5 papers
Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
Woojung Han, Seil Kang, Youngjun Jun +3
Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws. We reveal a surprising findi…
Interpretable Motion-Attentive Maps: Spatio-Temporally Localizing Concepts in Video Diffusion Transformers
Youngjun Jun, Seil Kang, Woojung Han +1
Video Diffusion Transformers (DiTs) have been synthesizing high-quality video with high fidelity from given text descriptions involving motion. However, understanding how Video DiT…
WAVE: Warp-Based View Guidance for Consistent Novel View Synthesis Using a Single Image
Jiwoo Park, Tae Eun Choi, Youngjun Jun +1
Generating high-quality novel views of a scene from a single image requires maintaining structural coherence across different views, referred to as view consistency. While diffusio…
Backbone Augmented Training for Adaptations
Jae Wan Park, Junhyeok Kim, Youngjun Jun +2
Adaptations facilitate efficient training of large backbone models, including diffusion models for image generation and transformer-based language models. While various adaptation…
PRETI: Patient-Aware Retinal Foundation Model via Metadata-Guided Representation Learning
Yeonkyung Lee, Woojung Han, Youngjun Jun +3
Retinal foundation models have significantly advanced retinal image analysis by leveraging self-supervised learning to reduce dependence on labeled data while achieving strong gene…