2 citations · 4 across the 13 of their papers we have counts for
7 papers · 1 filter
WorldCompass: Reinforcement Learning for Long-Horizon World Models
Zehan Wang, Tengfei Wang, Haiyu Zhang +9
This work presents WorldCompass, a novel Reinforcement Learning (RL) post-training framework for the long-horizon, interactive video-based world models, enabling them to explore th…
Unified Thinker: A General Reasoning Modular Core for Image Generation
Sashuai Zhou, Qiang Zhou, Jijin Hu +9
Despite impressive progress in high-fidelity image synthesis, generative models still struggle with logic-intensive instruction following, exposing a persistent reasoning--executio…
UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits
Keming Ye, Zhipeng Huang, Canmiao Fu +7
With the rapid advances of powerful multimodal models such as GPT-4o, Nano Banana, and Seedream 4.0 in Image Editing, the performance gap between closed-source and open-source mode…
PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards
Shulei Wang, Longhui Wei, Xin He +4
Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, when extended to multiple subject…
FADA: Fast Diffusion Avatar Synthesis with Mixed-Supervised Multi-CFG Distillation
Tianyun Zhong, Chao Liang, Jianwen Jiang +3
Diffusion-based audio-driven talking avatar methods have recently gained attention for their high-fidelity, vivid, and expressive results. However, their slow inference speed limit…
Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models
Zijian Zhang, Luping Liu, Zhijie Lin +2
We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. Our method is derived from an existing…
Extending Multi-modal Contrastive Representations
Zehan Wang, Ziang Zhang, Luping Liu +4
Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning. Although recent methods showcase impressive achievements, the high d…