3 citations · 34 across the 47 of their papers we have counts for
5 papers · 1 filter
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…
HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
Yingyu Liang, Zhizhou Sha, Zhenmei Shi +2
Flow Matching and Transformer architectures have demonstrated remarkable performance in image generation tasks, with recent work FlowAR [Ren et al., 2024] synergistically integrati…
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.…
High-Order Matching for One-Step Shortcut Diffusion Models
Bo Chen, Chengyue Gong, Xiaoyu Li +5
One-step shortcut diffusion models [Frans, Hafner, Levine and Abbeel, ICLR 2025] have shown potential in vision generation, but their reliance on first-order trajectory supervision…
RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation
Yuefan Cao, Chengyue Gong, Xiaoyu Li +4
Text-to-video generation models have made impressive progress, but they still struggle with generating videos with complex features. This limitation often arises from the inability…