AI

LongTake: Learning to Sustain Dynamics in Long-Horizon Video Generation

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 00:18:24 UTC.

Briefings

LongTake addresses the failure of autoregressive video diffusion in long-horizon generation by introducing Long-Horizon Teacher Forcing (TF). This two-stage pipeline trains the model to predict later frames conditioned on long ground-truth video prefixes, enabling sustained dynamics and visual quality. The central finding is that LongTake's initialization yields higher dynamic degree than short-horizon TF on 30-second rollouts, surpassing baselines in both measures. Hybrid DMD further extends supervision to later frames while maintaining bidirectional joint supervision over the initial window.

Key points

• LongTake uses a two-stage training pipeline with Long-Horizon Teacher Forcing (TF) to condition autoregressive video diffusion models on long ground-truth video prefixes.
• LongTake achieves higher dynamic degree than short-horizon TF initialization on 30-second rollouts, surpassing evaluated baselines in both dynamic degree and aesthetic quality.

Context

World models and long-horizon video generation face challenges where autoregressive diffusion becomes static or loses quality. LongTake addresses this by extending direct supervision beyond short training horizons using curated real long videos.

Limitations

Evidence is limited to the specific 30-second and 60-second self-rollout evaluations. Results are based on five-second DMD training setup and do not include long-horizon self-rollouts beyond 60 seconds. No causal claims or causal claims are supported by the evidence.

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