Reconstruction-Anchored Diffusion Model for Text-to-Motion Generation

Diffusion models have seen widespread adoption for text-driven human motion generation and related tasks due to their impres-sive generative capabilities and flexibility. However, current motion dif-fusion models face two major limitations: a representational gap causedby pre-trained text encoders that lack motion-specific information, anderror propagation during the iterative denoising process. This paper in-troduces Reconstruction-Anchored Diffusion Model (RAM) to addressthese challenges. First, RAM leverages a motion latent space as interme-diate supervision for text-to-motion generation. To this end, RAM co-trains a motion reconstruction branch with two key objective functions:self-regularization to enhance the discrimination of the motion space andmotion-centric latent alignment to enable accurate mapping from textto the motion latent space. Second, we propose Reconstructive ErrorGuidance (REG), a testing-stage guidance mechanism that exploits themotion diffusion model’s inherent self-correction ability to mitigate errorpropagation. At each denoising step, REG uses the motion reconstruc-tion branch to reconstruct the previous estimate, reproducing the priorerror patterns. By amplifying the residual between the current predic-tion and the reconstructed estimate, REG highlights the improvementsin the current prediction. Extensive experiments demonstrate that RAMachieves significant improvements and state-of-the-art performance. Ourcode will be released on https://feifeifeiliu.github.io/RAM .