4 papers · 1 filter
Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation
NaHyeon Park, Minhyun Lee, Hyunjung Shim
Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, applied broadly across prompts.…
Grounding Driving VLA via Inverse Kinematics
Junsung Park, Hyunjung Shim
Existing Driving VLAs predict trajectories while largely ignoring their visual tokens -- a phenomenon we trace not to insufficient training but to a structurally ill-posed task for…
SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals
Soyeon Yoon, Chang Wook Seo, Hyunjung Shim
Learning dense correspondences across deformable 3D shapes remains a long-standing challenge due to structural variability, non-isometric deformation, and inconsistent topology. Ex…
Representation Alignment for Just Image Transformers is not Easier than You Think
Jaeyo Shin, Jiwook Kim, Hyunjung Shim
Representation Alignment (REPA) has emerged as a simple way to accelerate Diffusion Transformers training in latent space. At the same time, pixel-space diffusion transformers such…