most citedSeedance 1.0: Exploring the Boundaries of Video Generation Models

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2026

KwaiMind Technical Report

Junlong Wu, Zijun Li, Yuting Hu +14

Commercial image editing requires product identity preservation, accurate text rendering, and user appeal alongside general editing quality. We present KwaiMind, an image editing s…

cs.CV2026

Joint Alignment and Distillation for Video Generation via Sample-Guided Distribution Matching

Jiuzhou Lin, Junlong Wu, Fei Zuo +11

Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suffers from extensive computational overhead. Existing workflows typical…

cs.LG2026

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory

Fei Zuo, Zikang Zhou, Hao Cong +2

Large language models cache all previously computed key-value (KV) pairs during generation, and this KV cache grows linearly with sequence length, making it a primary memory bottle…

cs.CV2026

CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating

Jiyuan Wang, Huan Ouyang, Jiuzhou Lin +15

In this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it first conducts a global tem…

cs.LG2026

FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels

Fei Zuo, Xiaoyan Xi, Quanyi Zeng +2

Large language models are increasingly deployed on CPU-only platforms where memory bandwidth is the primary bottleneck for autoregressive generation. Weight quantization to four bi…

cs.CV2026

Seedance 2.0: Advancing Video Generation for World Complexity

Team Seedance, De Chen, Liyang Chen +168

Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…

cs.CV2025

Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model

Team Seedance, Heyi Chen, Siyan Chen +194

Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically f…

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…