6 papers
Flexible Geometric Guidance for Probabilistic Human Pose Estimation with Diffusion Models
Francis Snelgar, Ming Xu, Stephen Gould +2
3D human pose estimation from 2D images is a challenging problem due to depth ambiguity and occlusion. Because of these challenges the task is underdetermined, where there exists m…
Gromov Wasserstein Optimal Transport for Semantic Correspondences
Francis Snelgar, Stephen Gould, Ming Xu +2
Establishing correspondences between image pairs is a long studied problem in computer vision. With recent large-scale foundation models showing strong zero-shot performance on dow…
SimFlow: Simplified and End-to-End Training of Latent Normalizing Flows
Qinyu Zhao, Guangting Zheng, Tao Yang +4
Normalizing Flows (NFs) learn invertible mappings between the data and a Gaussian distribution. Prior works usually suffer from two limitations. First, they add random noise to tra…
DiSA: Diffusion Step Annealing in Autoregressive Image Generation
Qinyu Zhao, Jaskirat Singh, Ming Xu +3
An increasing number of autoregressive models, such as MAR, FlowAR, xAR, and Harmon adopt diffusion sampling to improve the quality of image generation. However, this strategy lead…
ARINAR: Bi-Level Autoregressive Feature-by-Feature Generative Models
Qinyu Zhao, Stephen Gould, Liang Zheng
Existing autoregressive (AR) image generative models use a token-by-token generation schema. That is, they predict a per-token probability distribution and sample the next token fr…
Negative Token Merging: Image-based Adversarial Feature Guidance
Jaskirat Singh, Lindsey Li, Weijia Shi +7
Text-based adversarial guidance using a negative prompt has emerged as a widely adopted approach to steer diffusion models away from producing undesired concepts. While useful, per…