5 papers
Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
Haozhe Jia, Pengyu Yin, Wenshuo Chen +6
Physics-informed diffusion models typically enforce PDE constraints only on final outputs, leaving intermediate representations unconstrained and prone to shortcut learning under s…
Delta Score Matters! Spatial Adaptive Multi Guidance in Diffusion Models
Haosen Li, Wenshuo Chen, Lei Wang +4
Diffusion models have achieved remarkable success in synthesizing complex static and temporal visuals, a breakthrough largely driven by Classifier-Free Guidance (CFG). However, des…
POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
Wenshuo Chen, Haosen Li, Shaofeng Liang +6
The Inversion-Denoising Paradigm, which is based on diffusion models, excels in diverse image editing and restoration tasks. We revisit its mechanism and reveal a critical, overloo…
LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model
Haozhe Jia, Wenshuo Chen, Yuqi Lin +8
While current diffusion-based models, typically built on U-Net architectures, have shown promising results on the text-to-motion generation task, they still suffer from semantic mi…
ANT: Adaptive Neural Temporal-Aware Text-to-Motion Model
Wenshuo Chen, Kuimou Yu, Haozhe Jia +8
While diffusion models advance text-to-motion generation, their static semantic conditioning ignores temporal-frequency demands: early denoising requires structural semantics for m…