Illumination-aware character animation: a combined approach using animateanyone and IC-light

Abstract

This paper proposes a novel approach to generating high-fidelity animated videos under flexible illumination conditions by integrating two state-of-the-art models: An- imateAnyone for image-to-video synthesis and IC-Light for advanced relighting. We explore two primary pipelines: Animate-Then-Relight, where AnimateAnyone first generates an animated video subsequently relit by IC-Light frame by frame, and Relight-Then-Animate, in which the reference image is relit prior to the animation process. Experiments conducted on a subset of the TikTok Dataset demonstrate that both pipelines outperform the baseline Relight-A-Vid in terms of perceptual (FID) and semantic (CLIP) metrics, with the Relight-Then-Animate approach showing particular robustness in challenging side-light scenarios. These findings underscore the potential of combining specialized animation and relighting modules for more realistic, con- trollable video synthesis, paving the way for refined temporal modeling and adaptive lighting strategies in future work.