3 citations · 5 across the 22 of their papers we have counts for
24 papers
RepForcing: Representation-Space Initialization for Autoregressive Video Distillation
Siming Fu, Zheming Fu, Lianrui Mu +3
Distilling pretrained video diffusion models into few-step autoregressive (AR) generators typically combines student initialization with distribution matching distillation (DMD). A…
MedUP: Awakening Unified Understanding and Perception in Medical Vision-Language Models
Yuan Wang, Hualiang Wang, Yixin Chen +6
Medical Vision-Language Models (Med-VLMs) excel at verbalizing visual content, yet precise visual perception, segmentation, and grounding remain challenging. Existing approaches ei…
Poly-OPD: Heterogeneous Multi-Teacher On-Policy Distillation for Capability-Selectable Flow Models
Siming Fu, Haojun Xu, Ruizhe He +9
Leading open text-to-image models often carry complementary strengths: one may lead on preference-aligned aesthetics while another follows compositional instructions more faithfull…
JoyAI-Video-Edit: Real-Time Open-Ended Video Editing with Autoregressive Diffusion
Yicheng Xiao, Wenxun Dai, Xinran Qin +22
Real-time video editing requires low-latency causal generation with bounded computational resources while preserving source fidelity and long-term temporal consistency. We present…
Any-OPD: Heterogeneous On-Policy Distillation for Flow-Matching Models via Representation-Space Bridging
Siming Fu, Zheming Fu, Ruizhe He +7
On-policy distillation, in which a teacher corrects samples that the student itself generates, presupposes that the two models speak the same language: identical VAE latents, match…
Perceptual Flow Matching for Few-Step Generative Modeling
Chuyang Zhao, Yifei Song, Hongfa Wang +7
We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velocity regression in the conve…
FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification
Zheming Fu, Ruizhe He, Wei Shang +5
Aligning generative flow models on continuous spaces via online reinforcement learning is constrained by intractable trajectory likelihoods. Existing density-approximated policy gr…
AnchorEdit: Maintaining Temporal Consistency in Multi-turn Image Editing via Causal Memory
Hang Xu, Xiaoxiao Ma, Guohui Zhang +7
Multi-turn image editing is essential for iterative design, yet current models often struggle with identity drift and error accumulation over successive steps. While existing resea…
Ultra Flash: Scaling Real-Time Streaming Video Generation to High Resolutions
Xin Lu, Luxury, Jie Huang +27
While recent autoregressive video diffusion models achieve remarkable streaming quality, they remain confined to low resolutions (e.g., 480P), leaving efficient, scalable, real-tim…
HPSv3++: Scaling Reward Models Across the Full Spectrum of Diffusion Model Capabilities
Yijun Liu, Jie Huang, Zeyue Xue +5
Reward models guide text-to-image (T2I) systems toward outputs aligned with human preferences. However, typical reward models such as HPSv3 are trained on pre-annotated data from e…
OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation
Guohui Zhang, XiaoXiao Ma, Jie Huang +9
Recent advances in joint audio-video generation have been remarkable, yet real-world applications demand strong per-modality fidelity, cross-modal alignment, and fine-grained synch…
A Systematic Post-Train Framework for Video Generation
Zeyue Xue, Siming Fu, Jie Huang +9
While large-scale video diffusion models have demonstrated impressive capabilities in generating high-resolution and semantically rich content, a significant gap remains between th…
MAR-GRPO: Stabilized GRPO for AR-diffusion Hybrid Image Generation
Xiaoxiao Ma, Jiachen Lei, Tianfei Ren +6
Reinforcement learning (RL) has been successfully applied to autoregressive (AR) and diffusion models. However, extending RL to hybrid AR-diffusion frameworks remains challenging d…
Astrolabe: Steering Forward-Process Reinforcement Learning for Distilled Autoregressive Video Models
Songchun Zhang, Zeyue Xue, Siming Fu +6
Distilled autoregressive (AR) video models enable efficient streaming generation but frequently misalign with human visual preferences. Existing reinforcement learning (RL) framewo…
OmniForcing: Unleashing Real-time Joint Audio-Visual Generation
Yaofeng Su, Yuming Li, Zeyue Xue +7
Recent joint audio-visual diffusion models achieve remarkable generation quality but suffer from high latency due to their bidirectional attention dependencies, hindering real-time…
MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement
Dong She, Siming Fu, Mushui Liu +4
Multi-subject personalized generation presents unique challenges in maintaining identity fidelity and semantic coherence when synthesizing images conditioned on multiple reference…
FocusDPO: Dynamic Preference Optimization for Multi-Subject Personalized Image Generation via Adaptive Focus
Qiaoqiao Jin, Siming Fu, Dong She +4
Multi-subject personalized image generation aims to synthesize customized images containing multiple specified subjects without requiring test-time optimization. However, achieving…
CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers
D. She, Mushui Liu, Jingxuan Pang +9
Customized generation has achieved significant progress in image synthesis, yet personalized video generation remains challenging due to temporal inconsistencies and quality degrad…
RestorerID: Towards Tuning-Free Face Restoration with ID Preservation
Jiacheng Ying, Mushui Liu, Zhe Wu +7
Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradat…
Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image Generation
Qihan Huang, Siming Fu, Jinlong Liu +3
Personalized text-to-image generation methods can generate customized images based on the reference images, which have garnered wide research interest. Recent methods propose a fin…
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation
Fangxun Shu, Yue Liao, Le Zhuo +14
We introduce LLaVA-MoD, a novel framework designed to enable the efficient training of small-scale Multimodal Language Models (s-MLLM) by distilling knowledge from large-scale MLLM…
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
Wanggui He, Siming Fu, Mushui Liu +10
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis. In…
MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance
Xierui Wang, Siming Fu, Qihan Huang +2
Recent advancements in text-to-image generation models have dramatically enhanced the generation of photorealistic images from textual prompts, leading to an increased interest in…
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System
Haoyuan Li, Hao Jiang, Tianke Zhang +6
Training AI models has always been challenging, especially when there is a need for custom models to provide personalized services. Algorithm engineers often face a lengthy process…