Summary: SCAIL tackles significant challenges in character animation, specifically limited generalization across diverse characters and motion incoherence in complex scenarios like multi-character interactions or intricate movements (e.g., flipping, turning). It presents a framework enabling high-fidelity, studio-grade character animation by leveraging in-context learning of 3D-consistent pose representations. This approach benefits researchers and developers aiming to achieve production-level animation quality, enhancing generalization and ensuring natural, coherent movements for stylized characters and dynamic interactions.

How It Works: The core innovation lies in reimagining pose representation and injection mechanisms. SCAIL introduces novel 3D-consistent pose representations designed to simultaneously prevent identity leakage and retain rich motion dynamics. This design compels the model to perform robust spatiotemporal reasoning across entire motion sequences, leading to more natural, coherent, and production-ready animations. This contrasts with prior methods that struggled to balance identity preservation with motion expressiveness.

Quick Start & Requirements:

• Installation: Begin by cloning the repository using GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/zai-org/SCAIL-Preview. Subsequently, install dependencies via pip install -r requirements.txt. Initialize necessary submodules with git submodule update --init --recursive and consult POSE_INSTRUCTION.md for detailed pose extraction procedures.
• Prerequisites: Requires Python versions between 3.10 and 3.12. Users must download the SCAIL-Preview (14B) model weights, available on Hugging Face or ModelScope. The setup integrates Wan VAE and T5 modules within the checkpoint, relies on the SAT framework, and uses NLFPose for pose extraction. Input data must adhere to a specified directory structure.
• Links: Model weights are accessible at Hugging Face and ModelScope. Pose extraction instructions are detailed in POSE_INSTRUCTION.md. The primary inference script is scripts/sample_sgl_1Bsc_xc_cli.sh, with sampling configurations found in configs/sampling/.

Highlighted Details:

• Demonstrates remarkable capability in handling multi-character interactions and diverse character styles, including complex anime characters, even with minimal anime training data.
• Achieves strong generalization to stylized characters and complex poses beyond its core training distribution.
• Community-driven adaptations are notable, including GGUF versions for broader accessibility and integrations with ComfyUI (via WanVideoWrapper and SCAIL-Pose modules).
• Supports advanced prompt engineering, with snippets provided for generating detailed prompts using models like Google Gemini to guide animation generation.

Maintenance & Community: The project actively integrates community contributions, as seen with GGUF and ComfyUI adaptations. Recent updates include merging SCAIL-Pose as a submodule and releasing preview versions. Future development, "SCAIL-Official," is planned to enhance stability and introduce innate long video generation capabilities. No direct community channels (Discord/Slack) or social media links are provided in the README.

Licensing & Compatibility: The project is distributed under the permissive Apache License 2.0, facilitating commercial use and integration within closed-source applications without significant restrictions.

Limitations & Caveats: The current offering is a "Preview" release, indicating potential for future stability improvements and feature enhancements. Official models promising greater stability and innate long video generation are still under development. Certain community adaptations, such as the ComfyUI-SCAIL-Pose module, may have incomplete features (e.g., lacking multi-character tracking). Achieving optimal animation quality is highly dependent on utilizing long, descriptive prompts.