Precise Motion Inbetweening via Bidirectional Autoregressive Diffusion Models

Conditional motion diffusion models have demonstrated significant potential in generating natural and reasonable motions response to constraints such as keyframes, that can be used for motion inbetweening task. However, most methods struggle to match the keyframe constraints accurately, which resulting in unsmooth transitions between keyframes and generated motion. In this article, we propose Bidirectional Autoregressive Motion Diffusion Inbetweening (BAMDI) to generate seamless motion between start and target frames. The main idea is to transfer the motion diffusion model to autoregressive paradigm, which predicts subsequence of motion adjacent to both start and target keyframes to infill the missing frames through several iterations. This can help to improve the local consistency of generated motion. Additionally, bidirectional generation make sure the smoothness on both start frame target keyframes. Experiments show our method achieves state-of-the-art performance compared with other diffusion-based motion inbetweening methods.