3 papers
cs.CV2026
Learning Compositional Spatio-Temporal Video Grounding with Synthetic Curriculum
Xingjian Wang, Shijian Wang, Yibo Wang +4
Despite the impressive progress of recent MLLMs on spatio-temporal video grounding (STVG), existing evaluations and training data focus primarily on simple queries. They largely ov…
cs.GR2026
Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation
Diandian Gu, Jing Lin, Gaohong Liu +25
We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulation-ready capabilities, and ap…
cs.CV2025
Coefficients-Preserving Sampling for Reinforcement Learning with Flow Matching
Feng Wang, Zihao Yu
Reinforcement Learning (RL) has recently emerged as a powerful technique for improving image and video generation in Diffusion and Flow Matching models, specifically for enhancing…