5 papers
SPARE: Structural Parameter-Free Affinity Regularization for Flow Matching
Zong-Wei Hong, Jinglun Li, Shen Zhang +3
Denoising diffusion transformers achieve strong generation quality but converge slowly during training. Regularizing their internal representations has emerged as an effective acce…
The Velocity Deficit: Initial Energy Injection for Flow Matching
Linze Li, Zong-Wei Hong, Shen Zhang +4
While Flow Matching theoretically guarantees constant-velocity trajectories, we identify a critical breakdown in high-dimensional practice: the Velocity Deficit. We show that the M…
ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting
Luchao Wang, Kaimin Liao, Qian Ren +3
A converged 3D Gaussian Splatting (3DGS) model may approximate the target scene while remaining poorly parameterized for further optimization. We identify this failure mode as \emp…
VeCoR -- Velocity Contrastive Regularization for Flow Matching
Zong-Wei Hong, Jing-lun Li, Lin-Ze Li +2
Flow Matching (FM) has recently emerged as a principled and efficient alternative to diffusion models. Standard FM encourages the learned velocity field to follow a target directio…
Representation Entanglement for Generation: Training Diffusion Transformers Is Much Easier Than You Think
Ge Wu, Shen Zhang, Ruijing Shi +9
REPA and its variants effectively mitigate training challenges in diffusion models by incorporating external visual representations from pretrained models, through alignment betwee…