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FastVMT: Eliminating Redundancy in Video Motion Transfer

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Video Motion Transfer; Efficiency; Diffusion model;
Abstract:

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 3.43× speedup without degrading the visual fidelity or the temporal consistency of the generated videos.

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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 proposes FastVMT, which accelerates video motion transfer in diffusion transformers by eliminating motion redundancy through localized attention masking and gradient redundancy via step-skipping optimization. It resides in the 'Efficiency and Optimization in Motion Transfer' leaf, which contains only two papers in the entire 50-paper taxonomy. This sparse positioning indicates that computational efficiency in motion transfer remains an underexplored direction, with most prior work prioritizing motion quality or control mechanisms over runtime performance. The leaf's isolation from the larger Motion Transfer Mechanisms branch suggests the field has not yet converged on standard efficiency paradigms.

The taxonomy reveals that neighboring branches—Motion Transfer Mechanisms, Motion Customization, and Conditional Motion Generation—collectively house over 20 papers focused on motion extraction, fine-tuning, and trajectory-based control. These directions emphasize fidelity and expressiveness, often through multi-stage pipelines or attention-based encoders, which can incur substantial computational costs. FastVMT diverges by treating efficiency as a first-class design constraint rather than a secondary optimization. Its proximity to Foundational Video Diffusion Transformer Architectures (5 papers) suggests it builds on established DiT backbones but reframes their computational structure, whereas domain-specific branches like Portrait Animation (4 papers) or Multi-View Generation (2 papers) pursue orthogonal specialization.

Among 25 candidates examined, the step-skipping gradient optimization contribution shows overlap with 2 prior works out of 10 candidates reviewed, indicating that gradient reuse strategies have appeared in related diffusion contexts. The sliding-window motion extraction examined 5 candidates with no refutations, suggesting localized attention masking for motion may be less explored. The corresponding-window loss function reviewed 10 candidates without refutation, implying this training objective is relatively novel within the examined scope. These statistics reflect a limited semantic search rather than exhaustive coverage, so unexamined literature may contain additional overlaps, particularly in broader diffusion acceleration research.

Given the sparse taxonomy leaf and the modest search scale, FastVMT appears to address a genuine gap in efficiency-focused motion transfer, though the gradient optimization component has partial precedent. The analysis covers top-25 semantic matches and does not extend to general diffusion acceleration literature outside motion transfer, where gradient skipping techniques may be more established. The novelty assessment is thus conditional on the examined scope and the relatively underpopulated efficiency subcategory within this taxonomy.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
25
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: video motion transfer using diffusion transformers. The field has evolved into a rich ecosystem organized around several complementary directions. At the highest level, Motion Transfer Mechanisms and Optimization addresses the core algorithmic strategies for extracting and applying motion patterns, while Motion Customization and Control focuses on user-driven specification of motion characteristics. Conditional Motion Generation and Guidance explores how textual or structural cues can steer motion synthesis, and Domain-Specific Motion Applications targets specialized settings such as human performance or camera trajectories. Foundational Video Diffusion Transformer Architectures investigates the underlying model designs—ranging from early transformer-based diffusion frameworks like VDT[8] to more recent large-scale systems such as CogVideoX[2] and Tora[3]—that enable scalable video generation. Motion Modeling and Temporal Consistency examines techniques for maintaining coherent dynamics across frames, Video Editing and Appearance Manipulation deals with post-hoc modifications, and Human Motion Synthesis Beyond Video covers skeletal or pose-driven generation outside the pixel domain. Finally, Efficiency and Optimization in Motion Transfer seeks to reduce computational overhead while preserving quality. Within this landscape, a particularly active tension exists between achieving high-fidelity motion transfer and maintaining practical inference speeds. Many studies pursue sophisticated motion encoders or multi-stage pipelines—such as MotionDirector[29] and MotionEditor[24]—to capture fine-grained dynamics, yet these approaches can be computationally expensive. In contrast, FastVMT[0] sits squarely in the Efficiency and Optimization branch, emphasizing rapid motion transfer without sacrificing temporal coherence. Its design choices align closely with works like Principled Reframing in Motion[50], which also prioritizes streamlined processing, but FastVMT[0] distinguishes itself by integrating transformer-based diffusion backbones optimized for speed. Compared to heavier customization frameworks such as MotionAdapter[41] or domain-specific pipelines like Humandit[7], FastVMT[0] trades some degree of fine-tuned control for substantially faster generation, addressing a key bottleneck as video diffusion models scale to longer sequences and higher resolutions.

Claimed Contributions

Sliding-window motion extraction strategy

The authors propose a sliding-window approach for extracting motion embeddings that constrains attention computations to local neighborhoods rather than computing global token-by-token similarities. This addresses motion redundancy by exploiting the fact that frame-to-frame motion is small and locally smooth.

5 retrieved papers
Step-skipping gradient optimization

The authors introduce an optimization scheme that reuses gradients from previous diffusion steps instead of recomputing them at every iteration. This exploits the observation that gradient updates in consecutive optimization steps are highly similar, thereby reducing computational cost.

10 retrieved papers
Can Refute
Corresponding-window loss function

The authors design a loss function that enforces consistency of key representations within sliding windows across consecutive frames. This loss complements the sliding-window motion extraction to improve motion consistency in generated videos.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Sliding-window motion extraction strategy

The authors propose a sliding-window approach for extracting motion embeddings that constrains attention computations to local neighborhoods rather than computing global token-by-token similarities. This addresses motion redundancy by exploiting the fact that frame-to-frame motion is small and locally smooth.

Contribution

Step-skipping gradient optimization

The authors introduce an optimization scheme that reuses gradients from previous diffusion steps instead of recomputing them at every iteration. This exploits the observation that gradient updates in consecutive optimization steps are highly similar, thereby reducing computational cost.

Contribution

Corresponding-window loss function

The authors design a loss function that enforces consistency of key representations within sliding windows across consecutive frames. This loss complements the sliding-window motion extraction to improve motion consistency in generated videos.

Table of Contents