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cs.CV2026

DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation

Thanh-Tung Le, Yunhan Zhao, Menglei Chai +5

Video diffusion transformers have achieved state-of-the-art visual quality, but their high inference cost remains a major bottleneck for real-time applications. Recent distillation…

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.CV2025

Wan-Animate: Unified Character Animation and Replacement with Holistic Replication

Gang Cheng, Xin Gao, Li Hu +23

We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by preci…

cs.CV2025

Wan-S2V: Audio-Driven Cinematic Video Generation

Xin Gao, Li Hu, Siqi Hu +20

Current state-of-the-art (SOTA) methods for audio-driven character animation demonstrate promising performance for scenarios primarily involving speech and singing. However, they o…

cs.CV2025

Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance

Li Hu, Guangyuan Wang, Zhen Shen +6

Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character anim…