most citedT2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

3 papers · 1 filter

cs.CV2025

T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models

Xuyang Guo, Jiayan Huo, Zhenmei Shi +3

Thanks to recent advancements in scalable deep architectures and large-scale pretraining, text-to-video generation has achieved unprecedented capabilities in producing high-fidelit…

cs.CV2025

Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models

Xuyang Guo, Zekai Huang, Jiayan Huo +4

Generative models have driven significant progress in a variety of AI tasks, including text-to-video generation, where models like Video LDM and Stable Video Diffusion can produce…

cs.CV2025

Text-to-Image Diffusion Models Cannot Count, and Prompt Refinement Cannot Help

Xuyang Guo, Jiayan Huo, Yingyu Liang +4

Generative modeling is widely regarded as one of the most essential problems in today's AI community, with text-to-image generation having gained unprecedented real-world impacts.…