Motion Editing for Quadruped Characters via Latent Frequency Embedding

The accurate and diversified generation of motion sequences for virtual characters poses both an enticing and challenging task within the domain of 3D animation and game content production. To achieve a natural and realistic full-body motion, the movements of virtual characters must adhere to a set of constraints, promoting reliable and seamless pose-changing. This study presents a two-stage model specifically designed to learn Inverse Kinematics (IK) constraints from the representative quadruped character poses. In the first stage, we employ frequency analysis to decompose motion poses into the base-level and style-level components. The base-level content encapsulates the global correlations in the dataset, while the style-level variation centers on distinguishing the local attributes in similar data elements. In order to construct data correlations among poses, we embed the decomposed pose feature into a latent space in the second stage. The kernel matrix of the embedding, which is refined from the original joint angles to the decomposed representation and the IK constraints, creates a more compact distribution of the pose similarity and also guarantees a plausible sampling result with certain IK constraints. Moreover, new motions from the edited IK constraints can also be generated by proposing a searching strategy to adapt to our latent embedding. Experimental results reveal that our method is competitive with the state-of-the-art synthetic approaches in terms of accuracy, highlighting our considerable potential for high efficiency in the animation production.