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cs.CV2026

Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion

Henglin Liu, Fangyuan Kong, Jing Wang +7

The paper introduces concentrated Implicit Preference Optimization (cIPO), a post‑training method for text‑to‑video diffusion models that derives preference signals from reconstruc…

cs.CV2026

MAVIN: Multi-Shot Audio-Visual Generation with Customized Narrative Control

Kaiqi Liu, Yunyao Mao, Ziqi Cai +8

MAVIN is a framework for generating multi-shot audio‑visual content with fine‑grained narrative control, using boundary‑aware attention to align temporal segments and ID‑aware prop…

cs.CV2026

HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer

Qi Cai, Jingwen Chen, Chengmin Gao +22

The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDr…

cs.CV2026

Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping

Haoyuan Sun, Jing Wang, Yuxin Song +9

Recently, post-training methods based on reinforcement learning, with a particular focus on Group Relative Policy Optimization (GRPO), have emerged as the robust paradigm for furth…

cs.CV2026

CADC: Content Adaptive Diffusion-Based Generative Image Compression

Xihua Sheng, Lingyu Zhu, Tianyu Zhang +3

Diffusion-based generative image compression has demonstrated remarkable potential for achieving realistic reconstruction at ultra-low bitrates. The key to unlocking this potential…

cs.CV2025

DiverseGRPO: Mitigating Mode Collapse in Image Generation via Diversity-Aware GRPO

Henglin Liu, Huijuan Huang, Jing Wang +3

Reinforcement learning (RL), particularly GRPO, improves image generation quality significantly by comparing the relative performance of images generated within the same group. How…