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Covering Human Action Space for Computer Use: Data Synthesis and Benchmark
Miaosen Zhang, Xiaohan Zhao, Zhihong Tan +14
Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions is still poor, limiting user…
AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation
Ziwei Zhou, Zeyuan Lai, Rui Wang +6
Text-to-Audio-Video (T2AV) generation is rapidly becoming a core interface for media creation, yet its evaluation remains fragmented. Existing benchmarks largely assess audio and v…
DynaVid: Learning to Generate Highly Dynamic Videos using Synthetic Motion Data
Wonjoon Jin, Jiyun Won, Janghyeok Han +4
Despite recent progress, video diffusion models still struggle to synthesize realistic videos involving highly dynamic motions or requiring fine-grained motion controllability. A c…
Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering
Zeyu Liu, Weicong Liang, Zhanhao Liang +4
Visual text rendering poses a fundamental challenge for contemporary text-to-image generation models, with the core problem lying in text encoder deficiencies. To achieve accurate…
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
Yanhui Wang, Jianmin Bao, Wenming Weng +12
We present MicroCinema, a straightforward yet effective framework for high-quality and coherent text-to-video generation. Unlike existing approaches that align text prompts with vi…
ARTV: Auto-Regressive Text-to-Video Generation with Diffusion Models
Wenming Weng, Ruoyu Feng, Yanhui Wang +10
We present ARTV, an efficient framework for auto-regressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-s…