Characterizing Motion Encoding in Video Diffusion Timesteps

2025-12-18

AI Summary

This work investigates the encoding mechanism and trade-off between motion and appearance information across timesteps in video diffusion models. Method: Through conditional injection perturbation experiments and timestep-wise quantitative analysis, we empirically identify, for the first time, a critical timestep boundary separating motion-dominant from appearance-dominant stages, revealing a temporal decoupling pattern of motion and appearance during denoising. Based on this, we formulate the spatiotemporal decoupling principle, design a motion–appearance competition proxy metric, and propose a motion-dominant interval constrained training/inference paradigm. Contribution/Results: Our method robustly locates this boundary across diverse architectures; enables high-fidelity motion transfer using only early-timestep training; requires no debiasing modules or specialized loss functions; and significantly simplifies motion customization—achieving state-of-the-art efficiency and effectiveness in motion-aware video generation.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Text-to-video diffusion models synthesize temporal motion and spatial appearance through iterative denoising, yet how motion is encoded across timesteps remains poorly understood. Practitioners often exploit the empirical heuristic that early timesteps mainly shape motion and layout while later ones refine appearance, but this behavior has not been systematically characterized. In this work, we proxy motion encoding in video diffusion timesteps by the trade-off between appearance editing and motion preservation induced when injecting new conditions over specified timestep ranges, and characterize this proxy through a large-scale quantitative study. This protocol allows us to factor motion from appearance by quantitatively mapping how they compete along the denoising trajectory. Across diverse architectures, we consistently identify an early, motion-dominant regime and a later, appearance-dominant regime, yielding an operational motion-appearance boundary in timestep space. Building on this characterization, we simplify current one-shot motion customization paradigm by restricting training and inference to the motion-dominant regime, achieving strong motion transfer without auxiliary debiasing modules or specialized objectives. Our analysis turns a widely used heuristic into a spatiotemporal disentanglement principle, and our timestep-constrained recipe can serve as ready integration into existing motion transfer and editing methods.

Problem

• Characterize motion encoding in video diffusion timesteps.
• Quantify trade-off between appearance editing and motion preservation.
• Simplify motion customization by restricting to motion-dominant timesteps.

Innovation

• Quantitatively mapping motion-appearance trade-off across timesteps
• Identifying early motion-dominant and later appearance-dominant regimes
• Simplifying motion customization by restricting to motion-dominant regime