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DYNAMIC NOVEL VIEW SYNTHESIS FROM UNSYNCHRONIZED VIDEOS USING GLOBAL-LOCAL MOTION CONSISTENCY PRIOR

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
Dynamic novel view synthesisUnsynchronized multi-view videosGlobal-local motion consistency
Abstract:

Dynamic novel view synthesis (D-NVS) critically depends on hardware-based synchronization. Current approaches that accommodate unsynchronized settings within the widely-used NeRF or GS frameworks often struggle with local minima, particularly in textureless scenes or when multi-view videos exhibit large misalignments. To tackle this issue, we propose a novel 3D global–2D local motion consistency prior, which evaluates the alignment between predicted scene flow projections and pre-computed optical flows across multi-view videos. Our analysis reveals that the motion, produced by the anisotropy of projected global scene flow across different views, is inherently more effective for correcting temporal misalignments compared to the near-isotropic appearance typically leveraged in NeRF or GS. Extensive experiments on public datasets demonstrate the versatility of our loss function across various D-NVS architectures (NeRF and GS), achieving a 50% reduction in synchronization errors and a PSNR improvement of up to 4dB, thereby outperforming existing state-of-the-art methods.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a global-local motion consistency prior for dynamic novel view synthesis from unsynchronized multi-view videos. It resides in the 'Joint Offset Optimization with Neural Radiance Fields' leaf, which contains only three papers total, including the original work. This leaf sits within the broader 'Temporal Alignment and Synchronization Methods' branch, indicating a focused research direction rather than a crowded subfield. The taxonomy reveals that while temporal alignment is an active area, the specific approach of joint offset optimization with NeRF remains relatively sparse compared to other synchronization strategies.

The taxonomy structure shows neighboring leaves addressing temporal misalignment through alternative representations: 'Gaussian Splatting with Temporal Deformation' explores per-Gaussian embeddings, while 'Video Alignment and Registration Techniques' employs explicit correspondence methods. The paper's emphasis on motion consistency distinguishes it from sibling works that primarily optimize time offsets within standard NeRF frameworks. The broader 'High-Speed and Asynchronous Capture Systems' branch addresses related hardware-level solutions, but excludes methods like this one that assume standard multi-camera setups without specialized triggering. This positioning suggests the work bridges explicit alignment optimization with motion-based regularization.

Among twenty-nine candidates examined, the global-local motion consistency prior (Contribution 1) and its integration with NeRF/GS architectures (Contribution 2) show no clear refutation across ten and nine candidates respectively. The reliability masking strategy (Contribution 3) appears refuted by two of ten candidates examined, suggesting some overlap with existing optical flow filtering techniques. The limited search scope means these findings reflect top-K semantic matches rather than exhaustive coverage. The core motion consistency framework appears more distinctive than the masking component, though the analysis cannot rule out relevant prior work beyond the examined candidates.

Based on the limited literature search, the work appears to occupy a relatively sparse position within temporal alignment methods, with its motion-based consistency approach differentiating it from offset-only optimization. The taxonomy reveals only two sibling papers in the same leaf, and the contribution-level analysis found minimal overlap among examined candidates. However, the thirty-candidate scope and two refutable pairs indicate this assessment reflects available signals rather than comprehensive field coverage.

Taxonomy

Core-task Taxonomy Papers
15
3
Claimed Contributions
29
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: dynamic novel view synthesis from unsynchronized multi-view videos. The field addresses the challenge of reconstructing dynamic 3D scenes when input cameras are not temporally aligned, a common scenario in practical multi-camera setups. The taxonomy reveals several complementary research directions: Temporal Alignment and Synchronization Methods focus on explicitly estimating or optimizing time offsets between cameras, often jointly with scene reconstruction; High-Speed and Asynchronous Capture Systems explore hardware and algorithmic solutions for handling cameras with varying frame rates or trigger times; Scene-Specific Reconstruction Approaches tailor methods to particular content types such as human performance or controlled environments; Geometry-Guided and Consistency-Based Methods leverage geometric priors and cross-view consistency to regularize reconstruction; and Data-Driven Reconstruction Frameworks employ learned models to handle temporal misalignment implicitly. Representative works like Sync-NeRF[7] and Dynamic Gaussian Unsynchronized[5] illustrate how neural radiance fields and Gaussian splatting can be adapted to this setting, while earlier efforts such as Asynchronous Camera Array[8] and Time Slice Alignment[9] established foundational techniques for temporal correspondence. Recent progress highlights a tension between explicit offset estimation and implicit temporal modeling. Some approaches, including Sync-NeRF[7] and No Video Synchronization[11], optimize per-camera time offsets alongside scene parameters, enabling joint refinement of alignment and geometry. Others, such as 4DSlomo[3] and Per-Gaussian Deformation[4], model continuous temporal dynamics to interpolate or extrapolate frames without requiring strict synchronization. Global-Local Motion Consistency[0] sits within the joint offset optimization branch, closely related to Sync-NeRF[7] and No Video Synchronization[11], but emphasizes consistency constraints at both global scene and local motion levels to improve robustness. Compared to Sync-NeRF[7], which primarily optimizes offsets within a NeRF framework, Global-Local Motion Consistency[0] introduces hierarchical motion priors to handle complex deformations more effectively. This positioning reflects ongoing exploration of how to balance explicit temporal alignment with flexible motion modeling in unsynchronized multi-view capture.

Claimed Contributions

Global-local motion consistency prior for unsynchronized dynamic novel view synthesis

The authors introduce a motion consistency prior that aligns projected 3D scene flows with precomputed 2D optical flows from multi-view videos. This prior exploits the anisotropic nature of projected global scene flow across different views to correct temporal misalignments more effectively than appearance-based methods.

10 retrieved papers
Global-local motion consistency loss function integrated with NeRF and Gaussian Splatting

The authors develop a loss function that compares projected scene flows with precomputed optical flows and integrate it with popular dynamic novel view synthesis frameworks including dynamic NeRF and Gaussian Splatting. This enables joint optimization of scene geometry and temporal offsets.

9 retrieved papers
Reliability masking strategy for filtering unreliable optical flow predictions

The authors propose a binary reliability mask that selects pixels with the largest 50% optical flow amplitudes to filter out unreliable flow predictions in low-texture or textureless regions. This strategy improves robustness by focusing supervision on dynamically reliable regions.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Global-local motion consistency prior for unsynchronized dynamic novel view synthesis

The authors introduce a motion consistency prior that aligns projected 3D scene flows with precomputed 2D optical flows from multi-view videos. This prior exploits the anisotropic nature of projected global scene flow across different views to correct temporal misalignments more effectively than appearance-based methods.

Contribution

Global-local motion consistency loss function integrated with NeRF and Gaussian Splatting

The authors develop a loss function that compares projected scene flows with precomputed optical flows and integrate it with popular dynamic novel view synthesis frameworks including dynamic NeRF and Gaussian Splatting. This enables joint optimization of scene geometry and temporal offsets.

Contribution

Reliability masking strategy for filtering unreliable optical flow predictions

The authors propose a binary reliability mask that selects pixels with the largest 50% optical flow amplitudes to filter out unreliable flow predictions in low-texture or textureless regions. This strategy improves robustness by focusing supervision on dynamically reliable regions.

Table of Contents