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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
Portrait Animation (Self-reenactment)VFHQ (test)
FVD302.7
23
Portrait Animation (Cross-reenactment)FFHQ source + VFHQ driving (test)
CSIM0.5812
18
Self-reenactment portrait animationMEAD 59 (test)
CSIM0.827
18
Video GenerationTiktok (test)
SSIM0.92
11
Facial Expression EditingMetaHuman-based benchmark Replacement Mode (test)
PSNR22.8196
10
Facial Expression EditingMetaHuman-based Enhancement Mode (test)
PSNR22.6417
10
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
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