89 citations · 137 across the 16 of their papers we have counts for
16 papers
UniRL-Zero: Reinforcement Learning on Unified Models with Joint Language Model and Diffusion Model Experts
Fu-Yun Wang, Han Zhang, Michael Gharbi +2
We present UniRL-Zero, a unified reinforcement learning (RL) framework that boosts, multimodal language model understanding and reasoning, diffusion model multimedia generation, an…
Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models
Fu-Yun Wang, Yunhao Shui, Jingtan Piao +2
Diffusion models have made substantial advances in image generation, yet models trained on large, unfiltered datasets often yield outputs misaligned with human preferences. Numerou…
Self-NPO: Data-Free Diffusion Model Enhancement via Truncated Diffusion Fine-Tuning
Fu-Yun Wang, Keqiang Sun, Yao Teng +4
Diffusion models have demonstrated remarkable success in various visual generation tasks, including image, video, and 3D content generation. Preference optimization (PO) is a promi…
GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking
Weikang Bian, Zhaoyang Huang, Xiaoyu Shi +3
4D video control is essential in video generation as it enables the use of sophisticated lens techniques, such as multi-camera shooting and dolly zoom, which are currently unsuppor…
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events
Yijin Li, Yichen Shen, Zhaoyang Huang +9
Recent advances in event-based vision suggest that these systems complement traditional cameras by providing continuous observation without frame rate limitations and a high dynami…
Stable Consistency Tuning: Understanding and Improving Consistency Models
Fu-Yun Wang, Zhengyang Geng, Hongsheng Li
Diffusion models achieve superior generation quality but suffer from slow generation speed due to the iterative nature of denoising. In contrast, consistency models, a new generati…
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Le Zhuo, Ruoyi Du, Han Xiao +19
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and vi…
Phased Consistency Models
Fu-Yun Wang, Zhaoyang Huang, Alexander William Bergman +9
Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image ge…
Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance
Dazhong Shen, Guanglu Song, Zeyue Xue +2
Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models, where the CFG scale is introduced to control the strength of text guidance on the whole image…
Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation
Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang +5
Video outpainting is a challenging task, aiming at generating video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. Exist…
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
Fu-Yun Wang, Zhaoyang Huang, Weikang Bian +5
This paper introduces an effective method for computation-efficient personalized style video generation without requiring access to any personalized video data. It reduces the nece…
Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling
Xiaoyu Shi, Zhaoyang Huang, Fu-Yun Wang +9
We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated imag…
Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising
Fu-Yun Wang, Wenshuo Chen, Guanglu Song +3
Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and edi…
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye +1
The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categori…
Forward Compatible Few-Shot Class-Incremental Learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye +3
Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without fo…
PyCIL: A Python Toolbox for Class-Incremental Learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye +1
Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process. However, real-world a…