5 citations · 5 across the 3 of their papers we have counts for
5 papers
Edit2Perceive: Image Editing Diffusion Models Are Strong Dense Perceivers
Yiqing Shi, Yiren Song, Mike Zheng Shou
Recent advances in diffusion transformers have shown remarkable generalization in visual synthesis, yet most dense perception methods still rely on text-to-image (T2I) generators d…
InstructUDrag: Joint Text Instructions and Object Dragging for Interactive Image Editing
Haoran Yu, Yi Shi
Text-to-image diffusion models have shown great potential for image editing, with techniques such as text-based and object-dragging methods emerging as key approaches. However, eac…
Demystify Protein Generation with Hierarchical Conditional Diffusion Models
Zinan Ling, Yi Shi, Brett McKinney +3
Generating novel and functional protein sequences is critical to a wide range of applications in biology. Recent advancements in conditional diffusion models have shown impressive…
PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers
Michael Xu, Yi Shi, KangKang Yin +1
Humans excel in navigating diverse, complex environments with agile motor skills, exemplified by parkour practitioners performing dynamic maneuvers, such as climbing up walls and j…
StableMotion: Training Motion Cleanup Models with Unpaired Corrupted Data
Yuxuan Mu, Hung Yu Ling, Yi Shi +5
Motion capture (mocap) data often exhibits visually jarring artifacts due to inaccurate sensors and post-processing. Cleaning this corrupted data can require substantial manual eff…