Title: Time-to-Move: Training-Free Motion-Controlled Video Generation via Dual-Clock Denoising
Primary area: generative models
Keywords: Computer vision, Generative models

Abstract: Diffusion-based video generation can create realistic videos, yet existing image and text-based conditioning fails to offer precise motion control. Prior methods for motion control typically rely on displacement-based conditioning and require model-specific fine-tuning, which is computationally expensive and restrictive. We introduce Time-to-Move (TTM), a training-free, plug-and-play framework for motion- and appearance-controlled video generation with image-to-video (I2V) diffusion models. Our key insight is to use crude reference animations, obtained through user-friendly manipulations such as cut-and-drag or depth-based reprojection, as direct motion guidance, analogous to using coarse layout input in image editing. To integrate these signals, we adapt SDEdit to the video domain while anchoring the  appearance with image conditioning. We further propose dual-clock denoising, a region-dependent strategy that enforces strong alignment in motion-specified regions and grants flexibility elsewhere, balancing fidelity to user intent with natural dynamics. This lightweight modification of the sampling process incurs no additional training or runtime cost and is compatible with any backbone. Extensive experiments on object and camera motion benchmarks show that TTM matches or exceeds existing training-based baselines in realism and motion control. Beyond this, TTM introduces a unique capability: precise appearance control through pixel-level conditioning, exceeding the limits of text-only prompting.

TL;DR: We introduce Time-to-Move (TTM), a training-free, plug-and-play framework for motion- and appearance-controlled video generation with image-to-video (I2V) diffusion models.