RepForcing: Representation-Space Initialization for Autoregressive Video Distillation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2610.20220v1 Announce Type: new Abstract: Autoregressive generation underlies real-time, causal video synthesis and is the backbone of video world models. Today's dominant approach distills a b...
Autoregressive generation underlies real-time, causal video synthesis and is the backbone of video world models. Today's dominant approach distills a bidirectional diffusion model into a causal few-st...
The paper introduces Conditional Residual Prediction (CRP), a technique that reduces a causal video diffusion model’s dependence on past frames by having the model first predict the current frame without conditioning on history and then adding a residual based on the past. This approach allows training a causal model from an image‑model initialization without requiring a bidirectional teacher or complex distillation, and it nearly closes a 6.14‑point quality gap to a bidirectional model. Using CRP, the authors train Optica, a 2‑billion‑parameter causal video model that generates 5‑second 480p videos and achieves an 82.78 score on VBench with only about 15 million training videos.
Few-step autoregressive (AR) video diffusion enables low-latency streaming generation, but existing post-training methods predominantly rely on Distribution Matching Distillation (DMD), requiring both...
arXiv:2609.40037v1 Announce Type: new Abstract: Few-step autoregressive video generation enables efficient streaming synthesis, but errors introduced in early temporal blocks are reused as context an...
ViRDM is a new post‑training method for few‑step causal video generation that eliminates the need for a large teacher model and an online critic. By applying representation distribution matching (RDM) with a precomputed target distribution, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM overcomes memory, optimization, and temporal dynamics challenges. The approach reduces GPU memory usage and training time, achieving state‑of‑the‑art VBench performance with only 20 generator updates and 16 A100 GPU‑hours.