SCAIL-2

SCAIL-2 implements an end-to-end character animation model that takes a driving source — video, pose signal, or mesh — and produces controlled character animation through in-context conditioning. The core workflow runs through `generate.py`, with a `prompt_enhancer.py` utility and a `convert.py` preprocessing step. Configuration lives in the `configs` directory, and the repo includes example inputs under `examples`. The SCAIL-Pose submodule handles pose representation as a separate tracked dependency.

The differentiating design decision is treating pose representation and pose injection as a single unified conditioning problem rather than chaining two models. The SCAIL-1 line identified that splitting these two steps was the bottleneck blocking production-quality results — specifically, pose drift and identity bleed during cross-body transfer. SCAIL-2’s in-context conditioning addresses this by conditioning the full generation on pose context end-to-end, which the project claims supports zero-shot mesh rendering control without per-scene fine-tuning.

The model fits researchers running motion transfer experiments and developers assembling offline animation pipelines where they supply their own compute. It does not fit teams that need a REST endpoint, a queue-backed inference service, or any managed deployment surface — none of those exist in this repo. The Apache-2.0 license means teams can modify and distribute, but the inference-only scope means training custom variants requires work outside what the repository provides.

On the technical side, the repo is structured around a WAN-family model checkpoint (the `wan` directory), with submodule pinning for SCAIL-Pose at a specific commit. Teams integrating this into a pipeline need to resolve submodule dependencies on setup, manage checkpoint storage themselves, and write their own batching and serving logic around the provided scripts.