6 citations · 9 across the 11 of their papers we have counts for
12 papers
Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation
Xingtong Ge, Yi Zhang, Yushi Huang +6
Distilling video generation models to extremely low inference budgets (e.g., 2--4 NFEs) is crucial for real-time deployment, yet remains challenging. Trajectory-style consistency d…
Improving Joint Audio-Video Generation with Cross-Modal Context Learning
Bingqi Ma, Linlong Lang, Ming Zhang +5
The dual-stream transformer architecture-based joint audio-video generation method has become the dominant paradigm in current research. By incorporating pre-trained video diffusio…
AR-CoPO: Align Autoregressive Video Generation with Contrastive Policy Optimization
Dailan He, Guanlin Feng, Xingtong Ge +5
Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis, yet remain difficult to align via reinforcement lear…
Neighbor GRPO: Contrastive ODE Policy Optimization Aligns Flow Models
Dailan He, Guanlin Feng, Xingtong Ge +6
Group Relative Policy Optimization (GRPO) has shown promise in aligning image and video generative models with human preferences. However, applying it to modern flow matching model…
ADT: Tuning Diffusion Models with Adversarial Supervision
Dazhong Shen, Guanglu Song, Yi Zhang +5
Diffusion models have achieved outstanding image generation by reversing a forward noising process to approximate true data distributions. During training, these models predict dif…
High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning
Dailan He, Xiahong Wang, Shulun Wang +5
Face swapping aims to seamlessly transfer a source facial identity onto a target while preserving target attributes such as pose and expression. Diffusion models, known for their s…
VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping
Hao Shao, Shulun Wang, Yang Zhou +7
Video face swapping is becoming increasingly popular across various applications, yet existing methods primarily focus on static images and struggle with video face swapping becaus…
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM
Zhuofan Zong, Dongzhi Jiang, Bingqi Ma +5
Significant achievements in personalization of diffusion models have been witnessed. Conventional tuning-free methods mostly encode multiple reference images by averaging their ima…
Pretrained Reversible Generation as Unsupervised Visual Representation Learning
Rongkun Xue, Jinouwen Zhang, Yazhe Niu +4
Recent generative models based on score matching and flow matching have significantly advanced generation tasks, but their potential in discriminative tasks remains underexplored.…
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models
Bingqi Ma, Zhuofan Zong, Guanglu Song +2
Large language models (LLMs) based on decoder-only transformers have demonstrated superior text understanding capabilities compared to CLIP and T5-series models. However, the parad…
MoVA: Adapting Mixture of Vision Experts to Multimodal Context
Zhuofan Zong, Bingqi Ma, Dazhong Shen +5
As the key component in multimodal large language models (MLLMs), the ability of the visual encoder greatly affects MLLM's understanding on diverse image content. Although some lar…
Towards Large-scale Masked Face Recognition
Manyuan Zhang, Bingqi Ma, Guanglu Song +3
During the COVID-19 coronavirus epidemic, almost everyone is wearing masks, which poses a huge challenge for deep learning-based face recognition algorithms. In this paper, we will…