Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
On the map: Diffusion models → Video generation
Abstract (arXiv)
To achieve real-time interactive video generation, current methods distill pretrained bidirectional video diffusion models into few-step autoregressive (AR) models, facing an architectural gap when full attention is replaced by causal attention. However, existing approaches do not bridge this gap theoretically. They initialize the AR student via ODE distillation, which requires frame-level injectivity, where each noisy frame must map to a unique clean frame under the PF-ODE of an AR teacher. Distilling an AR student from a bidirectional teacher violates this condition, preventing recovery of the teacher's flow map and instead inducing a conditional-expectation solution, which degrades performance. To address this issue, we propose Causal Forcing, which uses an autoregressive teacher for ODE initialization to bridge the architectural gap, and then applies the same DMD procedure as in Self Forcing. Empirical results show that our method outperforms all baselines across all metrics, surpassing the SOTA Self Forcing by 19.3% in Dynamic Degree, 8.7% in VisionReward, and 16.7% in Instruction Following. Project page: hrefhttps://thu-ml.github.io/CausalForcing.github.io/https://thu-ml.github.io/CausalForcing.github.io/; the code: hrefhttps://github.com/thu-ml/Causal-Forcinghttps://github.com/thu-ml/Causal-Forcing.
Key facts. Cited by 159 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 76 of its 83 references that are in the map. First posted on arXiv 2 Feb 2026.
Builds on
76 of 83 references.
Closest ancestors · 29
Same direction, newest first
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Other references · 47
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- 2025LongLive: Real-time Interactive Long Video Generationcited by 237
- 2023Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Modelscited by 232
- 2019Scaling Autoregressive Video Modelscited by 189
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- 2025RELIC: Interactive Video World Model with Long-Horizon Memorycited by 89
- 2025Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generationcited by 84
- 2025Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistencycited by 81
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Built on by
Citing papers per year: 2026: 159.
159 papers.
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By area:
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