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cs.CV2026
AHPA: Adaptive Hierarchical Prior Alignment for Diffusion Transformers
Ruibin Min, Yexin Liu, Aimin Pan +5
Representation alignment has recently emerged as an effective paradigm for accelerating Diffusion Transformer training. Despite their success, existing alignment methods typically…
cs.CV2025
OpenSubject: Leveraging Video-Derived Identity and Diversity Priors for Subject-driven Image Generation and Manipulation
Yexin Liu, Manyuan Zhang, Yueze Wang +8
Despite the promising progress in subject-driven image generation, current models often deviate from the reference identities and struggle in complex scenes with multiple subjects.…
cs.CV2024
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection
Changsheng Lu, Zheyuan Liu, Piotr Koniusz
Exploiting the foundation models (e.g., CLIP) to build a versatile keypoint detector has gained increasing attention. Most existing models accept either the text prompt (e.g., ``th…