The paper introduces Any Forcing, a framework that enables flexible sampling steps and stable one-step generation for autoregressive video models. It employs a unified adversarial discriminator to align real distribution, inter-step consistency, and first-frame quality, and a training-free adaptive memory selection that preserves temporal relationships. Experiments show high-quality, stable one-step generation at 20.9 FPS for short videos and 24.7 FPS for long videos on a single H100 GPU.
arXiv:2610.10429v1 Announce Type: new Abstract: Autoregressive video generation requires denoising the current frames while writing their key-value representations as context for future predictions....
arXiv:2606. 13035v1 Announce Type: cross Abstract: Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content.
The paper introduces Connected Self Forcing, a training framework that reconnects gradient paths across autoregressive video chunks to improve long‑horizon visual quality and temporal consistency. It allows feedback from later predictions to influence earlier context generation, using shortcut gradient replay to keep memory usage manageable. The method is combined with distribution‑matching distillation and shows experimental gains without altering the inference procedure.
Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content. However, extending these models to minute-level generation remains challenging: the limited KV-cache budget prevents the model from retaining the full history, while repeatedly conditioning on self-generated frames induces a context distribution shift that accumulates over time, leading to visual artifacts, quality degradation, and temporal drift.
arXiv:2610.14654v1 Announce Type: cross Abstract: Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate futu...
arXiv:2610.17434v1 Announce Type: cross Abstract: Recent advances in video world models have enabled plausible interactive environments, yet long-horizon generation places a fundamental demand on vis...
arXiv:2610.00812v1 Announce Type: cross Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamic...
Avatar‑Forever introduces a decoupled parallel training framework that separates efficient generation from long‑horizon robustness, allowing each capability to be trained independently. The method employs full‑parameter distillation for high‑quality generation and a lightweight long‑horizon adapter trained via Recovery‑oriented Rollout Training (RRT) to mitigate autoregressive errors. Additionally, a ForeverCache feature‑caching mechanism reduces redundant computation during streaming inference, enabling real‑time, high‑resolution avatar generation at 27.2 FPS on a single H100 GPU.
arXiv:2610.35148v1 Announce Type: new Abstract: Recent advances in generative video models have improved visual realism, making detection increasingly dependent on subtle temporal inconsistencies. Ho...
The paper introduces a decoupled memory control framework for spatially consistent long‑horizon video generation. By separating memory conditioning from generation, it uses a lightweight memory branch to learn spatial consistency from past observations and a per‑frame cross‑attention mechanism to condition each frame on the most relevant historical data. A camera‑aware gating mechanism further ensures memory is used only when meaningful references exist, resulting in lower training costs, higher spatial consistency, and state‑of‑the‑art visual quality.
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...
arXiv:2512.07480v2 Announce Type: replace Abstract: While traditional and neural video codecs (NVCs) have achieved remarkable rate-distortion performance, improving perceptual quality at low bitrates...