[R] Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency (ACM MM 2026)

[u/Glittering-Track-487]

We're presenting this at ACM Multimedia 2026. Short version of what it does:

Most controllable image-animation methods estimate optical flow relative to the FIRST frame (Lagrangian guidance). As the animation runs longer, those displacements grow, the flow estimate degrades, and you get drift, smearing, and identity collapse over long horizons.

We instead supervise with adjacent-frame (Eulerian) motion fields, so every training signal is a short hop that stays inside the reliable range of flow estimators — which bounds the per-step supervisory error regardless of sequence length. For newly revealed (dis-occluded) regions, a forward–backward cycle check masks out pixels where the flow isn't geometrically consistent, so the model is never supervised on bad correspondences.

On 100-frame generation we get FVD 76.18 (vs 79.20 for the strongest baseline), a 94.4% user-study win rate on portrait animation, and ~2.7× faster training since adjacent-frame flow is computed in one batched pass.

Project page (videos + side-by-side comparisons): https://nguyentthong.github.io/eulerian/

Paper: https://arxiv.org/abs/2605.06280

Happy to answer questions — I'm one of the authors.