Wan releases Wan Animate 2 for character image animation

Character image animation remains a fundamental yet challenging task in computer vision. Existing approaches can be broadly categorized into three paradigms: methods based on explicit motion representations suffer from extraction errors and identity drift; methods based on implicit motion features lose fine-grained dynamics through compression; and in-context learning approaches avoid intermediate representations but incur prohibitive computational costs. Furthermore, all current systems are designed for offline synthesis, unable to meet the real-time requirements of interactive applications such as digital avatars and live-streaming hosts.

To address these limitations, Tongyi Lab from Alibaba Group has presented Wan-Animate-2, an end-to-end character animation framework that directly consumes the driving video within a redesigned Diffusion Transformer. The architecture achieves superior motion fidelity and identity preservation by eliminating intermediate motion extractors entirely. The developers further introduce text-driven viewpoint control that decouples the output camera perspective from the driving video—a capability rarely supported by prior character animation methods that rely on explicit motion representations.