March 15, 2024, 4:46 a.m. | Huan-ang Gao, Mingju Gao, Jiaju Li, Wenyi Li, Rong Zhi, Hao Tang, Hao Zhao

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.09638v1 Announce Type: new
Abstract: Semantic image synthesis (SIS) shows good promises for sensor simulation. However, current best practices in this field, based on GANs, have not yet reached the desired level of quality. As latent diffusion models make significant strides in image generation, we are prompted to evaluate ControlNet, a notable method for its dense control capabilities. Our investigation uncovered two primary issues with its results: the presence of weird sub-structures within large semantic areas and the misalignment of …

abstract arxiv best practices categorical controlnet cs.cv current diff diffusion diffusion models gans good however image image generation latent diffusion models photo practices prior quality semantic sensor shows simulation spatial synthesis type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Field Sample Specialist (Air Sampling) - Eurofins Environment Testing – Pueblo, CO

@ Eurofins | Pueblo, CO, United States

Camera Perception Engineer

@ Meta | Sunnyvale, CA