3 papers
cs.CV2026
Rethinking Test Time Scaling for Flow-Matching Generative Models
Qingtao Yu, Changlin Song, Minghao Sun +6
The performance of text-to-image diffusion models may be improved at test-time by scaling computation to search for a generated image that maximizes a given reward function. While…
cs.CV2025
AutoRefiner: Improving Autoregressive Video Diffusion Models via Reflective Refinement Over the Stochastic Sampling Path
Zhengyang Yu, Akio Hayakawa, Masato Ishii +4
Autoregressive video diffusion models (AR-VDMs) show strong promise as scalable alternatives to bidirectional VDMs, enabling real-time and interactive applications. Yet there remai…
cs.CV2025
Probability Density Geodesics in Image Diffusion Latent Space
Qingtao Yu, Jaskirat Singh, Zhaoyuan Yang +5
Diffusion models indirectly estimate the probability density over a data space, which can be used to study its structure. In this work, we show that geodesics can be computed in di…