  We present a novel task, i.e., animating a target 3D object through the
motion of a raw driving sequence. In previous works, extra auxiliary
correlations between source and target meshes or intermedia factors are
inevitable to capture the motions in the driving sequences. Instead, we
introduce AniFormer, a novel Transformer-based architecture, that generates
animated 3D sequences by directly taking the raw driving sequences and
arbitrary same-type target meshes as inputs. Specifically, we customize the
Transformer architecture for 3D animation that generates mesh sequences by
integrating styles from target meshes and motions from the driving meshes.
Besides, instead of the conventional single regression head in the vanilla
Transformer, AniFormer generates multiple frames as outputs to preserve the
sequential consistency of the generated meshes. To achieve this, we carefully
design a pair of regression constraints, i.e., motion and appearance
constraints, that can provide strong regularization on the generated mesh
sequences. Our AniFormer achieves high-fidelity, realistic, temporally coherent
animated results and outperforms compared start-of-the-art methods on
benchmarks of diverse categories. Code is available:
https://github.com/mikecheninoulu/AniFormer.
