Beyond Rigid: Benchmarking Non-Rigid Video Editing
- URL: http://arxiv.org/abs/2601.18340v1
- Date: Mon, 26 Jan 2026 10:28:09 GMT
- Title: Beyond Rigid: Benchmarking Non-Rigid Video Editing
- Abstract summary: We propose NRVBench, the first benchmark designed to evaluate non-rigid video editing.<n>First, we curate a high-quality dataset consisting of 180 non-rigid motion videos from six physics-based categories.<n>Second, we propose NRVE-Acc, a novel evaluation metric based on Vision-Language Models.<n>Third, we introduce a training-free baseline, VM-Edit, which utilizes a dual-region denoising mechanism to achieve structure-aware control.
- Score: 39.52926053968504
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite the remarkable progress in text-driven video editing, generating coherent non-rigid deformations remains a critical challenge, often plagued by physical distortion and temporal flicker. To bridge this gap, we propose NRVBench, the first dedicated and comprehensive benchmark designed to evaluate non-rigid video editing. First, we curate a high-quality dataset consisting of 180 non-rigid motion videos from six physics-based categories, equipped with 2,340 fine-grained task instructions and 360 multiple-choice questions. Second, we propose NRVE-Acc, a novel evaluation metric based on Vision-Language Models that can rigorously assess physical compliance, temporal consistency, and instruction alignment, overcoming the limitations of general metrics in capturing complex dynamics. Third, we introduce a training-free baseline, VM-Edit, which utilizes a dual-region denoising mechanism to achieve structure-aware control, balancing structural preservation and dynamic deformation. Extensive experiments demonstrate that while current methods have shortcomings in maintaining physical plausibility, our method achieves excellent performance across both standard and proposed metrics. We believe the benchmark could serve as a standard testing platform for advancing physics-aware video editing.
Related papers
- OmniEdit-Bench: A Comprehensive Benchmark for Instruction-based Video Editing [53.21868575804758]
We introduce a comprehensive and structured benchmark for instruction-based video editing (IVE)<n>Our benchmark decomposes editing tasks into multiple video-specific dimensions, including spatial, temporal, audio, and reference-based editing.<n>We propose an evaluation framework that assesses editing quality from four complementary dimensions: accuracy, preservation, realism, and consistency.
arXiv Detail & Related papers (2026-08-05T16:58:48Z) - PhyEditBench: A Real-World Multi-Stage Benchmark for Physics-Aware Image Editing [14.070238906847488]
We introduce PhyEditBench, a benchmark designed to assess the physical understanding of editing models.<n>It comprises 238 high-quality, high-resolution, real-world instances meticulously extracted from videos.<n>Our empirical analysis of current SOTA editing methods exposes substantial limitations in their physics-based reasoning.
arXiv Detail & Related papers (2026-06-25T02:57:34Z) - Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding [98.3098451637867]
Video-MME-v2 is a comprehensive benchmark designed to rigorously evaluate the robustness and faithfulness of video understanding.<n>To guarantee data quality, Video-MME-v2 is constructed through a rigorously controlled human annotation pipeline.
arXiv Detail & Related papers (2026-04-06T17:59:56Z) - SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing [76.349958946335]
We present SAMA (factorized Semantic Anchoring and Motion Alignment), a framework that factorizes video editing into semantic anchoring and motion modeling.<n>First, we introduce Semantic Anchoring, which establishes a reliable visual anchor by jointly predicting semantic tokens and video latents at sparse anchor frames.<n>Second, Motion Alignment pre-trains the same backbone on motion-centric video restoration pretext tasks.
arXiv Detail & Related papers (2026-03-19T17:59:51Z) - FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing [97.35186681023025]
We introduce FFP-300K, a new large-scale dataset of high-fidelity video pairs at 720p resolution and 81 frames in length.<n>We propose a novel framework designed for true guidance-free FFP that resolves the tension between maintaining first-frame appearance and preserving source video motion.
arXiv Detail & Related papers (2026-01-05T01:46:22Z) - Content Adaptive based Motion Alignment Framework for Learned Video Compression [72.13599533975413]
This paper proposes a content adaptive based motion alignment framework.<n>We first introduce a two-stage flow-guided deformable warping mechanism that refines motion compensation with coarse-to-fine offset prediction and mask modulation.<n>Second, we propose a multi-reference quality aware strategy that adjusts distortion weights based on reference quality, and applies it to hierarchical training to reduce error propagation.<n>Third, we integrate a training-free module that downsamples frames by motion magnitude and resolution to obtain smooth motion estimation.
arXiv Detail & Related papers (2025-12-15T02:51:47Z) - ReViSE: Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning [57.08352504712699]
Video unified models exhibit strong capabilities in understanding and generation, yet they struggle with reason-informed visual editing.<n>We introduce the Reason-Informed Video Editing (RVE) task, which requires reasoning about physical plausibility and causal dynamics during editing.<n>We propose ReViSE, a framework that unifies generation and evaluation within a single architecture.
arXiv Detail & Related papers (2025-12-10T18:57:09Z) - FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing [23.172235115031018]
We present FreqEdit, a training-free framework that enables stable editing across 10+ consecutive iterations.<n>Our approach comprises three synergistic components: (1) high-frequency feature injection from reference velocity fields to preserve fine-grained details, (2) an adaptive injection strategy that spatially modulates injection strength for precise region-specific control, and (3) a path compensation mechanism that periodically recalibrates the editing trajectory to prevent over-constraint.
arXiv Detail & Related papers (2025-12-01T15:00:47Z) - PhysCorr: Dual-Reward DPO for Physics-Constrained Text-to-Video Generation with Automated Preference Selection [10.498184571108995]
We propose PhysCorr, a unified framework for modeling, evaluating, and optimizing physical consistency in video generation.<n>Specifically, we introduce PhysicsRM, the first dual-dimensional reward model that quantifies both intra-object stability and inter-object interactions.<n>Our approach is model-agnostic and scalable, enabling seamless integration into a wide range of video diffusion and transformer-based backbones.
arXiv Detail & Related papers (2025-11-06T02:40:57Z) - VideoScore2: Think before You Score in Generative Video Evaluation [69.43069741467603]
VideoScore2 is a multi-dimensional, interpretable, and human-aligned framework that explicitly evaluates visual quality, text-to-video alignment, and physical/common-sense consistency.<n>Our model is trained on a large-scale dataset VideoFeedback2 containing 27,168 human-annotated videos.
arXiv Detail & Related papers (2025-09-26T18:09:03Z) - Low-Cost Test-Time Adaptation for Robust Video Editing [4.707015344498921]
Video editing is a critical component of content creation that transforms raw footage into coherent works aligned with specific visual and narrative objectives.<n>Existing approaches face two major challenges: temporal inconsistencies due to failure in capturing complex motion patterns, and overfitting to simple prompts arising from limitations in UNet backbone architectures.<n>We present Vid-TTA, a lightweight test-time adaptation framework that personalizes optimization for each test video during inference through self-supervised auxiliary tasks.
arXiv Detail & Related papers (2025-07-29T14:31:17Z) - DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models [1.972901110298768]
We propose DAPE, a high-quality yet cost-effective two-stage parameter-efficient fine-tuning framework for video editing.<n>In the first stage, we design an efficient norm-tuning method to enhance temporal consistency in generated videos.<n>The second stage introduces a vision-friendly adapter to improve visual quality.
arXiv Detail & Related papers (2025-05-11T17:08:50Z) - Zero-Shot Video Editing through Adaptive Sliding Score Distillation [51.57440923362033]
This study proposes a novel paradigm of video-based score distillation, facilitating direct manipulation of original video content.
We propose an Adaptive Sliding Score Distillation strategy, which incorporates both global and local video guidance to reduce the impact of editing errors.
arXiv Detail & Related papers (2024-06-07T12:33:59Z) - E2HQV: High-Quality Video Generation from Event Camera via
Theory-Inspired Model-Aided Deep Learning [53.63364311738552]
Bio-inspired event cameras or dynamic vision sensors are capable of capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range.
It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization.
We propose textbfE2HQV, a novel E2V paradigm designed to produce high-quality video frames from events.
arXiv Detail & Related papers (2024-01-16T05:10:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.