Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really need all this complexity?
We design a small and simple one-step model, ROMS-IMLE, which attains an FID of 2.56 on ImageNet.
Joint w/ @researchirag
1/6
Conditional IMLE (cIMLE) handles multimodal distributions by giving the model the opportunity to generate samples from multiple modes and avoiding penalizing samples from the modes that are different from the mode corresponding to the current observation.
7/8
Excited to share another work at ECCV 2026!
I’ll be presenting PointGT at #ECCV2026 tomorrow — on Thursday, Sep 10 at 10:30 AM – 12:30 AM poster # 206! Come by if you’re interested in texture or geometry editing in 3D. Would love to chat and hear your thoughts!
Excited to share our ECCV 2026 work, P-CORE!
I’ll be presenting this work at #ECCV2026 — come chat with me on Thu, Sep 10 at 10:30 AM – 12:30 AM poster # 207, if you’re interested in neural rendering, 3D reconstruction, or 3D editing!