Aug 2026· International Journal of Computer Vision· Vol 134· 1 citation· 54 references
TL;DR
This work proposes a Scale-wise Conditional Decoding mechanism, which projects semantic signals from a frozen vision encoder into scale-specific layers of the VAR backbone, and introduces a Unified Control Alignment strategy (SCALAR-Uni) to handle diverse control modalities within a single projection space.
Unigen-AR, a framework that pairs a general-purpose multi-modal language model (MLLM) with an efficient next-scale visual auto-regressive (VAR) decoder, is proposed, establishing visual auto-regressive modeling as a compelling and efficient backbone for unified visual generation.
This work proposes FreqExit, an integrated loss design that enables dynamic inference in VAR without altering its architecture or compromising output quality, and designs targeted mechanisms that guide the model to learn more effectively through frequency-aware supervision.
This work proposes Compact Autoregressive Latent Prior (CALP), which regularizes low-dimensional latent variables with a history-conditioned autoregressive prior, achieving compact, long-range temporally coherent, and spatially structured latent representations through a more reasonable latent capacity allocation and a...
Ruosen Hao, Tian-Yu Shen, Jing-Yuan Gao et al.· IEEE Transactions on Pattern...· 0 citations
VISTA is the first gradient-based test-time alignment framework for next-scale autoregressive image generation, and introduces the mechanisms needed to make such optimization stable across scales, together with an extensible objective space that any differentiable constraint on cross-attention can plug into.
DenseAR is extended to a unified model that handles multiple modalities and imaging tasks within a single backbone that unifies cross-modal translation, modality-conditioned generation, and tumor segmentation, while remaining competitive with task-specific methods.
Chicago Y. Park, Jia-Lin Mao, Xiaojian Xu et al.· arXiv.org· 0 citations
MotionCraft is presented, a controllable VSR framework that formulates restoration as motion-aware latent state prediction inspired by world models and integrates adaptive sparse attention with an explicit user-accessible control interface to deliver temporally consistent, high-quality reconstructions under streaming c...
Rong Fu, Chun-Lei Meng, Yangcheng Zeng et al.· 1 citation
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