Title: Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
Venue: NeurIPS 2025
Accepted: yes
Keywords: Dynamic scene reconstruction

Generalizable Reconstruction

Reconstruction from Monocular 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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nThe 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?\n\nLIMITATIONS:\nGaps 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nBTimer 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?**\n\nLIMITATIONS:\nYes, the paper appropriately discusses its limitations in Section 5.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n**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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1) 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?\n\nLIMITATIONS:\nNo limitations discussed. See Weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-04"}