arXiv:2608. 10519v2 Announce Type: replace Abstract: InfinityStar extends visual autoregressive generation to video through a sequence of image and clip pyramids.
By Jongbeom Lee, Hyunwoo Yu, Jincheol Yang, Jaemin Choi, Suk-Ju Kang
arXiv:2609.37001v1 Announce Type: cross
Abstract: Diffusion Transformers (DiTs) enable high-quality video generation but suffer from substantial inference latency, primarily attributable to the compu...
By Xingyu Jia, Baole Ai, Ang Wang, Kang Zhao, Yong Li
arXiv:2602. 01801v2 Announce Type: replace-cross Abstract: Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines.
By Dvir Samuel, Issar Tzachor, Matan Levy, Michael Green, Gal Chechik, Rami Ben-Ari
arXiv:2610.01013v1 Announce Type: new
Abstract: Feed-forward 3D vision models such as VGGT have achieved remarkable progress, unifying camera estimation and dense scene reconstruction in a single pas...
By Junyi Wu, Fanqing Kong, Leyang Chen, Shaoqiu Zhang, Yulun Zhang
The paper investigates how attention sparsity behaves in autoregressive image generation, finding a distinct diagonal sparsity pattern due to spatial locality of visual tokens. It introduces a diagonal‑aware sparse attention mechanism that skips KV entries along the diagonal within a recent window, achieving up to 3.1× higher throughput and 1.19× lower latency with less than 2% quality loss compared to dense inference.
By Daeun Kim, Junwha Hong, Changhun Oh, Yoonsung Kim, Yoonhyeong Lee, Jongse Park
arXiv:2505.16157v3 Announce Type: replace
Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Tran...
By Yuang Ai