SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation

TL;DR

SynMotion is a novel video generation model that jointly leverages semantic guidance and visual adaptation to produce motion-customized videos with high fidelity and diversity.

Contribution

It introduces dual-embedding semantic comprehension, motion adapters, and a new training strategy supported by a curated benchmark, advancing motion customization in video synthesis.

Findings

Outperforms existing baselines in T2V and I2V tasks.

Enhances motion fidelity and temporal coherence.

Promotes motion specificity while maintaining subject diversity.

Abstract

Diffusion-based video motion customization facilitates the acquisition of human motion representations from a few video samples, while achieving arbitrary subjects transfer through precise textual conditioning. Existing approaches often rely on semantic-level alignment, expecting the model to learn new motion concepts and combine them with other entities (e.g., ''cats'' or ''dogs'') to produce visually appealing results. However, video data involve complex spatio-temporal patterns, and focusing solely on semantics cause the model to overlook the visual complexity of motion. Conversely, tuning only the visual representation leads to semantic confusion in representing the intended action. To address these limitations, we propose SynMotion, a new motion-customized video generation model that jointly leverages semantic guidance and visual adaptation. At the semantic level, we introduce the dual-embedding semantic comprehension mechanism which disentangles subject and motion representations, allowing the model to learn customized motion features while preserving its generative capabilities for diverse subjects. At the visual level, we integrate parameter-efficient motion adapters into a pre-trained video generation model to enhance motion fidelity and temporal coherence. Furthermore, we introduce a new embedding-specific training strategy which \textbf{alternately optimizes} subject and motion embeddings, supported by the manually constructed Subject Prior Video (SPV) training dataset. This strategy promotes motion specificity while preserving generalization across diverse subjects. Lastly, we introduce MotionBench, a newly curated benchmark with diverse motion patterns. Experimental results across both T2V and I2V settings demonstrate that \method outperforms existing baselines. Project page: https://lucaria-academy.github.io/SynMotion/