most citedSeedance 1.0: Exploring the Boundaries of Video Generation Models

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

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

Henglin Liu, Fangyuan Kong, Jing Wang +7

Recent advances in preference alignment for diffusion-based video generation, particularly via Direct Preference Optimization (DPO), have significantly improved visual quality. How…

cs.AI2026

Edit-R2: Context-Aware Reinforcement Learning for Multi-Turn Image Editing

Yuxiao Ye, Haoran He, Fangyuan Kong +4

Text-guided image editing has advanced rapidly with diffusion models and unified multimodal foundation models. However, most existing methods remain confined to single-turn setting…

cs.CV2025

InfinityHuman: Towards Long-Term Audio-Driven Human

Xiaodi Li, Pan Xie, Yi Ren +6

Audio-driven human animation has attracted wide attention thanks to its practical applications. However, critical challenges remain in generating high-resolution, long-duration vid…

cs.CV2025★ 1 cited

Seedance 1.0: Exploring the Boundaries of Video Generation Models

Yu Gao, Haoyuan Guo, Tuyen Hoang +41

Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously b…

cs.CV2025

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment

Shuhao Han, Haotian Fan, Fangyuan Kong +112

This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment, which will be held in conjunction with the New Trends in Image Restoratio…

cs.CV2025

Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model

Team Seawead, Ceyuan Yang, Zhijie Lin +52

This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parame…