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FastVMT: Eliminating Redundancy in Video Motion Transfer

Yue Ma, Zhikai Wang, Tianhao Ren, Mingzhe Zheng, Hongyu Liu, Jiayi Guo, Kunyu Feng, Yuxuan Xue, Zixiang Zhao, Konrad Schindler, Qifeng Chen, Linfeng Zhang

arXiv:2602.05551v3cs.CV

TL;DR

Video motion transfer generates prompt-conditioned content while retaining reference motion, but existing pipelines contain motion and gradient redundancy. FastVMT addresses these redundancies with local correspondence search and skipped gradient computation, achieving a 3.43× average speedup without compromising visual fidelity or temporal consistency.

  • Problem

    Existing video motion-transfer implementations contain motion and gradient redundancy that leaves structural sources of computational inefficiency unaddressed.

    Source: §1 · p. 2

  • Method

    FastVMT combines sliding-window local motion-correspondence search with step-skipping gradient computation to address motion and gradient redundancy.

    Source: §5 · p. 9

  • Results

    3.43× average speedup without compromising visual fidelity or temporal consistency.

    Source: §5 · p. 10

  • Takeaways & Limitations

    The authors conclude that this work points toward more efficient and practical generative video motion transfer.

    Source: §5 · p. 10

    FastVMT remains bounded by the pretrained video backbone, making out-of-distribution prompts or motions and semantically conflicting conditions challenging.

    Source: §F.1 · p. 22

Abstract

from arXiv · show

Video motion transfer aims to synthesize videos by generating visual content according to a text prompt while transferring the motion pattern observed in a reference video. Recent methods predominantly use the Diffusion Transformer (DiT) architecture. To achieve satisfactory runtime, several methods attempt to accelerate the computations in the DiT, but fail to address structural sources of inefficiency. In this work, we identify and remove two types of computational redundancy in earlier work: motion redundancy arises because the generic DiT architecture does not reflect the fact that frame-to-frame motion is small and smooth; gradient redundancy occurs if one ignores that gradients change slowly along the diffusion trajectory. To mitigate motion redundancy, we mask the corresponding attention layers to a local neighborhood such that interaction weights are not computed unnecessarily distant image regions. To exploit gradient redundancy, we design an optimization scheme that reuses gradients from previous diffusion steps and skips unwarranted gradient computations. On average, FastVMT achieves a 3.43x speedup without degrading the visual fidelity or the temporal consistency of the generated videos.

1 INTRODUCTION

FastVMT targets motion-embedding and gradient-computation redundancy in training-free video motion transfer. It combines local motion correspondence search with reuse of gradients across diffusion steps, achieving substantial speedups while preserving video quality.

1 INTRODUCTION

  • Motion transfer generates a novel video that follows reference-video dynamics while preserving the target’s appearance and semantics, enabling controllable motion synthesis.Examples include transferring an actor’s action to a target subject while preserving identity, or replicating a camera path in a target scene.Source: §1 · p. 2
  • Training-free motion transfer avoids per-reference-video fine-tuning, inverting the reference into DiT embeddings before text-guided denoising uses gradients between source and target motion embeddings.Training-based methods fine-tune the diffusion backbone and can require up to 2 hours on an A100 GPU for each new reference video.Source: §1 · p. 2
  • A sliding-window strategy on downsampled attention maps and a corresponding window loss performs more reliable, efficient local motion-correspondence search than token-by-token extraction.The local search reflects the limited magnitude and local smoothness of frame-to-frame motion.Source: §1 · p. 2
  • FastVMT skips gradient computations at selected iterations and reuses the most recent gradients between them, exploiting the similarity of consecutive updates.The paper characterizes motion transfer as stable gradient optimization.Source: §1 · p. 2
  • FastVMT achieves state-of-the-art qualitative and quantitative performance, with up to 14.91× lower latency and a 3.43× speedup with minimal degradation versus the original training-free pipeline.It preserves near-lossless quality across various evaluation metrics.Source: §1 · p. 3

2 RELATED WORK

Prior work advances text-to-video generation with temporal UNets and Diffusion Transformers, while motion-transfer methods separate appearance from motion using explicit controls or learned and inference-time mechanisms. FastVMT targets redundancy in training-free motion transfer with sliding-window motion extraction and step skipping.

Text-to-video generation

Video motion transfer

3 METHOD

FastVMT is a training-free DiT-based framework that generates a prompt-guided video while preserving motion and camera-pose changes from a reference video. It addresses motion and gradient redundancy through local attention windows, a consistency loss, and step-skipping gradient reuse.

3.1 MOTIVATION

  • FastVMT identifies motion redundancy because nearby regions, rather than distant tokens, are the relevant correspondences for motion extraction.It uses sliding-window extraction to reduce incorrect matches and support motion consistency.Source: §3.1 · p. 4

3.2 EFFICIENT ATTENTION WINDOW

  • Constraining attention computations temporally and spatially extracts motion flow from relevant query-key pairs and reduces temporal complexity from O(F^2) to O(F).The method uses video DiT self-attention and a sliding window around locally relevant keys, with minimal quality loss.Source: §3.2 · pp. 4–6

3.3 CORRESPONDING-WINDOW LOSS

  • FastVMT combines weighted AMF displacement alignment with a corresponding-window loss on key representations to improve motion transfer accuracy and temporal stability.The loss weights are λAMF = 5 and λtrack = 1.Source: §3.3 · p. 6

3.4 STEP-SKIPPING GRADIENT OPTIMIZATION

  • FastVMT reuses the most recently computed gradient between interval-based computations, reducing backpropagation while maintaining motion-transfer quality.Gradients are recomputed when j mod ∆ = 0; otherwise the cached gradient guides optimization.Source: §3.4 · pp. 6–7

4 EXPERIMENTS

FastVMT is reported to outperform baseline methods across motion-transfer evaluations, while ablations examine how sliding-window extraction, corresponding-window loss, and gradient reuse affect quality and efficiency.

4.1 IMPLEMENTATION DETAILS

  • Experiments use WAN-2.1 with 50 denoising steps and default outputs of 480 × 832 resolution and 81 frames.Latent tiling uses tile_size=(30, 52) and tile_stride=(15, 26); AMF guidance runs during the first 20% of denoising steps, with 10-step latent-only AdamW optimization and linear learning-rate decay from 0.003 to 0.002.Source: §4.1 · p. 7

4.3 ABLATION STUDY

  • Removing sliding-window motion extraction degrades multiple metrics and increases computational overhead and inference time.The ablation also reports slight reductions in motion fidelity and temporal consistency without sliding windows, and more accurate motion extraction in a middle DiT layer.Source: Table 1 · p. 8, Figure 7 · p. 8, §4.3 · p. 9
  • Excluding the corresponding-window loss substantially degrades motion fidelity, while including it increases processing time by less than 1%.The loss is reported to constrain temporal inconsistencies and support robust motion alignment.Source: Table 1 · p. 8, Figure 7 · p. 8, §4.3 · p. 9
  • Step-skipping gradient reuse substantially reduces computation time with negligible impact on motion fidelity and temporal consistency.The ablation reports preserved video-generation quality alongside the time savings.Source: Table 1 · p. 8, Figure 7 · p. 8, §4.3 · p. 9

4.2 COMPARISON WITH BASELINES

  • FastVMT is reported as the fastest method and as outperforming other methods on automated metrics and user preferences.The comparison evaluates 50 DAVIS videos and includes motion fidelity, temporal consistency, text similarity, and a 20-volunteer user study.Source: §4.2 · p. 8, Table 2 · p. 9
  • FastVMT significantly outperforms all baselines across the four reported VBench video-quality metrics.The metrics are subject consistency, motion smoothness, aesthetic quality, and background consistency.Source: §4.2 · p. 8, Table 2 · p. 9
  • Qualitative comparisons report aligned movement patterns and preserved spatial relationships between moving subjects, including in complicated interactions.The comparison uses Wan-2.1 as the shared backbone.Source: §4.2 · p. 8, Figure 8 · p. 9

5 CONCLUSION

FastVMT is a training-free video motion transfer framework that addresses motion and gradient redundancy. It combines local motion correspondence search with step-skipping gradient computation and achieves a 3.43× average speedup without compromising visual fidelity or temporal consistency.

  • FastVMT addresses motion redundancy with a sliding-window strategy and corresponding window loss, and gradient redundancy with step-skipping gradient computation.The sliding-window strategy aims to make local motion-correspondence search more reliable and efficient; the framework is training-free.Source: §5 · pp. 9–10
  • Middle DiT attention layers exhibit stronger token correspondence performance than other layers.Source: Figure 9 · p. 10
  • 3.43× average speedup was achieved without compromising visual fidelity or temporal consistency.Source: §5 · p. 10

REPRODUCIBILITY STATEMENT

The paper states that its results are reproducible from unaltered model outputs, with materials and fixed settings intended to support independent reproduction.

REPRODUCIBILITY STATEMENT

  • Quantitative tables, qualitative images, and videos use raw model outputs, except for minimal format conversion and compression, and independent runs should match within expected stochastic variation.Fixed configuration files and random seeds will be provided.Source: REPRODUCIBILITY STATEMENT · p. 10
  • After review, the authors will release a partial public repository with inference scripts, example data, and videos; training and evaluation datasets, configurations, and procedures are documented.The documentation is in Section 4.1 and Appendix 5.Source: REPRODUCIBILITY STATEMENT · p. 10

ETHICS STATEMENT … B MORE DETAILS ABOUT IMPLEMENTATION

The ethics statement describes steps to reduce representational bias and safeguards against harmful uses, including conditions for dataset and code release. The appendix contents list implementation details, further results, limitations, and a video demo, but the supplied passages do not describe their findings or specifics.

ETHICS STATEMENT

  • The work studies motion-transfer video editing and proposes a dataset of people, vehicles, and landscape camera-motion videos, with curated examples spanning races, genders, and styles to mitigate representational bias.Source: ETHICS STATEMENT · p. 10
  • Illustrative videos come from public web content used for research, and examples span races, genders, and styles to mitigate representational bias.The dataset includes people, vehicles, and landscape camera motions.Source: ETHICS STATEMENT · p. 10
  • The dataset will not be publicly released before AI-generated watermarking and an ethics/content-safety audit; public code will use visible and/or machine-detectable inference watermarking.Future releases will prohibit impersonation, harassment, and other malicious applications, and content raising privacy, legal, or safety concerns may be removed or restricted.Source: ETHICS STATEMENT · p. 10

B More details about Implementation S1

  • The appendix lists implementation details, related work, human evaluation, and corresponding-window loss as topics in section B.The supplied passages provide these subsection headings but no further details.Source: B More details about Implementation S1 · p. 17

D More Results S4

  • The appendix lists more visual results, ablation results, and quantitative comparisons in section D, without supplying their findings here.Source: D More Results S4 · p. 17

F Limitations and Potential Social Impact S6

A DYNAMIC VIDEOS

B.1 IMPLEMENTATION DETAILS … D.1 MORE VISUAL RESULTS

The supplementary sections specify FastVMT’s optimization and tracking-loss setup, describe its user study, and present additional motion-transfer comparisons and visual cases.

B.1 IMPLEMENTATION DETAILS

  • With 3×4-token tiles, temporal span 5, search window 21, and temperature 1.0, the implementation recomputes Q/K and tile-AMF every 3 inner-loop steps.The weighted squared ℓ2 AMF loss spans temporal offsets, and cached gradients are reused on intermediate steps; only latent x is updated.Source: §B.1 · p. 17

B.2 RELATED WORK

  • FastVMT’s algorithm aligns noise levels, extracts self-attention features, computes displacement-based loss, optimizes the generating latent, and applies optimization only while t<Topt.The algorithm then denoises the generating latent at each iteration.Source: §B.2 · p. 18

B.3 HUMAN EVALUATION

  • The questionnaire study used 8 input videos across 4 categories, compared FastVMT with baselines, and asked participants to rank 8 videos by preference.Only two generated videos are presented because of page constraints.Source: §B.3 · p. 18, Figure 1 · p. 19, Figure 2 · p. 19

B.4 CORRESPONDING-WINDOW LOSS

  • The tracking loss penalizes first-order temporal variations in window-averaged key features across adjacent target frames, averaging over tiles and anchor frames.The method uses non-overlapping spatial tiles, temporal neighborhoods, and fixed-size windows on target frames.Source: §B.4 · p. 19

C MORE COMPARSONS

  • FastVMT resolves the inconsistent motion shown by previous works in additional comparisons.The comparison is presented in Fig. 5.Source: C MORE COMPARSONS · p. 20

D MORE RESULTS

  • Figures 3 and 4 pair each reference video with two distinct motion-transferred videos.Source: §D.1 · p. 20

D.1 MORE VISUAL RESULTS

  • Figure 7 shows an astronaut front-flipping into water and a complex camera move that rises from ground level before pushing in for a close-up.Source: §D.1 · p. 20

D.2 MORE ABLATION RESULTS · D.3 MORE QUANTITATIVE COMPARISON

The supplementary ablation finds that extracting motion from the middle attention layer performs best in motion transfer. Additional evaluations report Vbench ablation metrics and a quantitative experiment using MTBench, but the supplied passages provide no metric values.

D.2 MORE ABLATION RESULTS

  • The middle attention layer achieves the best motion-transfer performance among the attention-layer choices tested for motion extraction.The ablation results are presented in Table 2.Source: §D.2 · p. 20
  • The ablation samples were evaluated using Vbench metrics, with results presented in Table 4.Source: §D.2 · p. 20

D.3 MORE QUANTITATIVE COMPARISON

  • A further quantitative experiment used MTBench, but the supplied passage gives no results beyond stating that they are shown in Table ??.Source: §D.3 · p. 20

E EXPERIMENT DETAILS · F LIMITATIONS AND POTENTIAL SOCIAL IMPACT

The experiments used NVIDIA A100-80GB GPUs and publicly sourced reference videos; the paper also reports ablation-sample Vbench metrics and qualitative comparisons across motion types.

E EXPERIMENT DETAILS

  • Experiments used NVIDIA A100-80GB GPUs for fair comparison.Source: E EXPERIMENT DETAILS · p. 21
  • Reference videos were curated from publicly available internet sources to provide a diverse and representative evaluation dataset.Source: E EXPERIMENT DETAILS · p. 21
  • Table 4 evaluates Vbench metrics on ablation samples.The caption says red and blue denote the best and second-best results, respectively; it provides no metric values in the supplied passage.Source: Table 4 · p. 22
  • Figure 5 reports that FastVMT demonstrates superior performance across a range of motion types in qualitative comparisons with baselines.The caption describes comparisons using various kinds of motions.Source: Figure 5 · p. 22

F.1 LIMITATION … G THE USAGE OF LARGE LANGUAGE MODELS

FastVMT faces limits in handling out-of-distribution or semantically conflicting prompts and may incur memory costs when scaled to long videos. The paper proposes an agent-based future direction, describes potential uses across industries, and discloses its use of LLMs for editing.

F.1 LIMITATION

  • The pre-trained video backbone limits handling of out-of-distribution prompts and motions, while semantic conflicts can destabilize transfer; occlusion is also a reported failure case.Highly complex actions such as Thomas Flair are especially difficult; the paper suggests a more powerful video diffusion model could mitigate occlusion.Source: §F.1 · p. 22, Figure 6 · p. 22
  • AMF’s pairwise design increases memory consumption, which may complicate scaling to long-video generation.The paper suggests memory management, model compression, or hierarchical generation as possible engineering mitigations.Source: §F.1 · p. 23

F.2 FUTURE WORK

F.3 POTENTIAL SOCIAL IMPACT

  • FastVMT could speed film, gaming, and digital-content production, potentially reducing costs and widening access to high-quality video creation.The paper also highlights more dynamic storytelling and greater accessibility for smaller studios and independent creators.Source: §F.3 · pp. 23–24
  • In advertising and interactive applications, FastVMT could enable personalized campaigns and real-time video adaptation.The paper says brands could adapt campaigns to different demographics to improve relevance and audience resonance.Source: §F.3 · p. 23
  • Potential applications beyond media include education, remote work, and healthcare, where motion transfer could support adaptable simulations, remote learning, telemedicine, and virtual conferences.The paper suggests these scenarios could make interactions more engaging and effective.Source: §F.3 · p. 23

G THE USAGE OF LARGE LANGUAGE MODELS

  • The authors report using LLMs for grammar and chart or graph format checking, as well as language polishing.They state that all authors are responsible for LLM-generated content.Source: G THE USAGE OF LARGE LANGUAGE MODELS · p. 24

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