36 citations · 37 across the 12 of their papers we have counts for
13 papers
ORCA: The Annealed Spectral Conditioning Optimizer for Faster, Better LLM Training
Yuanshi Liu, Boyuan Jiang, Liang Hou +4
Modern LLM optimizers such as Muon often produce weight matrices with higher effective rank than Adam, yet further spectral control has delivered only modest gains. We identify a t…
VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization
Junhao Cheng, Liang Hou, Tianxiong Zhong +4
The recent "Reasoning with Video" paradigm utilizes Video Generation Models (VGMs) to generate temporally coherent visual trajectories to complete reasoning tasks. Although state-o…
SURF: Signature-Retained Fast Video Generation
Kaixin Ding, Xi Chen, Sihui Ji +4
The demand for high-resolution video generation is growing rapidly. However, the generation resolution is severely constrained by slow inference speeds. For instance, Wan2.1 requir…
Stable Velocity: A Variance Perspective on Flow Matching
Donglin Yang, Yongxing Zhang, Xin Yu +5
While flow matching is elegant, its reliance on single-sample conditional velocities leads to high-variance training targets that destabilize optimization and slow convergence. By…
VMonarch: Efficient Video Diffusion Transformers with Structured Attention
Cheng Liang, Haoxian Chen, Liang Hou +4
The quadratic complexity of the attention mechanism severely limits the context scalability of Video Diffusion Transformers (DiTs). We find that the highly sparse spatio-temporal a…
Video-as-Answer: Predict and Generate Next Video Event with Joint-GRPO
Junhao Cheng, Liang Hou, Xin Tao +1
While language models have become impactful in many real-world applications, video generation remains largely confined to entertainment. Motivated by video's inherent capacity to d…
Score Augmentation for Diffusion Models
Liang Hou, Yuan Gao, Boyuan Jiang +6
Diffusion models have achieved remarkable success in generative modeling. However, this study confirms the existence of overfitting in diffusion model training, particularly in dat…
VMoBA: Mixture-of-Block Attention for Video Diffusion Models
Jianzong Wu, Liang Hou, Haotian Yang +5
The quadratic complexity of full attention mechanisms poses a significant bottleneck for Video Diffusion Models (VDMs) aiming to generate long-duration, high-resolution videos. Whi…
MTV-Inpaint: Multi-Task Long Video Inpainting
Shiyuan Yang, Zheng Gu, Liang Hou +4
Video inpainting involves modifying local regions within a video, ensuring spatial and temporal consistency. Most existing methods focus primarily on scene completion (i.e., fillin…
Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings
Liang Hou, Cong Liu, Mingwu Zheng +4
Resolution generalization in image generation tasks enables the production of higher-resolution images with lower training resolution overhead. However, a key obstacle for diffusio…
RectifiedHR: Enable Efficient High-Resolution Synthesis via Energy Rectification
Zhen Yang, Guibao Shen, Minyang Li +5
Diffusion models have achieved remarkable progress across various visual generation tasks. However, their performance significantly declines when generating content at resolutions…
Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion
Shiyuan Yang, Liang Hou, Haibin Huang +5
Recent text-to-video diffusion models have achieved impressive progress. In practice, users often desire the ability to control object motion and camera movement independently for…
I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models
Xun Guo, Mingwu Zheng, Liang Hou +9
Text-guided image-to-video (I2V) generation aims to generate a coherent video that preserves the identity of the input image and semantically aligns with the input prompt. Existing…