Wan-Animate: Unified Video Animation

• Wan-Animate is a unified diffusion-based video generation framework that animates static character images and replaces characters for seamless scene integration.
• It leverages explicit spatial skeleton signals and implicit facial embeddings to achieve precise motion control and natural expressiveness.
• It incorporates a Relighting LoRA module to harmonize lighting and color tones, setting a new standard in video synthesis quality.

Wan-Animate is a unified diffusion-based video generation framework developed for high-fidelity character animation and seamless character replacement in video. The system is designed to take as input a static character image and a reference video, and either (1) animate the character by faithfully replicating body movements and facial expressions from the video, or (2) replace the original character within the reference video with the new animated character, adapting lighting and color tone for environmental consistency. Wan-Animate unifies these tasks under a single symbolic representation and leverages both explicit and implicit condition controls to achieve state-of-the-art performance in controllability, appearance preservation, and scene integration (Cheng et al., 17 Sep 2025).

1. Unified Framework for Animation and Replacement

Wan-Animate integrates character animation and character video replacement in a single architectural design. In animation mode, it generates temporally coherent character videos by replicating complex movements and expressions from a reference video using spatially aligned skeleton signals (for body motion) and implicit facial features (for expressions). In replacement mode, beyond animating the character, the system integrates the result directly into the original video, harmonizing lighting, illumination, and color tone for seamless visual incorporation. This dual capability removes the classical separation between animation (content creation) and compositional replacement (video editing), enabling a holistic video synthesis workflow.

Technically, Wan-Animate operates on a diffusion-based transformer backbone. The network ingests both the static reference character and the driving sequence, with explicit control signals as follows:

• Body motion is controlled by spatially aligned skeletons which are projected into the noise latent space for precise frame-wise motion transfer.
• Facial expressiveness is captured via implicit facial patches (or latent embeddings) extracted from the reference video, temporally encoded, and then injected through a dedicated cross-attention module.

This approach ensures both the fine preservation of character identity and the accurate transfer of motion and facial expression, enabling high-level video synthesis tasks to be performed within one unified paradigm.

2. Modified Input Paradigm and Symbolic Representation

Wan-Animate introduces a modified input paradigm to unify multiple video generation tasks under a shared symbolic latent representation. Rather than using separate conditioning strategies for animation and replacement, a single paradigm is adopted:

• The reference image is encoded as a latent representation via a pretrained encoder (e.g., Wan-VAE), carrying appearance cues.
• Temporal conditioning is established by constructing a sequence of reference frames interleaved with zero-filled placeholders. A binary mask signals which components are given (mask=1) and which must be synthesized (mask=0).
• For replacement tasks, environment latents are constructed by zeroing character-corresponding regions to maintain background fidelity; for animation tasks, standard reference and zero masking suffices.

All conditioning signals—pose, facial expression, background masking—are concatenated or added into the transformer input space. The network's architecture, therefore, remains task-agnostic, and the same pipeline can serve multiple subtasks in animation and compositional video editing. This design reduces architectural complexity and improves memory and compute efficiency for multi-task deployment.

3. Motion and Expression Control: Skeleton and Implicit Face Features

Fine-grained control in Wan-Animate is provided by two primary mechanisms:

• Spatially Aligned Skeleton Signal: Key skeletal joints are geographically parameterized and projected into the noise latent of the transformer, serving as explicit pose anchors in every generated frame. This yields temporally coherent, precise motion replication from the video driver.
• Implicit Facial Feature Extraction: Instead of utilizing only classical facial landmarks, Wan-Animate encodes entire facial patches (from the driving video) as latents, fusing them via cross-attention. This implicit signal encompasses not just rigid landmark positions but subtle microexpressions and appearance contexts that classical keypoints often miss.

The combined pathway ensures that both gross body pose and nuanced facial expression are precisely reenacted, enabling the resulting videos to display highly expressive, accurate animations even in long sequences.

4. Environmental Integration and Relighting LoRA

A distinctive feature of Wan-Animate is its environmental adaptation in replacement tasks—wherein the synthesized character must visually blend with the lighting, color tone, and scene context of the reference video. This is achieved by an auxiliary Relighting LoRA module:

• Relighting LoRA: During pretraining, paired images with the character composited on different backgrounds (produced using illumination harmonization tools such as IC-Light) are used to train a low-rank adaptation (LoRA) on select transformer attention layers. This module adjusts the output activations so that the color and illumination of the inserted character fit the target environment.
• Mathematical Form: The modification applies by updating the attention weights: $W = W_0 + A \cdot B$, where $W_0$ is the pretrained matrix and $A,B$ are learned low-rank matrices.
• Effect: The Relighting LoRA preserves intrinsic appearance but modifies only environment-adaptive features, ensuring seamless compositional blending.

A plausible implication is that advances in environment-aware adaptation and LoRA tuning may further generalize this approach to more complex scene integration scenarios, such as multicharacter interactions or extreme lighting shifts.

5. Performance and Empirical Results

Wan-Animate demonstrates state-of-the-art performance on metrics such as SSIM, LPIPS, and FVD compared to previous open-source and some commercial models (Cheng et al., 17 Sep 2025). Experimental evaluations include:

• Quantitative improvements in structural similarity and perceptual quality in both animation and replacement tasks.
• Qualitative user studies where participants strongly preferred Wan-Animate outputs for identity preservation, motion accuracy, and naturalness of compositional integration.
• Ablation studies substantiating the necessity of staged facial-body module training and the Relighting LoRA in attaining optimal results.

These results underscore the framework's ability to achieve realistic, temporally consistent, and highly expressive animated video synthesis from a static character image and a driving video sequence.

6. Open Source and Research Implications

The authors of Wan-Animate are committed to open-sourcing all model weights and the corresponding training and inference pipeline. This open release supports:

• Reproducibility and validation of empirical claims by the research community.
• Accessibility for applied research in filmmaking, digital avatar creation, interactive entertainment, and broader academic paper.
• Accelerated innovation through community-driven improvements and extensions of the unified paradigm.

A plausible implication is that the availability of high-quality, open-source, multi-task character animation models may broaden adoption in both academic and industrial contexts, facilitating new research directions in universal animation, controllable video synthesis, and environment-adaptive compositional models.

7. Conclusion and Broader Context

Wan-Animate represents a comprehensive step forward in high-fidelity character animation and replacement. By leveraging a unified symbolic input design, explicit and implicit control signals, and robust environmental adaptation through Relighting LoRA, it sets a new baseline for controllable, expressive, and scene-aware video character animation. Its modular approach suggests extensibility to broader generative tasks, including multi-agent choreography, dynamic scene synthesis, and complex video editing, especially as future work extends the symbolic representation and control signal modalities. The commitment to open-sourcing ensures that Wan-Animate will serve as both a practical tool and a foundation for further research and development in advanced generative video modeling (Cheng et al., 17 Sep 2025).