Title: HAIF-GS: Hierarchical and Induced Flow-Guided Gaussian Splatting for Dynamic Scene
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D dynamic reconstruction; computer vision
Abstract: Reconstructing dynamic 3D scenes from monocular videos remains a fundamental challenge in 3D vision. While 3D Gaussian Splatting (3DGS) achieves real-time rendering in static settings, extending it to dynamic scenes is challenging due to the difficulty of learning structured and temporally consistent motion representations. This challenge often manifests as three limitations in existing methods: redundant Gaussian updates, insufficient motion supervision, and weak modeling of complex non-rigid deformations. These issues collectively hinder coherent and efficient dynamic reconstruction. To address these limitations, we propose HAIF-GS, a unified framework that enables structured and consistent dynamic modeling through sparse anchor-driven deformation. It first identifies motion-relevant regions via an Anchor Filter to suppress redundant updates in static areas. A self-supervised Induced Flow-Guided Deformation module induces anchor motion using multi-frame feature aggregation, eliminating the need for explicit flow labels. To further handle fine-grained deformations, a Hierarchical Anchor Propagation mechanism increases anchor resolution based on motion complexity and propagates multi-level transformations. Extensive experiments on synthetic and real-world benchmarks validate that HAIF-GS significantly outperforms prior dynamic 3DGS methods in rendering quality, temporal coherence, and reconstruction efficiency.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel method for monocular dynamic reconstruction, named HAIF-GS, which enhances sparse motion anchor-based deformation with anchor filters, self-supervised induced flow and anchor densify mechanism to capture fine-grained and complex deformation fields. Experimental results show that HAIF-GS achieves state-of-the-art performance across various scenes on two popular datasets, and the visual comparison is convincing.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:** \n\n1. This paper proposes a novel method for monocular dynamic reconstruction, named HAIF-GS, achieving state-of-the-art performance across various scenes.\n2. The architecture of induced flow-guided deformation as well as the cycle loss, is interesting and intuitive.\n3. The paper is well-written and well-organized.\n\n**Weakness:**\n\nMy major concerns about this paper are the experiment settings, especially the comparison with \"MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds\" / \"Shape of Motion:\n4D Reconstruction from a Single Video\". I've found that when talking about reconstructing dynamic 3D scenes from monocular videos, existing works either follow MoSca/SoM and conduct experiments on DyChek/iphone/DAVIS datasets or compare with 4DGS / Deformable 3D-GS. I believe clarification is needed in the introduction or limitation section. Can this method apply to iphon/DAVIS dataset?  If not, please give some explanation of the limitations and some failure cases. If yes, can you provide some comparison with MoSca/SoM? **I'll raise my rating if this point is well-discussed in the rebuttal.**\n\nHere are some minor concerns, mainly on the readability of this paper.\n1. From lines 184-192, I highly recommend adding more formulas to describe the architecture. Without the help of Figure 2, it's difficult to understand the design of MLPs.\n2. For the ablation studies, can you provide the visual comparison of scene flows with and without $L_{cycle}$ to validate the effectiveness of this loss function?\n3. Also, for the ablation studies, can you provide the visual comparison of anchor points before and after densification to indicate that the densification mainly works on the regions containing complex and fine-grained motions?\n\nQUESTIONS:\nSee \"Weakness\"\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work first mentions three limitations in existing monocular dynamic 3DGS methods, i.e. redundant Gaussian updates, insufficient motion supervision, and weak modeling of complex non-rigid deformations. This work further proposes HAIF-GS for these issues, which includes Anchor Filter for identifying motion-relevant regions, Induced Flow-Guided Deformation using multi-frame feature aggregation, and Hierarchical Anchor Densification for progressively modeling fine-grained deformations.\n\nSTRENGTHS AND WEAKNESSES:\n* Strengths\n  * This work proposes the pipeline HAIF-GS for monocular dynamic 3DGS, which mainly includes motion anchors (the motion basis for modeling motions), an induced flow-guided deformation module, and a coarse-to-fine hierarchical anchor densification strategy. These modules are interesting and make sense.\n\n\n* Weakness\n  * This work mentioned that \"Figure 1 (b) shows hand motion, where the fine-scale appearance and localized movement highlight the challenges of modeling fine-grained dynamics in dynamic reconstruction\". But this claim cannot effectively convince me, since 4DGS and D-3DGS are not designed for the monocular setting. The qualitative experiments shown in Figure 1 can most importantly illustrate that previous multi-view dynamic 3DGS methods cannot be effectively generalized to the monocular setting. By the way, Figure 1 (a) does not appear to be referenced in the main manuscript. \n  * Although this work proposes a series of interesting designs, it lacks sufficient experiments to demonstrate the advancedness and effectiveness of its sophisticated design. Specifically, more quantitative experiments of previous monocular dynamic 3DGS methods are supposed to be implemented, since current reported results of dynamic methods mainly focus on multi-view methods. This work should complement the already open-sourced monocular dynamic 3DGS methods, like Shape of Motion [A] and MoSca [B]. Furthermore, more quantitative experimental results of ablation combinations should be added. At least the baseline (w/o IF, L_{cycle}, AF, HAD) result is supposed to be reported.\n  * Typos. \"suppresses\" to \"suppress\" in Line 11.\n\n[A] Wang Q, Ye V, Gao H, et al. Shape of motion: 4d reconstruction from a single video[J]. arXiv preprint arXiv:2407.13764, 2024.\n\n[B] Lei J, Weng Y, Harley A W, et al. Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 6165-6177.\n\nQUESTIONS:\n* After reviewing the method proposed in this work, I was a little worried about its training and rendering efficiency, but I didn't find relevant descriptions in the submitted manuscript. What is the training and rendering time of the proposed method?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose HAIF-GS, a sparse anchor-based dynamic Gaussian Splatting algorithm.\nIt separates dynamic and static anchors with an Anchor Filter, a Induced Flow-Guided Deformation module to estimate anchor motion with multi-frame feature aggregation to encourage temporal consistency, and a Hierarchical Anchor Propagation mechanism to help model complex motion with level-of-details.\nExperiments on D-NeRF and NeRF-DS shows superior quality over previous dynamic Gaussian baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n- The proposed method shows consistent higher performance over other baselines in Table 1 and Table 2.\n- Anchor Filter, Inductive-Flow-Guided Deformation Module and Hierarchical Anchor Propagation mechanism are all novel and effective based on comprehensive ablation study in Table 3 and Figure 5.\n- Hierarchical sparse-anchor idea is novel and intuitive by introducing level-of-details into Sparse-Anchor-based GS.\n- The IFGD module induces forward/backward scene flow does not need ground-truth labels, and HAIF-GS is trained end-to-end in both training stages.\n\n\nWeakness:\n- Efficiency of the proposed method is listed as an advantage (e.g. in Line 43, \" a unified framework that significantly improves both the efficiency and accuracy of dynamic scene reconstruction\"), but no experimental results are yet to support the claim; also memory footprint is listed as a limitation in section A.5.\n- HAIF-GS is qualitatively not necessarily better than 4DGS in Figure 4.\n- SP-GS, as a sparse-anchor based method, is not compared quantitatively in Table 1 and Table 2, as a recent sparse-anchor competitor.\n- Results are only shown in D-NeRF and NeRF-DS dataset, which are from limited scenarios.\n- The paper currently lacks a clear and consistent storytelling trajectory. For example, in abstract, \"This challenge often manifests as three limitations in existing method...\", but in introduction, instead of expanding on the three limitations, the authors start to talk about two factors: \"This limitation is mainly due to two factors...\" \n- Lacking visualization of induced scene flow / static-dynamic separation.\n- The systems have a lot of hyperparameter to tune.\n\nQUESTIONS:\nI would love to see how HAIF-GS performs on more datasets, especially real-world ones with bigger movement like HyperNeRF dataset because a lot of the innovative components could be very sensitive to optimization curve and might fail if not enough multi-view clue is given. It's okay if it fails and the authors can list this as another limitation.\nI would also love to see dynamic results in video format if this is allowed during rebuttal.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes a framework, called HAIF-GS, for dynamic 3D reconstruction. The key to the proposed method is: 1) a sparse-anchor based motion representation, 2) a self-supervised induced flow-guided deformation module, 3) a hierarchical anchor propagation mechanism.

STRENGTHS AND WEAKNESSES:
Strength: 
* The induced flow-guided deformation module is a reasonable design.
* The proposed method surpasses existing baselines in quantitative evaluation.
Weakness:
* The design of sparse and hierarchical anchor-based motion representation has already be seen in existing works, for example: Y. Liang et. al., “HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation”, CVPR 2025.

QUESTIONS:
Can authors provide some results (at least qualitative) for dynamic-static decomposition? (For example, render segmentation maps in novel view synthesis)

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03