7 papers
StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
Guanlong Jiao, Chenyangguang Zhang, Jia Jun Cheng Xian +2
Although existing video editing methods are generally feasible, they often require many costly iterations and still struggle to deliver high-quality yet satisfying editing results.…
Stable Velocity: A Variance Perspective on Flow Matching
Donglin Yang, Yongxing Zhang, Xin Yu +5
While flow matching is elegant, its reliance on single-sample conditional velocities leads to high-variance training targets that destabilize optimization and slow convergence. By…
UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
Guanlong Jiao, Biqing Huang, Kuan-Chieh Wang +1
Flow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are often ineffective or inapplicab…
TrajLoom: Dense Future Trajectory Generation from Video
Zewei Zhang, Jia Jun Cheng Xian, Kaiwen Liu +4
Predicting future motion is crucial in video understanding and controllable video generation. Dense point trajectories are a compact, expressive motion representation, but modeling…
QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution
Donglin Yang, Paul Vicol, Xiaojuan Qi +2
Deep learning-based super-resolution (SR) methods often perform pixel-wise computations uniformly across entire images, even in homogeneous regions where high-resolution refinement…
ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion
Nima Kondori, Hanwen Liang, Hooman Vaseli +5
Synthetic data generation represents a significant advancement in boosting the performance of machine learning (ML) models, particularly in fields where data acquisition is challen…