Can Pose Transfer Models Generate Realistic Human Motion?

Recent pose-transfer methods aim to generate temporally consistent and fully controllable videos of human action where the motion from a reference video is reenacted by a new identity. We evaluate three state-of-the-art pose-transfer methods-AnimateAnyone, MagicAnimate, and ExAvatar-by generating videos with actions and identities outside the training distribution and conducting a participant study about the quality of these videos. In a controlled environment of 20 distinct human actions, we find that participants, presented with the pose-transferred videos, correctly identify the desired action only $42.92 \%$ of the time. Moreover, the participants find the actions in the generated videos consistent with the reference (source) videos only $36.46 \%$ of the time. These results vary by method: participants find the splatting-based ExAvatar more consistent and photorealistic than the diffusion-based AnimateAnyone and MagicAnimate.