Our new X account is live! Follow @wizwand_team for updates
WorkDL logo mark

Wan-Animate: Unified Character Animation and Replacement with Holistic Replication

About

We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code.

Gang Cheng, Xin Gao, Li Hu, Siqi Hu, Mingyang Huang, Chaonan Ji, Ju Li, Dechao Meng, Jinwei Qi, Penchong Qiao, Zhen Shen, Yafei Song, Ke Sun, Linrui Tian, Feng Wang, Guangyuan Wang, Qi Wang, Zhongjian Wang, Jiayu Xiao, Sheng Xu, Bang Zhang, Peng Zhang, Xindi Zhang, Zhe Zhang, Jingren Zhou, Lian Zhuo• 2025

Related benchmarks

TaskDatasetResultRank
Video GenerationTiktok (test)
SSIM0.92
11
Video GenerationTikTok Cross-ID
MQ4.35
7
Video GenerationTikTok dataset Self Reenactment (test)
PSNR21.12
7
Portrait AnimationSelf-Reenactment (test)
PSNR27.97
6
Portrait AnimationCross-Reenactment (test)
CSIM0.551
6
AnimationAW Bench
Auto Imaging Quality4.05
6
Human Motion GenerationLLM-generated Prompts (50 prompts)
Aesthetic Quality61.4
5
Character ReplacementSynthesized benchmark
SSIM0.692
4
Video Character ReplacementVBench real-world
Subject Consistency91.25
4
Motion TransferMotion Transfer User Study
Motion Fidelity3.71
3
Showing 10 of 10 rows

Other info

Follow for update