FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios

TL;DR

FlexiAct introduces a novel approach for flexible action transfer in videos, allowing for variations in layout, viewpoint, and skeletal structure while maintaining identity, through innovative modules RefAdapter and FAE.

Contribution

The paper presents FlexiAct, a new method that enables adaptable action transfer across diverse subjects and scenarios, surpassing existing methods in flexibility and consistency.

Findings

Effective action transfer across diverse layouts and viewpoints.

RefAdapter outperforms existing spatial adaptation methods.

FAE enables direct action extraction during denoising.

Abstract

Action customization involves generating videos where the subject performs actions dictated by input control signals. Current methods use pose-guided or global motion customization but are limited by strict constraints on spatial structure, such as layout, skeleton, and viewpoint consistency, reducing adaptability across diverse subjects and scenarios. To overcome these limitations, we propose FlexiAct, which transfers actions from a reference video to an arbitrary target image. Unlike existing methods, FlexiAct allows for variations in layout, viewpoint, and skeletal structure between the subject of the reference video and the target image, while maintaining identity consistency. Achieving this requires precise action control, spatial structure adaptation, and consistency preservation. To this end, we introduce RefAdapter, a lightweight image-conditioned adapter that excels in spatial adaptation and consistency preservation, surpassing existing methods in balancing appearance consistency and structural flexibility. Additionally, based on our observations, the denoising process exhibits varying levels of attention to motion (low frequency) and appearance details (high frequency) at different timesteps. So we propose FAE (Frequency-aware Action Extraction), which, unlike existing methods that rely on separate spatial-temporal architectures, directly achieves action extraction during the denoising process. Experiments demonstrate that our method effectively transfers actions to subjects with diverse layouts, skeletons, and viewpoints. We release our code and model weights to support further research at https://shiyi-zh0408.github.io/projectpages/FlexiAct/