activity
20232026
most citedSeedance 1.0: Exploring the Boundaries of Video Generation Models

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Cola2: Co-Adaptive Hierarchical Language Modeling with Continuous Semantic Compression and Balanced Discrete Generation

Hongcan Guo, Yian Zhao, Feng Wang +4

Hierarchical language generation separates continuous semantic modeling from discrete text realization, but its effectiveness depends on what the latent space compresses and how th…

cs.CV2026

Improving Video Sparse Attention with Fine-grained Router and Sparse Rebasing

Peiyuan Zhang, Guoqiang Wei, Yilong Zhao +7

We present VSA2, a frontier trainable sparse attention for video DiTs. VSA2 includes a variety of new architectural features and training procedures that we apply across all stages…

cs.CL2026

Continuous Latent Diffusion Language Model

Hongcan Guo, Qinyu Zhao, Yian Zhao +8

Large language models have achieved remarkable success under the autoregressive paradigm, yet high-quality text generation need not be tied to a fixed left-to-right order. Existing…

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.CV2024

Training-free Camera Control for Video Generation

Chen Hou, Zhibo Chen

We propose a training-free and robust solution to offer camera movement control for off-the-shelf video diffusion models. Unlike previous work, our method does not require any supe…

cs.CV2024

Boximator: Generating Rich and Controllable Motions for Video Synthesis

Jiawei Wang, Yuchen Zhang, Jiaxin Zou +4

Generating rich and controllable motion is a pivotal challenge in video synthesis. We propose Boximator, a new approach for fine-grained motion control. Boximator introduces two co…

cs.CV2023

Make Pixels Dance: High-Dynamic Video Generation

Yan Zeng, Guoqiang Wei, Jiani Zheng +4

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, curr…