4 citations · 8 across the 13 of their papers we have counts for
14 papers
GARDO: Reinforcing Diffusion Models without Reward Hacking
Haoran He, Yuxiao Ye, Jie Liu +7
Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-tr…
SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder
Minglei Shi, Haolin Wang, Borui Zhang +11
Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generati…
Latent Diffusion Model without Variational Autoencoder
Minglei Shi, Haolin Wang, Wenzhao Zheng +6
Recent progress in diffusion-based visual generation has largely relied on latent diffusion models with variational autoencoders (VAEs). While effective for high-fidelity synthesis…
DiffMoE: Dynamic Token Selection for Scalable Diffusion Transformers
Minglei Shi, Ziyang Yuan, Haotian Yang +10
Diffusion models have demonstrated remarkable success in various image generation tasks, but their performance is often limited by the uniform processing of inputs across varying c…
Improving Video Generation with Human Feedback
Jie Liu, Gongye Liu, Jiajun Liang +14
Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this w…
ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning
Yuzhou Huang, Ziyang Yuan, Quande Liu +6
Text-to-video generation has made remarkable advancements through diffusion models. However, Multi-Concept Video Customization (MCVC) remains a significant challenge. We identify t…
Consistent Human Image and Video Generation with Spatially Conditioned Diffusion
Mingdeng Cao, Chong Mou, Ziyang Yuan +4
Consistent human-centric image and video synthesis aims to generate images or videos with new poses while preserving appearance consistency with a given reference image, which is c…
SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints
Jianhong Bai, Menghan Xia, Xintao Wang +6
Recent advancements in video diffusion models have shown exceptional abilities in simulating real-world dynamics and maintaining 3D consistency. This progress inspires us to invest…
3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation
Xiao Fu, Xian Liu, Xintao Wang +7
This paper aims to manipulate multi-entity 3D motions in video generation. Previous methods on controllable video generation primarily leverage 2D control signals to manipulate obj…
MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions
Xuan Ju, Yiming Gao, Zhaoyang Zhang +6
Sora's high-motion intensity and long consistent videos have significantly impacted the field of video generation, attracting unprecedented attention. However, existing publicly av…
Image Conductor: Precision Control for Interactive Video Synthesis
Yaowei Li, Xintao Wang, Zhaoyang Zhang +5
Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world…
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
Yuzhou Huang, Liangbin Xie, Xintao Wang +8
Current instruction-based editing methods, such as InstructPix2Pix, often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text…
MotionCtrl: A Unified and Flexible Motion Controller for Video Generation
Zhouxia Wang, Ziyang Yuan, Xintao Wang +4
Motions in a video primarily consist of camera motion, induced by camera movement, and object motion, resulting from object movement. Accurate control of both camera and object mot…
CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models
Ziyang Yuan, Mingdeng Cao, Xintao Wang +3
Incorporating a customized object into image generation presents an attractive feature in text-to-image generation. However, existing optimization-based and encoder-based methods a…