volumetric video representations that inherently support multiple viewpoints. Meanwhile, in the presence of fast motions, we additionally introduce Novel Time Enhancer (NTE) module to predict the intermediate frames before feeding them to the main model.
In summary. we present B Timer, the first feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes. To achieve this goal, we introduce the core bullet-time formulation and develop a curriculum training strategy that enables the learning of a highly generalizable model on a large, carefully curated dataset comprising both static and dynamic scenes, Furthermore, we present an additional NTE module to effectively handle fast motions, enhancing the model's 's robustness in challenging scenarios. Our method highly efficient: feed-forward inference with 12 context frames of 256 x 256 resolution only costs 1 150ms on a single GPU, and the output 3DGS can be rendered in real-time. BTTimer is capable of handling both static and dynamic reconstructions. It achieves competitive results on various reconstruction benchmarks, even surpassing many expensive per-scene optimization- methods, as illustrated in Fig.
2 Related work
Dynamic 3D representations. Depending the tasks at hand, typical choices of 3D representations
include voxels (19 (0), implicit fields/NeRFs (2 3 22), and point clouds/3I Gaussians 23 18).
Representing dynamics on top has even larger design space: One existing line of works directly builds a '4D' representation to enable feature queries at arbitrary positions and timestamps from given step (26) 22). with the extensibility to higher dimensions such as material 28). Another line of work first defines a canonical 3D space, and learns deformation field to warp the canonical space to the target frame. While these methods learn additional information about shape correspondences, their performance heavily relies on the quality and topology of the canonical space.
an implicit field 24 25 or via marginalization
Dynamic novel view synthesis. For tasks that require relatively smaller view extrapolation, the problem of novel view synthesis can be tackled without explicit 3D geometry in the loop, using depth warping I or multi-plane images (29]. Otherwise, the study of novel view synthesis of dynamic scenes 30 is mainly on (1) effectively optimizing the 3D representation through input images
through monocular cues [31 8 10] or geometry regularizations [32 33 and (2) being able to
render fast with grids 34 local-planes (5). or dynamic 3DD Gaussians (36 formulation. Our method
aims to provide a dynamic representation that is fast to build within hundreds of milliseconds while reaching competitive rendering quality as the above optimization-banl based methods.
Feed-forward reconstruction models. many applications where the reconstruction speed is crucial, most optimization-batt based reconstruction methods become less preferable. To this end,one line

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Gaussians Bullet Timestamp
Buillt Time
Self LeffAAit Attettion MMP
Sclt Atccuttionn +
Rendered View
Rullet
Time
Ruillet
Context Frames
Plücker Embeddings
BTimer
LRGB
The model takes as input a sequence of context frames and their Plücker embeddings, along with the context timestamp and target ('bullet') timestamp embeddings. It then directly predicts the 3DGS representation at the bullet timestamp.
Figure 2: BTimer.
of work that starts emerge is fully feed-forward models that directly regress from 2D images to 3D, represented as either neural field 13 37 triplanes [12] 3D Gaussians 14 [S] sparse voxels 38), or latent tokens [16]. Crucially, while feed-forward reconstruction models for static scenes have seen development, the extension to dynamic scenes is still challenging. Existing methods either require hard-to to-acquire consistent video depth input [39]. do not support rendering [40], only work on object-scale data [17]. In contrast, our method supports reconstructing from a monocular video containing dynamic scenes in fully feed-forward manner, and is able render at arbitrary viewpoints and timestamps.
3 Method
Overview. Given a monocular video (image sequence) represented by (1, R with N frames of width W and height H, along with known camera poses (P, E SE(3) intrinsies, and corresponding timestamps T {4, R] our goal build beed-forward model capable of rendering high-quality novel views at arbitrary timestamps E [41, tx].
The core of our approach is transformer-basee based bullet-time reconstruction model, named BTimmr, that takes in a subset of frames Ie I (denoted as context frames) along with their corresponding poses P. P and timestamps To T, and outputs complete 3DGS [18] scene frozen at specified bullet timestamp t, E [miny,, maxy.] ($ 3.I.. Iterating over all t, E T results in full video reconstruction represented by sequence of 3DGS. We further introduce Novel Time Enhancer (NTE) module that synthesizes interpolated frames with timestamps $ T ($ 3.2h. The output of the NTE module is used along with other context views input the bullet-time model to enhance reconstruction at arbitrary intermediate timestamps. To effectively train our model, we carefully design learning curriculum 3.3) that incorporates large mixture of datasets containing both static and dynamic scenes, to enhance motion awareness and temporal consistency of our models.
3.1 BTimer reconstruction model
network as its backbone, consisting of 24 self-attention blocks with LayerNorms 42 applied both the beginning and the end of the model. We divide each input context frame I, E I into x 8 patches, which are projected HW/64 using linear embedding layer. The camera Plücker embeddings (43 into feature space (f,5b) 1j=1 derived from the camera poses P; Pa and the time embeddings (detailed later) are processed similarly to form the camera pose features (rpese and the time features (fimm) (shared for all patches j). These features added together to form the input tokens for the patches of the context
Model design. Inspired by [5, our BTimer model uses a ViT-based 41

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frame (f,, / where fij bj + fgme + f.umm The input tokens from all context frames are concatenated and fed into the Transformer blocks.
Each corresponding output token by is decoded into 3DGS parameters G.y E R8X8812 using single linear layer. Each 3D Gaussian is paramaterized by its RGB color R scale E R² rotation represented as unit quaternion q E IR*, opacity Ø E R, and ray distance T E R, resulting in 12 paramaters per Gaussian. The 3D position of each Gaussian μ I3 is obtained through pixel-aligned unprojection as μ o + rd, where o E RR3 and d E R are the ray origin and direction obtained from P..
Time embeddings. The aforementioned input time feature fime obtained from: (i) context timestamp 4 that is separate for each context frame I, and (ii) bullet timestamp l, that is shared across all context frames i. Both timestamp scalars are encoded using standard Positional Encoding (PE) 44 with sinusoidal functions, and then passed through two linear layers to obtain the features phlllet
4 and phollct respectively. Finally, we set fime
f.
Supervision loss. Our model is supervised only by losses defined in the RGB image space, by- passing the need for any source of 3D ground truth that is hard obtain for real data. The final loss is a weighted sum of Mean Squared Error (MSE) loss and Learned Perceptual Image Patch Similarity (LPIPS) [45 loss between the images rendered from the 3DGS output and the ground-trume -trth image:
Bullet
Time Encedding
Enihediigg
Time Enitedding
Plicker
Plädeer
Enboliis
Enbesinn
Novel Time Eshancer
BTance
Prodicted
Bullet-time Gaussians
LRGB LISE + ALLPPPS
(1)
Bullet time
Frame
with À >= 0.5.
Careful selection of input context frames and corresponding supervision frames (at the bullet timestamp) during training is essential for sta- ble training and good convergence. In practice, we find the combination of the following two strategies particularly effective: (i) In-context Supervision where the supervision timestamp is randomly selected from the context frames, en- couraging the model to accurately localize and reconstruct the context timestamps. For multi-view video datasets, images from additional viewpoints can also contribute to the loss. (ii) Interpolation Supervision where the supervision timestamp lies between two adjacent context frames. This forces the model to interpolate the dynamic parts while maintaining consistency for the static regions. The interpolation supervision significantly impacts our final performance (cf. $ 4.4 for details); without it, the model falls into a local minima by positioning the 3D Gaussians close to the context views but hidden from other views.
Figure 3: NTE Module. It takes input the target bullet time embedding, target pose, as well as adja- cent frames to directly predict corresponding RGB values. The predicted frame is then used in BTimee as bullet frame for novel time reconstruction.
Inference. Our buller-time formulation makes it straightforward to reconstruct a full video, which only involves iteratively setting the bullet timestamp to every single timestamp in the video, and can be done efficiently in parallel. For a video longer than the number of training context views [Ze at timestamp 6, apart from including this exact timestamp and setting 4, we uniformly distribute the remaining [I_] 1 required context frames across the whole duration of the video to form the input batch with [Z| frames.
3.2 Novel time enhancer (NTE) module
While our BTimer model can already reconstruct the 3DGS representation for all observed timestamps, we notice that forcing it to reconstruct at novel intermediate timestamp, i.e. performing interpolation t, & T, leads to suboptimal results. such cases, the exact bullet-time frame cannot be included in the context frames as it does not exist. Our model specifically fails to predict smooth transition between adjacent video frames when the motion complex and fast. This is mainly caused by the inductive bias of pixel-aligned 3D Gaussian prediction. To mitigate this issue, we propose a 3D-free Novel Time Enhancer (NTE) module that directly outputs images at given timestamps, which are then used as input to our BTimer model, illustrated in Fig.

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NTE module design. The design of this module is largely inspired by the very recent decoder-on -nly LVSM [T6] model. Specifically, NTE copies the same ViT architecture from the BTimer model, but the time features of input context tokens only encode their corresponding context timestamps (i.e. we set fu = ). Additionally, we concatenate extra target tokens to the input tokens, which encode the target timestamp and the target pose for which we want to generate the RGB image. Following [66), we use QK-norm to stabilize training. Implementation -wis we apply an attention mask that masks all the attention to the target tokens, so KV- (cf. [46]) can be used for faster inference. From the output of the Transformer backbone, we only retain the target tokens, which we then unpatchify and project to RGB values the original image resolution using single linear layer. The interpolation model is trained with the same objective as the main BTimer model (see $ $3.1,. I. but the output image is directly decoded from the network and not rendered from a 3DGS representation.
Integration with BTimer. While the NTE module can be used on its own to generate novel views, we empirically find the novel-view-synthes integrate it with our main BTimer model. To reconstruct bullet-time 3DGS at ty & 'T we first use NTE to synthesize I, at the timestamp 4s where the target pose P3 is linearly interpolated from the nearby context poses P. and the context frames are chosen as the nearest frames to la. To accelerate the inference of the interpolation model, we use the KV-Cache strategy. In practice we observe that the interpolation model adds negligible overhead to the overall runtime.
quality to be inferior ( 4.4). We hence propose to
3.3 Curriculum training at scale
One important lesson people have learned from training deep neural networks is to scale up the training 47 48), and the model's generalizability is largely determined by the data diversity. Since our bullet-time reconstruction formulation naturally supports both static (by equalizing all elements in 7) and dynamic scenes, and requires only RGB loss for weak supervision, we unlock the potential of leveraging the availability of numerous static datasets to pretrain our model. We hence aim to train a kitchen-sinkk reconstruction model that not specific to any dataset, making it generalizable to both static and dynamic scenes, and capable of handling objects as well as both indoor and outdoor scenes. This is in contrast to, e.g., GS-LRM S or MVSplat 14 where one needs different models in different domains.
Notably, we apply the following training curriculum to BTimer and the NTE module separately, but during inference they are used jointly as explained in & 3.2
Stage 1: Low-res to high-res static pretraining, To obtain more generalizable 3D prior as initialization, we first pretrain the model with a mixture of static datasets. Time embedding will not be used in this stage. The collection of datasets covers object-centric (Objaverse [49] and indoor/outdoor scenes (RE10K (50, MVTmgNet [5T) DL.3D (52). The datasets cover both the synthetic and real- real-world domains and consist of 390K training samples. We normalize the scales of different datasets to be bounded roughly in a 10x cube. Due to the complex data distribution, our training starts from low-resolution few-view setting that reconstructs on 128 x 128 resolution from [Ze| context views. To further increase the reconstruction details, we fine-tune the model from 128 x 128 by first increasing the image resolution to 256 x 256, and then fine-tune to 512 x 512.
Stage 2: dynamic scene co-training. After the training on static scenes, we start fine-tuning the model along with time embedding projection layers on dynamic scenes with available 4D data that contains monocular or multi-view synchronized videos. We leverage Kubric [53] PointOdyssey [54], DynamicReplica [55] and Spring (56) datasets for training. Due to the scarcity of 4D data, during this stage we keep the static datasets for co-training which provides more multi- -ieti supervision and stabilizes the training. Additionally, we build a customized pipeline to label the camera poses from Internet videos (detailed below), and add them to our training set to further enhance the model's robustness towards real-world data.
Stage 3: long-context window fine-tuning. Including more context frames is vital when reconstruct- ing long videos. Therefore, as a final stage, we increase the number of context views from [I,| = 4 to [Ia| 12 to cover more frames. Note that this stage does not apply to NTE it only takes nearby frames as input.
Annotating internet videos. We randomly select subset from the PANDA-70M cut the videos into short clips with ~20 s duration. We mask out the dynamic objects in the videos with Segment Anything Model (58) and then apply DROID-SLAM [59] to estimate the camera poses.
57 dataset, and
5

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Model
Rec. Time
PSNRT
SSIM"
LPIPS.
Model
Rec. Time
Render FPS
PSNR
LPIPS.
Tox
63
0.75
14.03
0.502
0.538
HyperNeRF 4
64h
0.40
17.60
0.367
NSFF 2
h4
15.46
0.551
0.396
DynNeRF M
74h
0.05
26.10
0.082
T-NeRF ++
12h
16.96
0.379
RoDyaRE
28 h
0.42
25.89
0.065
Netties
02
24 h
16.45
0,570
0.339
4D-G8 66
1.2h
44
21.45
0.199
HlyperNeRF
Casual 9
24.57
0.081
PGDVS 30
3h
15.88
0,548
0.340
PGDVS 30
3h
0.70
24.41
0.186
Depih Warp
7.81
0.201
0.678
Depih Warp
12.63
0.564
8Timer (Ours)
0.98
16.52
0,570
0.338
BTimerr (Ours)
0.78a
115
25.82
0.086
(a)
(b)
Table I: Quantitative comparisons on dynamic datasets. (a) DyCheck iPhone dataset [64] comparison. (b) NVIDIA Dynamic Scene dataset [T] comparison. The results are rendered on 480 x 270 resolution. 'Rec. Time' is per-scene reconstruction time. Video-consistent depth estimation step included. We highlight the best second best and third best results.
Low-quality videos or annotated poses are filtered out by measuring the reprojection error. The final dataset contains more than 40K clips with high-quality camera trajectories.
4
Experiments
In this section we first introduce necessary implementation details in $ 4.1 We evaluate the perfor- mance BTimer extensively on available dynamic scene benchmarks $ 4.2 and demonstrate its backward compatibility with static scenes $ 4.3 Ablation studies are found in 4.4
4.1 Implementation details
Training. Our backbone Transformer network is implemented efficiently with FlashAttention-3 (60)
and FlexAttention [61]. We use gsplat [622] for robust and scalable 3DGS rasterization since the total number of 3D Gaussians generated by our model can be very large. For BTTimer, the numbers of training iterations are fixed 90K, 90K, and 50K for Stage 1 training on 1282 2562. and 5122 resolutions, and are 10K and 5K for Stage 2 and Stage 3 dynamic scene training respectively. We use the initial learning rates of X 10-4 2 x 10-4 and x 10 for the three stages, and apply a cosine annealing schedule to smoothly decay the learning rate to zero. Training is conducted on 32 NVIDIA A100 GPUs The learning rate, training GPU numbers and training schedules mainly follow [6 5 Training cost analysis and ablation on batch size can be found in the Supplement. We use the same training strategy for NTE. The numbers of iterations are 140K, 60K, and 30K for the progressive training in Stage 1, and are 20K for Stage 2, with the same learning rate schedule
above. As
introduced in $ 3.3 we use mixture of multiple datasets for training [49 [1 50 52 53
54 55 (66)
along with our 40K annotated dataset on PANDA-70M (57). Note that we make sure that none of the testing scenes we show below is included in the training datasets.
Inference cost. Our model can be flexibly applied to different resolutions and numbers of context views. Measured on a single NVIDIA A 100 GPU, BTimer takes 20 ms for 4-view 256² reconstruction, 150 ms for the same resolution with 12 views, and 1.55 for 12-view 512² reconstruction. It requires less than 10 GB memory, which easily fits on commercial-grade GPU (Result shown in Supplement). Please note that our model inference can be parallelized and the overall time overhead remains constant given sufficient memory.
4.2 Dynamic novel view synthesis
4.2.1
Quantitative analysis
We provide quantitative evaluations of the largest dynamic view synthesis benchmarks.
DyCheck benchmark 644. The benchmark includes a dataset that contains dynamic scenes recorded by 3 synchronized cameras. Following the protocol in [64], we take images from the iPhone camera as our context frames and use the frames from the other stationary cameras for evaluation (totaling 3928 images of resolution 360 x 480). Our baselines include per-scene optimization-
based methods, i.e., ToNeuVox 63), NSFF 2). T-NeRF (64) Nerfies (32 and HyperNeRF 4 We
additionally compare to pseudo fecudo-feed-forwatt approach PGDVS 39]

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Referonce
View Time
Time
Reference
View
Reference
View Time
Reforence View Time
BWZ.AN
Figure 4: Visualizations on DAVIS dataset 667 We show our renderings on novel combinations of view poses and timestamps, with the correspondending references shown on the left. The lower- left/right corner shows the rendered depth map for each example.
Nafice
HypesNaRF
RoDyiRF
MorTNNiRF
Figure 5: Qualitative results DyCheck 665 (left) and NVIDIA dynamic scene (right) benchmarks.
We report masked Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) 68 and LPIPS following the benchmark protocol 64 Tab. [a and show visualiza- tions in Fig. 5 Note that since multi-frame inference can run in parallel, for our model we report single-frame reconstruction time regardless of video lengths. It is encouraging to observe that even without per-scene optimization, BTimer achieves a very competitive performance compared to the baselines, ranking 2nd in both SSIM and LPIPS scores. Our model surpasses PGDVS across all metrics without the need of consistent depth estimate. This demonstrates our model's efficiency and strong generalization capability, being capable providing sharper details and richer textures.
NVIDIA dynamic scene benchmark M. NVIDIA Dynamic Scene dataset contains 9 scenes captured by 12 forward-facing synchronized cameras. Following the protocol in DynNeRF (65, build the input by selecting the frames at different timestamps in a 'round-robin' manner. Then we evaluate the novel view synthesis quality the first camera view but at different timestamps. We compare against HyperNeRF [4) DynNeRF [65]. RoDynRF optimization baselines.
33 4D-GS (66. Casual-FVS as per-scene
Our results are shown in Tab. Tb]and Fig. 5 Our model demonstrates performance that is competitive exceeds that of previous optimization-based methods, ranking 3rd among all baselines in terms representation
of PSNR. Compared to the explicit
[66, 99, our approach outperforms their performance 5% PSNR (25.82d 24.57dB). In terms of training and rendering speed, NeRF-based methods 665 6 require multiple GPUs and/or > day for optimization. Compared to 666, 9. our feed-forward bullet-time formulation is significantly faster, requiring no optimization time and rendering in real-time.
4.2.2 Qualitative analysis
To assess the performance of our method in real-world scenarios, we select multiple monocular videos from the DAVIS dataset [67] for testing. Camera poses for the videos were estimated using
7

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(a)
(b)
(c)
REIOK
DLa0V
MVllaggeee
All State
Bulletiane
Figure 6: Left: Qualitative comparison of models trained on different datasets and evaluated on the (b) model w/ Re10K only 3D Pretrain, (c) model w/o static Co-train in Stage 2, (d) model w/o interpolation supervision, (e) Novel Time Enhancer model, (f) our full model. The upper two scenes are from NVIDIA dataset, and lower two scenes are from DAVIS dataset.
Tanks & Temples benchmark 699. Right: (a) model w/o 3D Pretrain,
the same annotation technique as detailed in $ 3.3 Fig. shows visualization of the results. Our model demonstrates strong generalization capabilities in real-world captures, producing high high-quality, sharp renderings across variety of objects with complex motions while maintaining robust temporal and multiview consistency.
4.3
Compatibility with static scenes
Although our model is primarily designed to handle dynamic scenes, the formulation and the training strategy enable
Model
LPTPS]
Model
Datasets
IPIPS
be still backward compatible with static scenes. In this section, we show that the same model achieves competitive results on static scenes.
GPNR
0.250
GS-LRM
0.310
PixeiSplat 15
0.142
MVSplal 14
0.128
Ohjaverse
0.668
GS-LRM
0.114
Ours-Static
MVImageNer
0.343
DL,DD
0.278
Static
Static
0.093
RealEstate10K (RE10K) benchmark [50] We evaluate our model on the RE10K dataset and compare with several state-of-the-art mod- els 770 [5]
Ours Full
0.089
Ours Full
+Dynamic
0.093
To ensure comparability with baseline models, train and test our model ing 256x256 resolution. Fig. 7ajpresents a quan- titative comparison on LPIPS, where our static model outperforms all the baselines. Please re- fer to the Supplement for more comparisons on other metrics and visualizations.
14
5
Figure 7: Quantitative comparisons on static datasets. (a) results on the RE10K bench- mark [50]; (b) results on the Tanks and Tem-
ples benchmark [69]. We highlight the best second best and third best models. Our re- produced results.
Tanks& & Temples benchmark [69]. We further evaluate model unseen test dataset, the Tanks & Temples [7I] subset from the InstantSplat (69] benchmark, which consists of 10 scenes. We the state-of-the-art novel view synthesis model [5] baseline, reproducing their model since the original code and weights are not publicly available. Additionally, we include our pretrained static model from Stage 1 as additional baseline.
To analyze the impact of our mixed-dataset pretraining strategy, we also train single-dataset models using the same training schedule further baselines. All models utilize 4 context views. Quantitative results (Fig. 7b) demonstrate that our pretrained static model with mixed-datate outperforms the single-dataset models, highlighting the crucial role of multi-dataset training for generalization to unseen domains. Even when incorporating the dynamic scene datasets, BTimer achieves comparable result to our best static models. 4.2.1 provides qualitative comparison, showing that BTimer consistently generates sharper outputs that closely align with the ground truth.
training substantially
4.4
Ablation study
We study the effect of different design choices. 1) Context frames. We visualize the reconstruction results progressively add 3DGS predictions from more context frames across multiple different

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Middle Frame wio Enhancer
Middle Frame w/ Enhancer
Thegge Vine
Prere (Fui Viswe)
Wz.cc viz.ch/lobs
wz.ch Yobs
Tnyutt Phenios
2d Prrme (Rof Viewd
Toryet Vinu
Tramee (Pul, Vins)
Trpa. wmp..Paime
Trames
(b)
Figure 8: (a) Illustration of bullet-time reconstruction from multiple context frames. Increased number of frame predictions leads to progressively more complete scene reconstruction on target views. (b) Ablation the NTE module. The middle frame is between the 1st frame and the 2nd frame. Results are rendered from the view of the 1st frame.
timestamps Fig. Sal where increasing the number of context frame leads to progressively more complete scene reconstruction. This demonstrates the flexibility of our bullet-time reconstruction for- mulation: during the inference stage, we can arbitrarily select spatially-distant frames that contribute to complete view coverage of the scene. 2) Curriculum training. We show in $.2..1 the effect of our curriculum training strategy. Without Stage of pre-training on static scenes, the model struggles to produce results of correct geometry and sharp details. Pretraining datasets is also crucial, which we demonstrate by jus/ training on RE10K dataset, and non-negligible distortions are observed in the results. Similarly, even in Stage 2 of our curriculum, still need to co-train on static scenes which provide more multi-view supervisions, thus maintaining the rich details and reasonable geometries. Quantitative ablation results are shown in the Supplement. 3) Interpolation supervision. Shown in & 4.2. (with more results in the Supplement). interpolation supervision (introduced in $ 3.1 plays significant role, without which the model tends to produce white-edge artifacts. This occurs because without interpolation loss, the model often generates 3DGS that are positioned close to the camera with low depth values to cheat the loss. In contrast, adding the interpolation supervision requires the model to account for scene dynamics and encourages consistency across multiple views. 4) NTE. As demonstrated in Fig. 8b our NTE module enhances the bullet-time reconstruction model's ability to handle scenes with fast or complex motions, largely reducing the ghosting artifacts. Additional video results are provided in the supplementary material. Although 3D-free design enables NTT to handle complex dynamics and produce smooth transitions between adjacent frames, the model struggles to produce novel views that are far from the input camera trajectory (As illustrated in $ 4.2.1
multiple diverse
5
Conclusion
In this paper we present BTimer, the first feed-forward dynamic 3D scene reconstruction model for novel view synthesis. We present bullet-time formulation that allows to train the model in more flexible and scalable way. We demonstrate through extensive experiments that our model is able to provide high-quality results at arbitrary novel views and timestamps, outperforming the baselines in terms of both quality and efficiency.
Limitations. Our method is mainly targeted for novel view synthesis, and the recovered geometry (hence the depth map) is usually not as accurate, Correspondences between frames are implicitly modeled by the neural network, and our pixel-aligned Gaussian representation cannot represent temporal deformations. Although practically we observe temporally coherent results, additional post-processing steps have to be introduced to recover the explicit motion of the geometry.

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Broader Impact. BTimer can transform posed casual videos into realistic dynamic 3D assets. However, it should be used with caution, particularly concerning privacy, copyrights, and the potential for malicious impersonation.
References
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The claims in the abstract and introduction ($ I accurately reflect the paper's core contributions, including the introduction of BTimer, the first real-time feed-forward bullet-time reconstruction model for dynamic scenes. The text clearly outlines the bullet- time formulation, Novel Time Enhancer module, and curriculum training strategy, and these are consistently supported by results in $
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
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The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: Limitations are explicitly discussed in $ The paper acknowledges that while BTTimer excels at novel view synthesis, the recovered geometry may lack accuracy due to the pixel-aligned 3D Gaussian formulation. It also notes that temporal deformations are not explicitly modeled and that additional post- post-processing may be needed for extracting motion. Ethical concerns, such as privacy and potential misuse, are also briefly mentioned.
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The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
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Supplementary Material
In this supplementary material, we provide additional details on the datasets used in our experiments ( and our training cost and more ablation studies ($ G On the results side, we first show more
qualitative static and dynamic reconstruction results ($ S D.. We further justify our choice of the
bullet-time formulation by showing that our reconstruction is temporally smooth ($ D as well as an simple extension to unlock the power for estimating dynamic deformations ($
F
Dataset Details
The static datasets used in our training follows: OBJAVERSE 49 is synthetic object-centric dataset, and we use the 80K-object subset from [72). MVIMGNET 511 is real-world object-centric dataset that has 220K objects. RE10K 50 is real-world scene dataset that has 80K video clips. DL3DV (2 is a real-world scene dataset that has 10K video. We sample DL3DV 10 times more frequently than other datasets balance the number of training samples. We spatial scale of for Objaverse and scale for all other datasets.
The dynamic datasets used in training are as follows: KUBRICMV is synthetic multi-vew video dataset that has 3K scenes. We rendered this dataset using the Kubric [53] engine. The scene setup follows Movi-E [53] and videos rendered from all camera poses in the camera trajectory
produces a mueti-view video. POINTODYSSEY (54) is a synthetic monocular dataset with 131 scenes. DYNAMICREPLICA 55 is synthetic stereo video dataset with 484 training sequences. SPRING (56) a synthetic stereo video dataset with 37 scenes. PANDA-70M [57] is a real-world monocular video dataset. We around 40K clips filtered from random subset. We use scale for Spring and DynamicReplica and for other datasets. More details can be found Tab. S2
Dataset
Dynamic
Subject
Domain
#Views
#Frames
#Scenes
#Multiplies
#Scale
RB10K
50
S
Real Real
10M
80K
MVIngNet
51
0
6.5M
220K
Objaverse
44
0
Synthetic
4M
80K
DL3DV
52
S
Real
51M
10K
PointOdyssey
54
O+S
Synthetic
131
3e3
Kubric-MV
0+S
Synthetic
70K
2c2
DynamicReplica
55
0+S
Synthetic
14SK
484
8e2
Spring [66
0+S
Synthetic
200K
37
1c4
PANDA-70M
57
0+S
Real
19M
40K
10
Table S2: Datasets. Dynamic indicates if the dataset is dynamic or static. Subject indicates if the dataset is object-centric (O) or scene-centric (S). Domain indicates if the dataset is captured from the real world is synthesized. #Views denotes the number of synchronized views for dynamic video. #Frames and #Scenes are the numbers of image frames and unique scenes in the dataset respectively. #Multiplies denotes the number of multiplies sample the dataset (by scene) in training for balance. #Scale is the scale we applied to the dataset so that all datasets have approximately the same metric scale.
G Training Cost Analysis and Effect of Batch Size
The full training of BulletTimer takes 4 days on 32 NVIDIA A.00 GPUs. As illustrated in Fig. S9 the training cost is comparable to existing feed-forward 3D reconstruction methods, such LVSM 16 and LRM [2] (384 GPU-days) or GS-LRM 5 (192 GPU-days). Like these methods, our work amortized algorithm: once trained, the inference cost becomes negligible. Taking inference cost also into consideration, per-scene optimization quickly becomes more expensive, with the difference becoming more pronounced with the growing number of scenes.
also functions
Fig. S1O shows the results of training our model with GPU, GPUs, and 32 GPUs. Although inference fits on single GPU, our training benefits from large batch sizes so we used 32 GPUs (cach
23

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0.40
Compute GPU hours)
Ours
Stage
GPU
USM
30
LRM
32 GPU
TNeRF
Training loss
0.20
HyperNeeR
20
Nerfies
Stage 2
Slage
0.06
0
200
400
600
800
230
235
240
Number scenes
Number of iterations (10*)
Figure S9: Computation cost.
Figure S10: Batch-size ablation.
Injut
I
Phsi
MV Spiat
Ours
GT
Irput 1
Injut
Pize Splat
MVVyl
Ous
GT
Figure S11: Comparison on static scene dataset. We compare renderings with the baseline models, trained and tested on the RE10K dataset.
GPU holds single batch). The same number of GPUs was also used both LVSM and GS-LRM. In line with other fields (LLMs, GenAl), we regard the scalability of our method one of its key strengths. For ease of reproduction and fine-tuning, we will release our source code and pretrained checkpoints. To provide a more rigorous assessment of runtime, we have conducted an additional study varying both the number of context frames and the input resolution (see Tab. (S8). The results show that BTimer consistently remains in the second range (even at 512x512 resolution with 12 context frames), and is still several orders of magnitude faster than per-scene optimisation pipelines.
H
More Results on Dynamic Scenes
H.1 Qualitative Results
We have provided video results on the DyCheck Benchmark [65] and NVIDIA Dynamic Scene Benchmark 2 on our project webpage htttps:/research.ntr wtt.d/aaa.a//aa/aao/oooooooo--aa tttp:/oooooooooo/ooo bullet-timer/ Additionally, we include novel view synthesis videos for the DAVIS dataset, DyCheck iPhone dataset, and SORA scenes. We also showcase video demonstrating the effects of the NTE module, along with our video results on the Tanks & Temples static scenes.
www/labs
H.2 Additional Quantitative Results
In this section, we compare our method with more sota models on DyCheck iPhone dataset 64 (see
Tab. (55)) and NVIDIA Dynamic Scene dataset [1] (see (5b).. All previous models require hours of per-scene optimization, whereas our method performs inference in real time with comparable accuracy. We also compare with DyniBaR using DynlBaR's protocol, which demonstrates strong performance the NVIDIA Dynamic Scene Benchmark(sce Tab. S4). While our approach performs

---

Model
PSNR1
SSIM
LPIPS!
PixeINeRF
7
20.43
0.589
0.550
GPNR 70
24.11
0.793
0.250
AttnRend
74
24.78
0.820
0.213
MuRF 775
26.10
0.858
0.143
PixelSplat
15
25.89
0,858
0.142
MVSplat 14
26.39
0.869
0.128
GS-LRM
28.10
0.892
0.114
Ours-Static
26.49
0.886
0.096
Ours-Static 28.91
0.920
0.070
Ours-BTimer
26.82
0.891
0.089
Table S3: Quantitative comparison of models performance on RE10K test set. To be consistent with the baselines, we adopt the 256 x 256 resolution. Our Bullet Timer has been trained on both static and dynamic scenes, while the other model is only trained on RE10K training set. We highlight the best second best and third best models. t: 4 input views.
slightly worse in terms of reconstruction quality, DynlBaR relies extensive per-scene optimization (approximately 300 hours per NSFF sequence) and leverages additional supervision signals such as optical flow and depth. In contrast, BTimer is fully ferd-forward system: it reconstructs scene in just one second and is trained solely with a photometric loss.
Model
PSNR
SSIM LPIPS!
DynIBaR S
30.86
0.957
0.027
Ours-BTimer
26.31
0.852
0.0730
Table S4: Quantitative comparison on NSFF benchmark using DynlBaR's 's protocol
More Results on Static Datasets
We provide comprehensive qualitative comparison of our method against the baselines, MVS- plat [14] and PixelSplat [15), on the RE1OK dataset, as shown in Fig. STT] For each scene, the figure also displays the input views provided to the networks. Compared to the baselines, our method produces sharper outputs and more closely aligns with the ground-truth renderings. Note that all the methods used for the evaluation in this figure trained exclusively REI0K. Additionally, we use two views as context for all methods to ensure fairness evaluation and to align with the setup of the basclines. Tab, S presents quantitative evaluation against the baselines under the same settings. While our static model achieves the best performance among the baselines, our dynamic BTimer model, trained for the dynamic task, also demonstrates strong performance the static task, ranking third on the static benchmark.
Our complete static model, trained across all datasets, is capable of reconstructing highly diverse set of environments. Fig. S12 showcases our model's reconstructions across wide variety of scenes, including outdoor forward-facing, outdoor drone shots, outdoor 360-degree views, indoor 360-degree views, and indoor forward- facing scenes, as well as object-centric synthetic scenes. Notably, all these reconstructions achieved using shared set of weights, demonstrating that our model, trained across multiple datasets, generalizes effectively to different scenarios.
To further demonstrate the importance of training on multiple datasets for generalization to unseen datasets, we conduct ablation study on the datasets used to train our static model. Tab, (S6 compares the performance of our model when trained individually on single DL3DV, or Objaverse-againe its performance when trained on all these datasets simultaneously. The evaluation is conducted on completely unseen dataset, the Tanks & Temples split from the InstantSplat [69] paper. Our model, whether static or dynamic, trained on all datasets significantly outperforms the single-dataset models.
MV ImMagNNet,
25

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Model
PSNR
LPIPS
Model
PSNRT
SSIMT
LPIPS.
D.NeRF
30
21.49
0.232
NR-NeRF
80
19.69
0.323
13.64
0.428
Vox
19.74
0.285
Marse.Mrrbles 76
16.72
0.413
NSFF 2
24.33
0.199
DyBluRF 777
17.37
0.591
D-NPC 78
16.41
0.582
0.319
DynPoint 81
26.53
0.068
Shape of Motion
10
17.32
0,598
0.296
D-NPC 7%
25.64
0.109
MoSca 70
19.32
0.706
0,264
MoSca
26.72
0.070
PGDVS
0.548
PGDVS 39
Depth Warp
7.81
0.201
0.678
Depth Warp
12.63
0.564
BTimer (Ours)
16.52
0,570
0.338
BTimer (Ours)
25.82
0.086
(a)
(b)
Table s5: Additional quantitative comparisons on dynamic datasets. (a) DyCheck iPhone
dataset [64] (b) NVIDIA Dynamic Scene dataset [1]
Figure $12: A diverse set of scenes reconstructed using our static model, trained on multiple datasets and capable of generalizing to various scenarios.
Evaluation of Temporal Smoothness
Since our method reconstructs each timestamp individually, it is necessary to understand its temporal smoothness. In this section, quantitatively evaluate the temporal smoothness of the reconstructed dynamic scenes, with results shown in Tab. S9 We render the reconstructed DyCheck (65) scenes from of the evaluation fixed cameras and evaluate the rendered video using the Temporal Flickering metric in VBench (83) Concretely, the metric computes the pixel-wise Mean Absolute Error every adjacent frames and averages over all pixels and frames:
N Σ
Sticker
MA
(S.2)
where N is the number of videos, T is the number of frames per video, f is the frame in video and MAE is the Mean Absolute Error between two consecutive frames over all pixel locations. Finally, the metric is normalized into the range of 0 to I:
255 S SSticker
Sticker norm
(S.3)
255
The higher metric, the less flickering will be observed in a video. BTimer achieves second best on the Temporal Flickering metric, which suggests that our bullet-time prediction, though not explicitly associated across frames, still achieves a better temporal smoothness than other baselines that decode from some temporal representations.
26

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Model
Datasets
PSNR
SSIM
LPIPS!.
GS-LRM
REIOK
17.56
0.546
0.310
Objaverse
7.00
0.363
0.668
MVImageNet
17.75
0.530
0.343
Ours-Static
DL3DV
17.92
0.566
0.278
All Static
24.22
0.807
0.093
Ours-Full
+Dynamic
24.13
0.806
0.093
Table S6: Baseline comparisons on the Tanks & Temples dataset (InstantSplat split). Test views are 512 512. LPIPS are compuated on 256 x 256. We highlight the best second best and third best models. Our reproduced results.
Method
PSNR 1
w/o Pretmain
17.94
#Ctx.
Ros.
Time
Mem.
w/ RelOK only 3D Pretrain
21.29
Co-train
22.79
4
256
0.02s
1.42G
wlo interpolation supervision
20.54
12
256
0.15%
2,60G
12
512
1,55s
9.68G
Full model
24.00
Table S8: Inference cost. Model is evaluated on single NVIDIA A100 GPU.
Table S7: Quantitative ablation results NVIDIA Dynamic Scene Benchmark. Abla- tion models are trained with 4 context frames.
K
Visualization of Learned Deformation
While BTimer primarily intended for novel view synthesis at the bullet timestamp, in order to demonstrate that our model design can be also targeted for building explicit temporal correlations, we train a variant of BTTimer that predicts the canonical positions (XYZ) of Gaussians instead of the pixel-aligned depths the Objaverse4D [77 dataset. The 4 input images taken from different camera poses and different timestamps. In Fig. S13] we render the reconstructed dynamic object from fixed viewpoint and find that the model successfully recovers the 3D motion by predicting the positions of the Gaussians at the correctly warped locations. This is further justified by keeping only the partial reconstruction from the 3DGS associated with one of the input images, and we find out that the model learns to warp the Gaussians according to different timesteps. Canonical XYZ prediction is commonly used in object-centric cases for being bounded (LGM (72), L4GM [T]), however aixel-algned prediction is popular in large unbounded scene for being easy to optimize (pixelSplat (15), GS-LRM ()). Applying canonical XYZ prediction to scene data valuable direction that we would like explore in future work.
L Visualization of Learned Scene Flow
Although BTimer is not trained with scene flow supervision, we show that our model effectively model scene flows under the hood in the process of learning dynamic reconstruction. We treat the Gaussian associated with each pixel in the input images as a point and treat its trajectory over time as scene flow. The visualization in Fig. [14 that the learned scene flows closely represent the actual object motion.
27

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Model
apple
block
windmill
space
spin
teddy
wheel
Average
Ground Truth
0.9878
0.9767
0.9940
0,9939
0,9829
0.9759
0.9650
0.9823
TiNeu Vox
63
0.9807
0.9814
0.9879
0.9949
0.9832
0.9782
0.9695
0.9823
T-NeRF
0.9831
0.9791
0.9866
0,9907
0.9828
0.9730
0.9624
0.9797
Nerfies 32
0,9817
0.9791
0.9868
0,9918
0.9809
0.9720
0.9609
0,9790
HyperNeRF
4
0.9825
0.9784
0.9865
0,9914
0,9821
0,9720
0.9584
0.9787
PGDVS 39
0.9719
0.9738
0.9956
0,9903
0.9816
0.9649
0.9517
0.9757
BTimer (Ours)
0.9835
0.9760
0.9884
0.9881
0.9789
0.9746
0.9745
0.9806
Table S9: Temporal Flickering [82]
evaluation on the DyCheck [65] dataset.. There are scenes
and we report their average. We highlight the best
second best and third best
t=1
t=2
t=3
t=4
t=
t=2
t=3
t=4
Input
Recon.
Partial
Recon.
Input
Recon.
Partial Recon.
Figure S13: Learned deformation visualization. In each example, the first row shows the 4 input images captured from different viewpoints and timestamps, the second row is our reconstruction rendered from fixed viewpoint, and the third row keeps only Gaussians associated from one of the input images.
28

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View
View
View
t-
t=3
Figure $14: Learned scene flow visualization. We color the Gaussians by the pixel positions they associate with. As the Gaussians move, their trajectories considered scene flows. Our model learns meaningful scene flows that closely represent the object motion without any scene flow supervision.
29


Reviews:
[{"review_id": "IPMCniFkCf", "reviewer": "Reviewer_6QoZ", "summary": "The paper introduces BTimer, a feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes from monocular videos, using a 3D Gaussian Splatting representation.", "questions": "1) The evaluation lacks comparisons with recent methods like CAT4D and MoSca, which outperform BTimer in dynamic reconstruction. Have the authors tested BTimer against these methods, and if not, why were they excluded given their relevance to feed-forward dynamic reconstruction?\n\n2) The NTE module predicts intermediate frames for fast motions, but its contribution is unclear without ablation studies. Can the authors quantify the NTE’s impact on PSNR/SSIM/LPIPS and explain why it outperforms direct 3DGS prediction in fast-motion scenarios?\n\n3) The model uses 12 context frames. How does BTimer scale to longer videos with more frames or higher temporal variability? \n\n4) The NTE module predicts intermediate frames for fast motions, but its contribution is unclear without ablation studies. Can the authors quantify the NTE’s impact on PSNR/SSIM/LPIPS and explain why it outperforms direct 3DGS prediction in fast-motion scenarios?", "limitations": "No limitations discussed. See Weaknesses.", "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\n1) The feed-forward approach enables real-time reconstruction, a significant advantage over optimization-based methods for applications like AR/VR and content creation.\n\n2) The bullet-time formulation and NTE module are clearly described, with visualizations illustrating the pipeline effectively.\n\nWeaknesses:\n\n1) The contribution is incremental, as the bullet-time embedding and NTE module offer limited advancements over prior feed-forward models. The reliance on large-scale static and dynamic datasets raises concerns about generalizability to unseen scenarios without clear evidence of robustness.\n\n2) The motivation for choosing timestamp embeddings over explicit motion modeling is weak.", "was_revised": true, "final_justification": "I am satisfied with the authors’ responses and the evidence provided, and I am willing to increase my score to 4.", "rebuttal": "> **[W1.1]** Incremental Contribution of bullet-time embedding and NTE module  \n\nWe respectfully disagree with the assessment that our contributions are incremental.\n\n1. Prior feed-forward models are limited to static scene reconstruction mainly due to two key challenges: (a). the complexity of modeling scene dynamics in a single forward pass, and (b). the scarcity of large-scale 4D dynamic supervision. Our proposed bullet-time formulation directly addresses both issues in a novel and elegant manner.\n\n    - the bullet-time design allows our model to predict the scene at an arbitrary bullet time by aggregating information from context frames. This training objective implicitly encourages the model to become motion-aware and capture scene dynamics. This is the first feed-forward dynamic reconstruction formulation that has never been explored before.\n\n    - More importantly, this formulation naturally unifies the static and dynamic reconstruction scenarios that allows us to pre-train our model on large amounts of static scene data and scales effectively across datasets. This scalability is key to generalization and performance, as shown in our results across static and dynamic benchmarks.\n\n    We believe this formulation is both conceptually novel and practically impactful, offering a significant step towards feed-forward real-time dynamic scene reconstruction.\n\n2. We propose the NTE module to further enhance reconstruction quality, particularly under complex and fast motions. It introduces a novel design by conditioning on the context frame, interpolated timestamp embedding, and relative pose. This design complements the bullet-time prediction and can be seamlessly integrated into our framework.\n\n3. Moreover, we also introduce curriculum training at scale and interpolation supervision, which are critical to ensuring stable convergence. As demonstrated in our ablation studies, each of these components contributes meaningfully to the overall performance of the model.\n\nTaken together, we believe our contributions form a cohesive and non-trivial advancement in feed-forward dynamic scene reconstruction and address long-standing limitations around scalability, supervision, and real-time inference.\n\n\n\n> **[W1.2]** Reliance on large-scale static and dynamic datasets raises concerns about generalizability.\n\n1. To rigorously evaluate the generalization ability of our model, we conduct extensive experiments across a broad range of datasets, including real-world dynamic scenes, static scenes, AI-generated content (e.g., from Sora), and synthetic 4D environments (e.g., Kubric4D). These datasets exhibit substantial domain gaps, yet our model maintains consistent and strong performance, achieving results competitive with per-scene optimization methods and significantly outperforming previous feed-forward baselines. We believe this provides compelling empirical evidence of robustness and cross-domain generalization.\n\n2. To be the same with most recent feed-forward reconstruction methods (e.g., GS-LRM [1], LGM [2], DUSt3R [3], VGGT [4]), our model leverages large-scale datasets to enhance generalization capacity. This reliance is not a limitation, but a necessary component for real-time, generalizable scene understanding at scale. We emphasize that our design further amplifies the benefits of such pretraining by unifying static and dynamic reconstruction within a single formulation.\n\n3.  We further include additional experiments to demonstrate transfer performance across datasets. We simplify the training setting and only use RE10K in the static stage and a mix of Kubric4D and RE10K in the dynamic stage. The model is evaluated on the Dynamic Scenes dataset. We highlight that Kubric4D is a synthetic dataset with rigid motion, while Dynamic Scene is a real-world dataset with articulated and deformable motion. Despite the significant domain difference, training on Kubric4D substantially improves all metrics on Dynamic Scenes, which shows that the dynamic representation learned can be effectively transferred across very different datasets.\n\n\n    | | PSNR↑ | SSIM↑ | LPIPS↓|\n    |--|--|--|--|\n    |RE10K| 21.62 |  0.724 | 0.257 |\n    |RE10K + Kubric4D | **23.61** | **0.755** |  **0.214**|\n\n\n[1] GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting, ECCV 2024\n\n[2] LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation, ECCV 2024\n\n[3] DUSt3R: Geometric 3D Vision Made Easy, CVPR 2025\n\n[4] VGGT: Visual Geometry Grounded Transformer, CVPR 2025\n\n\n\n> **[W2]** Weak motivation of timestamp embeddings over explicit motion modeling\n\nTimestamp embeddings offer important advantages over explicit motion modeling.\n\n1. It allows our model to capture flexible and complex dynamics, such as deformation with topological changes, and complex natural phenomena such as fire, waves, smoke, and dust. This is evident in our supplementary visualizations on SORA and DAVIS datasets.\n\n2. Despite using implicit time conditioning, our model can still recover decent 3D deformation and scene flows as evidenced in Figure S5 and S6 of the supplementary, where the dynamic structure of the scenes is clearly recovered. This indicates that the model learns meaningful motion representations from time embeddings, even without explicit motion supervision.\n\n3. Our approach aligns with several prior works that successfully use implicit time modeling for dynamic scenes, including ST-NeRF [5], DyNeRF [6], and HexPlane [7].\n\n\n[5] Space-Time Neural Irradiance Fields for Free-Viewpoint Video, CVPR 2021  \n\n[6] DyNeRF: Neural 3D Video Synthesis from Multi-view Video, CVPR 2022  \n\n[7] HexPlane: A Fast Representation for Dynamic Scenes, CVPR 2023  \n  \n\n\n> **[Q1]** Compared with CAT4D and MoSca  \n  \n1. Our primary focus is on fully feed-forward dynamic scene reconstruction, while both CAT4D and MoSca rely on lengthy per-scene optimization to achieve 4D reconstruction. CAT4D’s code and pretrained weights are not available, which precludes a broader empirical comparison. And, in MoSca’s case, it requires additional 2‑D tracks and depth supervision for optimization. Hence, a direct comparison is not strictly required.\n\n2. Notwithstanding the differing problem settings and supervisions, we make a comparison on the NSFF benchmark, the only common dataset on which CAT4D and MoSca report results. Using their own reported scores, our method significantly surpasses CAT4D and is on par with MoSca, while our model is trained with only photometric loss, and runs in a feed-forward manner.\n\n\n    | | PSNR↑ | SSIM↑ | LPIPS↓|  \n    |--|--|--|--|\n    | MoSca | 26.72 | - | 0.070 |\n    | CAT4D | 21.97 | 0.683 | 0.121 |\n    | BTimer | **25.82** | **0.835** | **0.086** |\n\n\n\n> **[Q2.1]** Quantitative effect of NTE module\n\n1. NTE is designed to improve reconstructions when the temporal frame is sparse or motion is fast, by refining predictions at intermediate timestamps. On standard dynamic benchmarks such as Dynamic Scenes, frames are dense and motion magnitude is limited; the standard protocal also does not evaluate in-between timestamps.  \n2. We have shown qualitative ablation in Fig6. To provide quantitative evaluation, we additionally follow the interpolation evaluation protocol in NSFF by extracting every other frame from the original Dynamic Scenes dataset. `w/ GT` means using the ground truth in-between frames which shows the upper bound of the interpolation model. NTE effectively improves on PSNR and SSIM. Note that the slight drop on LPIPS is expected, since perceptual metrics will worsen with interpolation [8].\n\n    | | PSNR↑ | SSIM↑ | LPIPS↓|  \n    |--|--|--|--|\n    | w/o NTE | 25.73 | 0.844 | 0.0786 |\n    | w/ NTE | 25.97 (+0.24) |  0.846 (+0.02) | 0.0791 (+5e-4) |\n    | w/ GT | 26.29 (+0.56) | 0.851 (+0.07)| 0.0734 (-5.2e-3)|\n\n\n    [8] The Perception-Distortion Tradeoff, CVPR 2018\n\n\n\n> **[Q2.2]** Reason for outperforming direct 3DGS prediction in fast-motion scenarios.  \n\nAs noted in L160–167, when temporal frames are sparse or motion is fast, the frame corresponding to an intermediate timestamp is absent from the context. Due to the inductive bias of pixel‑aligned 3D‑Gaussian predictor, the model struggles to produce smooth transitions. Since the NTE module is not subject to this 3D bias, it synthesizes the missing intermediate view accurately; we then pass this NTE output to BTimer for the final reconstruction.\n\n\n> **[Q3]** The model uses 12 context frames. How does BTimer scale to longer videos with more frames or higher temporal variability?\n\nBTimer can accommodate lengthy or highly dynamic sequences through several practical strategies:\n\n1. At test time we apply BTimer in a sliding window manner; thus, memory usage and latency remain constant, regardless of the total video length.  \n2. We can subsample representative key frames and pair them with the context frames corresponding to the target timestamp as input to the model. This reduces computational load while preserving global coherence.  \n3. The architecture readily accepts more frames. By replacing the standard ViT blocks with recent Mamba‑2 blocks, as adopted in Long‑LRM (ICCV 2025), we can process substantially more tokens efficiently.  \n  \n\nHence, these strategies allow BTimer to scale gracefully to longer videos and higher temporal variability without requiring fundamental architectural changes."}, {"review_id": "y0FCnhNPRJ", "reviewer": "Reviewer_HRsg", "summary": "The paper presents a method for novel view synthesis of dynamic scenes which is capable of showing the scene at a fixed point in time from unobserved viewpoints and also at interpolated time-points. For this the  paper proposes a ViT-based architecture, trained on a mixture of static and dynamic datasets, to infer a 3D Gaussian representation in a single forward pass, which can then be used to render views in real-time.", "questions": "**Major**\n1. What is the contribution of the method beyond stitching existing components together?\n2. Is there a specific reason to use different methods and different metrics for the two dataset? For example FPS is only reported for NVIDIA Dynamics Scenes (b) and SSIM is only reported for DyCheck (a).\n3. How does the method compare to the current SoTA method DynIBaR?\n\n**Minor**\n1.  What is the exact meaning or intuition of \"This is mainly caused by the inductive bias of pixel-aligned 3D Gaussian prediction\" (L164)? \n2. The paper uses a low resolution of 480 × 270 for for their quantitative evaluation and claims 150 FPS for NVIDIA Dynamics Scene (Tab. 1). How does this number change for high quality rendering (eg. Full-HD or 4K)?\n3.  BTimer and 4D-GS rely on the same underlying representation. Can you give an explanation why BTimer is nearly 3x faster at rendering?", "limitations": "The paper only contains a short high-level discussion of the method limitations and failure cases. There are several visual artefacts visible in the included in the supplementary video snippets which should have been discussed somewhere in the paper. Also all the presented scenes are reconstructed using forward facing cameras with small FoV and are only of a short duration (approx 5s), which should be included as additional limitations.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths \n\n**1. Well written:** The paper is well written, easy to follow, and the figures are well made and are a good selection to give an overview of the method.\n\n**2. Static scene evaluation:** The idea of also evaluating dynamic methods on a static setting is good. Since dynamic scenes are mostly made-up of static parts, this evaluation gives a good intuition how those parts will be reconstructed by the method.\n\n**3. Number of qualitative examples:** The authors present a high number of qualitative examples from different datasets to give a good feel of the model performance.\n\n\n## Weaknesses\n\n**1. Contribution**: The biggest limitation of the paper seems to be the limited contribution. The core of the idea is a standard ViT architecture which is trained on a mixture of mostly common datasets with a standard image reconstruction loss. The difference here to existing static \"feed-forward\" reconstruction methods is the addition of a time-embedding (which is a basic positional encoding + linear layers) and a novel time enhancer (NTE) module. Anyhow, the idea behind NTE module is mostly taken from an existing method (LVSM, Jin et al. arXiv 2024) as mentioned by the authors. The used curriculum training is also pretty straight forward with the steps 1. coarse-to-fine on static scenes, 2. dynamic co-training, 3. extension of context window size to cover more complex deformations. Since the contribution on the side of the propose method is limited, more high-quality results (see below) would be required to justify a higher score for the paper.\n\n**2. Mixed qualitative performance:** The quality of the rendered views varies between the selected scenes without a deeper discussion of this in the paper (see limitations review). It especially seems that the method has a hard time changing the perspective on the dynamic scene content in the bullet-time set-up. For example for the SORA scenes: The wave front in amalfi-coast_vid.mp4 seems to be floating above the ocean or the faces in grandma_birthday_vid.mp4 seem to deform with the selected viewpoint. Especially for high-fidelity scene contents such as the hand rolling the ball (iphone7.mp4 in the DyCheck dataset) the method seems to exhibit severe artefact's.\n\n**3. Baseline selection**: As of 2025 DynIBaR (Li et al. CVPR 2023) seems to be still the current SoTA for high-quality dynamic novel view synthesis with a reported PSNR of >30 Db on Nvidia Dynamic Scenes. Arguably, the method takes substantially longer, but should be included as the max-quality baseline. Additionally, not all baselines are evaluated on both of the used datasets. For example the strongest method on the DyCheck dataset (T-NeRF) is not also evaluated on the NVIDIA dataset, where the authors then additionally report FPS.", "was_revised": true, "final_justification": "I thank the authors for the submitted rebuttal. After reviewing the rebuttal and discussion with the other reviewers, I am willing to increase the score from \"Borderline reject\" to \"Borderline accept\". For a higher score, the method would have required either a bigger technical contribution, instead of mostly relying on repurposed existing components, or more consistent results on the evaluated datasets.", "rebuttal": "> **[W1,Q1]** What is the contribution of the method beyond stitching existing components together?\n\nWe would like to clarify several points regarding our contributions:\n1. **We propose the first feed‑forward dynamics scene reconstruction model with bullet‑time formulation.** Prior feed‑forward methods have been confined to static scenes due to (a) the challenge of modeling dynamics in a feed-forward manner and (b) the lack of large‑scale 4D supervision data. \n    - Our bullet‑time formulation extends well beyond a “standard ViT + time embedding” since it addresses both of the above issues in a novel manner, by training the network to aggregate context frames and predict the scene at an arbitrary target timestamp. This  implicitly encourages the model to become motion-aware and capture scene dynamics. To our knowledge, our formulation is the first feed-forward dynamic reconstruction method.\n    - More importantly, this formulation naturally unifies the static and dynamic reconstruction scenarios that allows us to pre-train our model on large amounts of static scene data and scales effectively across datasets, thus no longer being constrained by scarcity of 4D data. This scalability is key to generalization and performance, as shown in our results across static and dynamic benchmarks.\n\n    We believe this formulation is both conceptually novel and practically impactful, offering a significant step towards feed-forward real-time dynamic scene reconstruction.\n  \n2. Although our design is inspired by LVSM, **the NTE module differs in purpose and formulation**:\n    - NTE consumes the context frame, time embeddings for both context and target timestamps, and relative pose, whereas LVSM operates without time embedding.\n    - LVSM is designed for static reconstruction, while NTE is used within a dynamic‑reconstruction pipeline to predict missing intermediate frames under fast or sparse motion.\n    - NTE functions as an image‑space motion interpolator that complements the bullet‑timer, refining its 3DGS predictions and improving temporal consistency in challenging dynamic scenarios.\n\n    These differences make NTE a novel, task‑specific component rather than a direct reuse of LVSM.\n\n3. **Our three‑stage curriculum is deliberately crafted to leverage abundant static data while gradually introducing motion complexity.** We also introduce interpolation losses which ensures a stable convergence of the model.\n\n4. BTimer unites efficiency, robustness and fidelity. It delivers **orders of‑magnitude faster** inference than per‑scene optimization pipelines while **preserving comparable quality**. A single model generalises across a broad range of datasets with substantial distribution shifts, including real-world dynamic scenes, static scenes, AI-generated content (e.g., from Sora), and synthetic 4D environments (e.g., Kubric4D), whereas prior works train separate models for each domain.\n\nTogether, we believe our contributions form a cohesive and non-trivial advancement in feed-forward dynamic scene reconstruction and address long-standing challenges in scalability, supervision, and real-time inference.\n\n> **[W2]** Mixed qualitative performance\n\nWe appreciate the reviewer’s careful inspection of the qualitative videos and would like to clarify the observed artifacts:\n1. Our model interpolates views by aggregating information across the context frames; consequently, its accuracy is proportional to the effective baseline and pose fidelity in the input. Most Sora clips contain only subtle camera motion and lack accurate pose estimates, making dynamic‑content extrapolation shown in bullet-time setup intrinsically ill‑posed. In scenes where the baseline is larger and the poses are more reliable (e.g., amalfi‑coast, cloud‑man, robot), the reconstructions are visibly sharper. We will highlight this dependency and add analysis in the revision.\n2. Dycheck is a challenging dataset that features generic, high diverse dynamic scenes(MoSca [CVPR 2025]), where even many optimization‑based methods struggle. The `iphone 7` sequence, in particular, shows a hand moving much faster at close range, this is an edge case for many reconstruction techniques. Despite this, our feed‑forward model,\n    - outperforms several per‑scene optimisation approaches on this very clip (see the DyCheck evaluation page https://xiaoming-zhao.github.io/projects/pgdvs/videos/dycheck_eval/apple/index.html, where those approaches exhibit even more severe artefacts), \n    - matches or exceeds their quantitative scores across the entire DyCheck set, \n    - performs well on other hand sequences (`iphone4, 6, 8, 33`) with high fidelity. \n    We will add a brief discussion of this failure case and include comparisons to existing methods in revision.\n3. As noted in Point 4 of our response to W1/Q1, BTimer shows strong robustness, efficiency, and reconstruction quality across diverse datasets.\n\n> **[W3.1, Q3]** Comparison with DynIBaR.\n\n1. DynIBaR achieves high quality by performing hours of per‑scene optimisation (≈ 300 h per NSFF sequence) and by utilising additional supervision signals (optical flow + depth). In contrast, BTimer is a fully feed‑forward system: it reconstructs a scene within 1 second and is trained using only photometric loss. Given the vastly different computational budgets and data requirements, we belive that a head‑to‑head comparison is not strictly necessary for establishing our contribution.\n2.  Nevertheless, we evaluate our method using DynIBaR’s protocol and compare it with the official DynIBaR results and the reproduction by PGDVS [ICLR 2024] (*) for reference.\n\n    | | PSNR↑ | SSIM↑ | LPIPS↓|  \n    |--|--|--|--|\n    | DynIBaR | 30.86 | 0.957 | 0.027 |\n    | DynIBaR* | 29.35 | 0.934 | 0.0621 |\n    | BTimer | 26.31 | 0.852 | 0.0730 |\n\n> **[W3.2, Q2]** Not all baselines are evaluated on both of the used datasets.\n\nThanks for this suggestion.\n1. Baselines selection: we would like to clarify that we are not cherry-picking baselines. For each benchmark, we compare with methods that have official results on that benchmark, to ensure the comparison is rigorous and fair. This is exactly the same convention followed by previous dynamic‑scene reconstruction works (e.g., MoSca, CAT4D, PGDVS, Casual‑FVS, RoDynRF), where they likewise report different baseline sets across DyCheck and NVIDIA baselines for the same reason. If additional official results appear, we will incorporate them in the revision. Due to the limited rebuttal time, we were not able to re‑run T‑NeRF on the NVIDIA dataset (or DNeRF on DyCheck). However, we are happy to include these additional comparisons in the revised version.\n2. Metric Comparison:\nFor the same reason as baselines selections, to enable direct, fair comparisons with the published results of prior work, we follow each benchmark’s established protocol and adopt exactly the metrics that the existing baselines report.  On NVIDIA Dynamic Scenes, prior works (MoSca, Casual‑FVS, RoDynRF, MonoNeRF, DynNeRF) only list PSNR, LPIPS but not SSIM. We are happy to provide our SSIM score (`0.835`), however we didn’t find any official SSIM score from our compared baselines. FPS is reported in Casual‑FVS, so we adopt the same set. The reconstruction time for both two benchmarks have been reported in PGDVS. Anyhow, we will include complete metric comparison for those that are officially available in our revised version. \n3. Considering that the focus of our work is on feed-forward reconstruction setting, we believe the reported benchmarks are already sufficient to demonstrate that BTimer matches or surpasses existing per-scene optimization methods in quality while achieving several‑orders‑of‑magnitude faster runtime.\n\n> **[Q4]** meaning of L164.\n\nSince the 3D position of each Gaussian is obtained through pixel-aligned unprojection (L121-122), this introduces a geometric bias that every Gaussian is anchored to a corresponding viewing ray of the input context frame. When we query an intermediate timestamp that has no corresponding context frame, the predictor struggles to predict the gaussian at that timestamp. In supplementary (L84) we train a variant of BTimer that removes this bias, which demonstrates that our BTimer formulation can successfully recover the 3D deformation and scene flow (Fig. S5-6 of supplementary). \n\n> **[Q5]** The paper uses a low resolution of 480 × 270 for for their quantitative evaluation and claims 150 FPS for NVIDIA Dynamics Scene (Tab. 1). How does this number change for high quality rendering (eg. Full-HD or 4K)? \n\nWe additionally evaluate at 912x512 on Dynamic Scenes using the NSFF protocol. (Previous work evaluate at no higher than 288x540.) Due to limited rebuttal time, we test the existing 512x512 model without retraining a high resolution one, so quality drops somewhat. Inference rises from 0.44s to 4.48s due to the larger token count, yet it remains orders of magnitude faster than per-scene optimisation.\n\n| | PSNR↑ | SSIM↑ | LPIPS↓| Infer. Time↓ |  \n|--|--|--|--|--|\n|480x270 | 26.40 | 0.856 | 0.0714 | 0.44s |\n|912x512| 24.59 | 0.788 | 0.1264 | 4.48s|\n\n> **[Q6]** BTimer and 4D-GS rely on the same underlying representation. Can you give an explanation why BTimer is nearly 3x faster at rendering?\n\nThere are mainly two reasons: \n1. 4D‑GS stores degree‑3 spherical‑harmonic (SH) coefficients for every Gaussian, while BTimer removes this overhead by storing a single RGB value, which cuts the computation and memory bandwidth per splat. \n2. 4D-GS did not effectively reconstruct the scene from monocular video, hence more particles are used in order to overfit each view, causing the speed performance drop. Please note that we use their default configuration and directly trained on the NVIDIA benchmark, and both are tested on a single A100 GPU."}, {"review_id": "RlC2BYnvEj", "reviewer": "Reviewer_6kVt", "summary": "BTimer is a motion-aware, feed-forward model designed for real-time reconstruction and novel view synthesis of dynamic scenes. Its architecture is both simple and efficient for representing 4D dynamic scenes. The method achieves state-of-the-art performance on both static and dynamic scene datasets, including the DyCheck iPhone, NVIDIA Dynamic Scene, RE10K, and Tanks and Temples benchmarks, proving competitive even against optimization-based approaches.", "questions": "1. For a given in-the-wild video, what is the prescribed method for estimating camera poses and intrinsics?\n\n**2. Do the authors intend to release the source code and model checkpoints for re-implementation?**", "limitations": "Yes, the paper appropriately discusses its limitations in Section 5.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n1. The paper is well-written, presenting a clear methodology and a logically sound model architecture.\n\n2. The proposed model demonstrates robust performance across a diverse array of datasets, including both static and dynamic scenes. Its effectiveness is validated on real-world captures, AI-generated videos from models like Sora, and rendered scenes from the Objaverse4D dataset.\n\n3. The work addresses a significant and challenging problem within the field, and the efficacy of the proposed method is substantiated through extensive experimentation.\n\n**Weaknesses:**\n\n**0. Do the authors intend to release the source code and model checkpoints for re-implementation? It will be very helpful for the 4D reconstruction of the community.**\n\n1. To prevent \"cherry-picking of results\", it is recommended that the evaluation metrics and baselines remain consistent throughout the paper. For instance, all metrics, including Rec. Time, Render FPS, PSNR↑, SSIM↑, and LPIPS↓, should be comprehensively reported for all methods in Table 1 (a) and (b).\n\n2. The authors claim a reconstruction time of BTimer of less than 150ms (line 11). However, as indicated in **Table 1** and **Table S5**, this duration is dependent on both the resolution and the number of frames in the input video. While the efficiency of the BTimer's feed-forward architecture is acknowledged, a more qualified and rigorous conclusion regarding its runtime performance is warranted.\n\n3. Given the importance of the deformation field and scene flow for downstream applications, a discussion comparing pixel-aligned models with BTimer's approach of predicting canonical Gaussian positions (XYZ) would be a valuable addition to the main paper.\n\n\nMinors:\n\nThe item labeled as \"Figure 7\"  is a \"table\" and appears to be incorrectly captioned.", "was_revised": true, "final_justification": "Most of my concerns have been addressed, so I keep my positive score.\nI hope the authors keep their promise and incorporate the discussions and experiments (including the conclusions from other reviews) into the revision.", "rebuttal": "> **[W1]** Benchmark Comparison\n\nThank you so much for this suggestion.\n1. Baselines selection: we kindly clarify that we are not cherry-picking baselines. For each benchmark, we compare with methods that have official results on that benchmark, to ensure the comparison is rigorous and fair. This is exactly the same convention followed by previous dynamic‑scene reconstruction works (e.g., MoSca, CAT4D, PGDVS, Casual‑FVS, RoDynRF), where they likewise report different baseline sets across DyCheck and NVIDIA baselines for the same reason. If additional official results appear, we are happy to include these additional comparisons in the revised version.\n2. Metric Comparison: For the same reason as baselines selections, to enable direct, fair comparisons with the published results of prior work, we follow each benchmark’s established protocol and adopt exactly the metrics that the existing baselines report.  On NVIDIA Dynamic Scenes, prior works (MoSca, Casual‑FVS, RoDynRF, MonoNeRF, DynNeRF) routinely list PSNR, LPIPS but not SSIM. We are happy to provide our SSIM score (`0.835`), however we didn’t find any official reported SSIM score from our compared baselines. FPS is reported in Casual‑FVS, so we adopt the same set. The reconstruction time for both two benchmarks have been reported in PGDVS. \n\n    In any case, we will include complete metric comparison for those that are officially available in our revised version. As for now, we additionally evaluate our methods all three different evaluation protocols used by prior works on Dynamic Scenes, so that Reviewers can conveniently compare.\n    \n    || Reference |PSNR↑ | SSIM↑ | LPIPS↓|  \n    |--|--|--|--|--|\n    |Protocol A*|  DynNeRF [1] | 25.82 | 0.835 | 0.086 |\n    |Protocol B|NSFF [2]| 26.40 | 0.856 | 0.071 |\n    |Protocol C|DynIBaR [3]| 26.31 | 0.852 | 0.073 |\n    \n    *We used this evaluation protocol in the main paper.\n\n3. Considering that the focus of our work is on feed-forward reconstruction setting, we believe the reported benchmarks are sufficient to demonstrate that BTimer matches or surpasses existing per-scene optimization methods in quality while achieving several‑orders‑of‑magnitude faster runtime. Still, thank you so much for the suggestion.\n\n[1] Dynamic View Synthesis from Dynamic Monocular Video, ICCV 2021\n    \n[2] Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes, CVPR 2021\n    \n[3] DynIBaR: Neural Dynamic Image-Based Rendering, CVPR 2023\n\n> **[W2]** Runtime performance\n\nThank you for this valuable suggestion. To provide a more rigorous assessment of runtime, we have conducted an additional study varying both the number of context frames and the input resolution.\n| # Context frames | Resolution | Time (s) |  \n|--|--|--|\n| 4 | 256x256 | 0.02s |\n|12| 256x256 | 0.15s |\n|12| 512x512 | 1.55s |\n\nThe new results show that BTimer consistently remains in the second range (even at 512 × 512 resolution with 12 context frames), and is still several orders of magnitude faster than per‑scene optimisation pipelines. We will include these studies in the revised manuscript.\n\n\n\n> **[W3]** pixel-aligned prediction vs. XYZ prediction\n\nThank you for raising this point. Canonical XYZ prediction is commonly used in object‑centric cases for being bounded (LGM, L4GM), however pixel-aligned prediction is popular in large unbounded scene for being easy to optimize (pixelSplat, GS-LRM). Applying canonical XYZ prediction to scene data is a valuable direction that we would like to explore in future work. And we will include the comparison discussion between them in our revised version.\n\n\n\n> **[Q1]** For a given in-the-wild video, what is the prescribed method for estimating camera poses and intrinsics? \n\nGiven an in-the-wild video, we first apply per-frame Segment Anything Model to mask out pixel regions corresponding to the dynamic objects. The SAM model is prompted with several predefined categories of movable objects. In the meantime, we initialize the camera intrinsics using the GeoCalib model. We then apply DROID-SLAM on top of the video to estimate the camera poses, and the camera intrinsics is refined along with the poses in the global Bundle Adjustment process. We will release the full method and code for performing such camera annotations soon.\n\n\n\n> **[Q2]** Do the authors intend to release the source code and model checkpoints for re-implementation?\n\nYes, the code and pretrained model will be released once accepted."}, {"review_id": "f8YrywTVpw", "reviewer": "Reviewer_69bn", "summary": "This paper presents BTimer, a novel feed-forward model for real-time 3D scene reconstruction and novel view synthesis from monocular videos. Its key innovation is the introduction of a bullet-time formulation that enables flexible, scalable training and inference across arbitrary timestamps. BTimer leverages a neural rendering approach using 3D Gaussian splatting, which allows it to efficiently generate high-quality, temporally coherent views without the need for per-scene optimization. The model can handle dynamic scenes effectively, producing detailed reconstructions and novel views with minimal latency—around 150ms—outperforming prior optimization-based methods in speed, quality, and generalization. The work also demonstrates compatibility with static scenes and discusses potential applications and broader impacts.", "questions": "The pipeline for annotating internet videos—using SAM to mask dynamic objects before applying DROID-SLAM—is a very clever approach to leverage a powerful static-scene SLAM system. However, this relies on the segmentation quality and treats motion as an outlier to be removed, rather than data to be modeled.\n\nI'm curious if the authors experimented with SLAM systems that explicitly model dynamic objects? For example, methods that track moving objects separately or represent dynamic parts of the scene with their own motion models. What was the rationale for choosing the current pipeline over these alternatives? Was it primarily for robustness and scalability on diverse internet videos, or did dynamic-aware SLAM methods prove less reliable in practice?", "limitations": "Gaps in Ablation and Analysis：\\\nWhile the paper's ablation studies are generally solid, certain aspects could benefit from further exploration. For instance, using DROID-SLAM and SAM to annotate web videos is a crucial part of the data strategy. However, the robustness to annotation errors and the impact of these potential errors on final performance have not been quantitatively analyzed.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Novelty and Significance: It proposes BTimer, the first feed-forward model for this task, using an elegant \"bullet-time\" formulation to unify static and dynamic scene reconstruction.\n2. Performance: It achieves a massive speedup (reconstruction in under a second versus hours for competitors) while maintaining competitive, state-of-the-art quality on major benchmarks. \n3. High Quality: The technical approach is sound, using a ViT and 3DGS backbone, and is validated by comprehensive experiments and ablation studies that demonstrate the effectiveness of its curriculum training and Novel Time Enhancer (NTE) module.\n4. Clarity and Presentation: The paper is well-written and easy to follow. Its core ideas are supported by high-quality visualizations and thorough experimental results that convincingly validate its claims.\n\nWeakness:\n1. Dependencies: The model relies on pre-computed camera poses and requires significant computational resources for the initial training.\n2. Implicit Motion: Dynamics are modeled implicitly, without explicit motion fields, which limits applicability for editing or physical analysis.", "was_revised": false, "final_justification": "", "rebuttal": "> **[W1]** Dependencies on pre-computed camera poses and requires significant computation for the initial training. \n\n1. Most feed-forward reconstruction models need camera poses as input. Particularly in monocular dynamic scene reconstruction settings, without camera poses will add more ambiguity to the dynamics of the scene, so we use it in this work. And we would like to leave the model without camera poses for future work. We will also release the full method and code for performing camera annotations for monocular video soon.\n2. The training cost for BTimer (≈ 256 GPU‑days) is on par with other state‑of‑the‑art feed‑forward 3 D reconstruction methods—e.g., LVSM (ICLR’25) (≈ 384 GPU‑days), LRM (ICLR’24) (≈ 384 GPU‑days), and GS‑LRM (ECCV’24) (≈ 192 GPU‑days). Like these works, our model operates as an amortised algorithm: once training is complete, inference is virtually negligible. When inference cost is taken into account, per‑scene optimization pipelines rapidly become more expensive, and the gap widens as the number of scenes grows (see Fig. S1 in the supplementary).\n\n\n\n> **[W2]** Implicit Motion\n\n1. Modeling dynamics implicitly allows our model to capture flexible and complex dynamics, such as deformation with topological changes, and complex natural phenomena such as fire, waves, smoke, and dust. This is evident in our supplementary visualizations on SORA and DAVIS datasets.\n2. Despite using implicit time encoding, our model can still recover decent 3D deformation and scene flows as evidenced in Figure S5 and S6 of the supplementary, where the dynamic structure of the scenes is clearly recovered. This indicates that the model learns meaningful motion representations from time embeddings, even without explicit motion supervision.\n3. Our approach aligns with several prior works that successfully use implicit time modeling for dynamic scenes, including ST-NeRF (CVPR’21), DyNeRF (CVPR’22), and HexPlane (CVPR’23).\n\n\n\n> **[Q1]** The pipeline for annotating internet videos—using SAM to mask dynamic objects before applying DROID-SLAM—is a very clever approach to leverage a powerful static-scene SLAM system. However, this relies on the segmentation quality and treats motion as an outlier to be removed, rather than data to be modeled.\n\nThanks for the thoughtful comment. We generally observe that the segmentation quality from SAM is sufficient for the majority of internet videos. In challenging cases where semantic segmentation may fail, the original outlier rejection mechanism in DROID‑SLAM provides a complementary safeguard by regressing per‑pixel weights, which we use as a supplement to down‑weight and prevent them from corrupting the pose estimation.\n\nIt is important to note that, in this pipeline, our primary objective is to obtain reliable camera poses. As such, explicit motion modeling at this stage is not strictly required, which is why we remove them rather than model them directly. Motion information is preserved in the input frames and later learned by the BTimer network during reconstruction.\n\n\n\n> **[Q2]** I'm curious if the authors experimented with SLAM systems that explicitly model dynamic objects? For example, methods that track moving objects separately or represent dynamic parts of the scene with their own motion models. What was the rationale for choosing the current pipeline over these alternatives? Was it primarily for robustness and scalability on diverse internet videos, or did dynamic-aware SLAM methods prove less reliable in practice?\n\nThank you for the question. We did consider SLAM‑style pipelines that treat dynamic objects separately, but found them ill‑suited to our target setting of unconstrained, monocular in-the-wild videos: \n1. Limited robustness and scalability. Dynamic‑aware SLAM pipelines require reliable depth, motion segmentation, and object tracking. In practice these steps are not robust enough (PGDVS) and fail on texture‑poor, fast‑moving or out-of-distribution clips. Per‑object tracking and optimisation make such systems slower than a single feed‑forward pass and difficult to scale to large datasets.\n2. And SLAM methods typically assume known and accurate camera intrinsics, which are unavailable for the vast majority of in‑the‑wild videos.\nWith the goal of pursuing robustness, scalability and a complete generalizable framework, we make this framework design.\n\n\n\n> **[Limitation1]** Gaps  in Ablation and Analysis\n\nThank you for the suggestion. Although we have not yet performed a dedicated robustness study, several design choices (please refer to answer of Question1) already mitigate most SAM + SLAM annotation errors. We will add a detailed discussion of these factors in the final version."}]

Author rebuttal:

Meta-review: This submission introduces a feed-forward solution for dynamic scene reconstruction and novel view synthesis. The reviewers provided positive feedback (with three borderline accepts and one accept). The AC agrees with the reviewers that the problem addressed by the submission is important and that the technical solution is sound.
The AC recommends accepting the paper, subject to significant revisions, including:
- 1. The AC finds that a highly relevant discussion on feed-forward 4D reconstruction is missing. Please incorporate such a discussion in the revision. A useful reference is Section 3.4 of Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey.
- 2. Based on the comparison results in Table 1 and the additional results provided in the rebuttal with DynIBaR, the authors should moderate their state-of-the-art performance claims in the abstract. The authors are also encouraged to include comparisons with NeRFPlayer and Shape of Motion.
- 3. Please include an inference speed performance table and explicitly state the image resolution when referring to the reported 150 ms per frame.
- 4. Improve the clarity of the misleading points pointed out by the reviewers.
Decision: Accept (poster)