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Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout

Paper recorded by Signals 4 on 2026-09-08 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-08 on arXiv · recorded by Signals 4 on 2026-09-09

Category: cs.CV · 计算机视觉 · first seen 2026-09-09

Abstract

Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretrained bidirectional video diffusion models into causal AR students through Distribution Matching Distillation (DMD), but the generated videos often suffer from over-saturation and over-smoothing issues, resulting in limited visual quality and realism. The key contributin

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#113 most recent of 237 cs.CV papers we have recorded · ↑ newer: Point4D: Long-range 4D Motion Reconstruction · ↓ older: Rethinking Learned Occupancy in Autonomous Active Mapping with Observa
Cite this page: Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout: the #113 most recent of 237 cs.CV papers we have recorded (as of 2026-09-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mask-forcing-improving-autoregressive-video-diffusion-distillation-via-dual-nois.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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