MotionCFG: Boosting Motion Dynamics via Stochastic Concept Perturbation

TL;DR

MotionCFG introduces a noise-contrastive approach to enhance motion dynamics in text-to-video synthesis, avoiding semantic bias from explicit negative prompts and improving temporal detail refinement.

Contribution

The paper presents MotionCFG, a novel noise-perturbed contrastive guidance method that improves motion quality without semantic distortion in T2V models.

Findings

Enhanced motion dynamics across state-of-the-art frameworks

Effective steering of complex concepts like object numerosity

Minimal additional computational overhead

Abstract

Despite recent advances in Text-to-Video (T2V) synthesis, generating high-fidelity and dynamic motion remains a significant challenge. Existing methods primarily rely on Classifier-Free Guidance (CFG), often with explicit negative prompts (e.g. "static", "blurry"), to suppress undesired artifacts. However, such explicit negations frequently introduce unintended semantic bias and distort object integrity; a phenomenon we define as Content-Motion Drift. To address this, we propose MotionCFG, a framework that enhances motion dynamics by contrasting a target concept with its noise-perturbed counterparts. Specifically, by injecting Gaussian noise into the concept embeddings, MotionCFG creates localized negative anchors that encapsulate a broad complementary space of sub-optimal motion variations. Unlike explicit negations, this approach facilitates implicit hard negative mining without shifting the global semantic identity, allowing for a focused refinement of temporal details. Combined with a piecewise guidance schedule that confines intervention to the early denoising steps, MotionCFG consistently improves motion dynamics across state-of-the-art T2V frameworks with negligible computational overhead and minimal compromise in visual quality. Additionally, we demonstrate that this noise-induced contrastive mechanism is effective not only for sharpening motion trajectories but also for steering complex, non-linear concepts such as precise object numerosity, which are typically difficult to modulate via standard text-based guidance.