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Attn-Forcing: Learning Full-Context Attention Proxies for Streaming Video Generation

Paper recorded by Signals 4 on 2026-10-21 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.CV · 计算机视觉 · first seen 2026-10-22

Abstract

Increasing the attention context length significantly improves streaming video generation quality, while incurring prohibitive memory and computational costs as the context grows. Existing methods attempt to capture this contextual information through attention selection or compression, but remain limited by bounded representations and lack explicit attention-level supervision to retain historical

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#193 most recent of 756 cs.CV papers we have recorded · ↑ newer: ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric Vi · ↓ older: Plausible but Unimplantable: A Trustworthiness Audit of Shape Completi
Cite this page: Attn-Forcing: Learning Full-Context Attention Proxies for Streaming Video Generation: the #193 most recent of 756 cs.CV papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/attn-forcing-learning-full-context-attention-proxies-for-streaming-video-generat.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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