2 papers across 2 sessions
We present PhysDiff-VTON via physics modeling and trajectory optimization for better virtual try-on performance.
This paper introduces an effective Ientity Distribution-Oriented Physical Invariant Learning framework to deal with distribution variability with physical invariance.