5 citations · 8 across the 18 of their papers we have counts for
9 papers · 1 filter
AgenticGen: Reward-Guided Agentic Video Generation for Advertising
Xingyuan Bu, Chengru Song, Hao Zhou +9
Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose success is measured by online business metrics. Recent video…
Beyond Attention Masks: Instruction Anchoring for Efficient In-Context Diffusion Generation
Yangshuai Liu, Zheming Li, Jiaao Li +4
In-context diffusion transformers concatenate instruction, target, and reference tokens into a single sequence for joint attention. Reference-side computation must therefore be rep…
HeadCast: Casting Attention Heads for Efficient Autoregressive Video Generation
Jinliang Shen, Lianghao Su, Zheming Li +4
Autoregressive (AR) video diffusion models have become a promising paradigm for long and streaming video synthesis, but the continuously growing Key-Value (KV) cache makes attentio…
ScalingAttention: Discovering Intrinsic Sparse Attention Topology for Video Diffusion Transformers
Ruiliang Zhou, Xuecheng Wu, Kang He +6
While Diffusion Transformers (DiTs) have revolutionized high-fidelity video generation, their reliance on 3D full attention creates a quadratic computational bottleneck. Existing s…
Kwai Keye-VL-2.0 Technical Report
Kwai Keye Team, Bin Wen, Changyi Liu +50
We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding and agentic intelligence. To…
: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation
Linqian Fan, Peiqin Sun, Tiancheng Wen +2
Diffusion models achieve state-of-the-art generative performance but are fundamentally bottlenecked by their slow, iterative sampling process. While diffusion distillation techniqu…
Kwai Keye-VL Technical Report
Kwai Keye Team, Biao Yang, Bin Wen +57
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form vi…
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
Yang Jin, Zhicheng Sun, Kun Xu +9
In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Co…
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Yang Jin, Kun Xu, Liwei Chen +12
Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. How…