3 accepted papers
Knowledge Distillation (KD) aims to improve a light-weight student network supervised by a large teacher network. The core idea of KD is to explore valuable knowledge from the teacher. Previous works often extract information from a single sample, but ignore relation modeling among multiple samples…
Removing unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge. This difficulty is especially pronounced in emerging paradigms, such as Stable Diffusion (SD) v3 and Flux, which incorporate flow matching an…
Text-to-motion generation, which translates textual descriptions into human motions, has been challenging in accurately capturing detailed user-imagined motions from simple text inputs. This paper introduces StickMotion, an efficient diffusion-based network designed for multi-condition scenarios, wh…