Triangular Resampling for Long-Horizon Motion Generation

We introduce Triangular Resampling (TR), a post-training method for mitigating long-horizon error accumulation in motion diffusion models. TR builds on FloodDiffusion, which generates motion using a triangular denoising schedule. During training, the current model observes short, ground-truth-derived motion windows, whereas long-horizon inference repeatedly conditions on its own predictions, allowing errors to accumulate. A common remedy is to expose the model to its own rollouts during training. However, replacing only completed motion history does not capture the evolving, partially denoised states within a triangular denoising window. To address this mismatch, TR introduces a key design that extends rollout-based training to the entire active window, including partially denoised states. This exposes training to model-induced errors, but unrestricted rollout can also move the training states away from their paired ground-truth motion. We therefore introduce a denoising threshold to retain ground-truth anchoring while controlling the transition to model-generated rollouts. For each replayed training sample, TR draws a shared threshold and replays the multi-step triangular denoising trajectory without gradient tracking. After each update, states below the threshold are replaced with noise-matched ground truth, while states at or above it retain model predictions. The resulting latent window is then used in the standard forward pass, loss computation, and gradient update. This rollout construction supports both the original supervised objective (TR) and distribution matching (TR-DMD). We evaluate 120-second motion generation on HumanML3D test prompts. TR and TR-DMD achieve state-of-the-art FID AUC within their respective non-DMD and DMD comparison groups. Specifically, supervised TR reduces FID AUC by 40.9% and FID degradation slope by 55.3% relative to matched post-training without replay.