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20242026
most citedNext Patch Prediction for Autoregressive Visual Generation

1 citations · 3 across the 16 of their papers we have counts for

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cs.CV2024★ 1 cited

Next Patch Prediction for Autoregressive Visual Generation

Yatian Pang, Peng Jin, Shuo Yang +8

Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision task…

cs.CV2024★ 1 cited

VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention

Mingzhe Zheng, Yongqi Xu, Haojian Huang +8

Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutio…

cs.CV2024

Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning

Harold Haodong Chen, Harry Yang, Ser-Nam Lim

Recent advances in video generation have outpaced progress in video editing, which remains constrained by several limiting factors, namely: (a) the task's dependency on supervision…

cs.CV2024

DreamDance: Animating Human Images by Enriching 3D Geometry Cues from 2D Poses

Yatian Pang, Bin Zhu, Bin Lin +5

In this work, we present DreamDance, a novel method for animating human images using only skeleton pose sequences as conditional inputs. Existing approaches struggle with generatin…

cs.CV2024★ 1 cited

Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly

Yexin Liu, Zhengyang Liang, Yueze Wang +8

Multimodal Large Language Models (MLLMs) have displayed remarkable performance in multi-modal tasks, particularly in visual comprehension. However, we reveal that MLLMs often gener…