1 citations · 3 across the 10 of their papers we have counts for
11 papers
TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation
Hongyu Zhang, Yufan Deng, Zilin Pan +6
Generating high-quality videos from complex temporal descriptions that contain multiple sequential actions is a key unsolved problem. Existing methods are constrained by an inheren…
Focal Guidance: Unlocking Controllability from Semantic-Weak Layers in Video Diffusion Models
Yuanyang Yin, Yufan Deng, Shenghai Yuan +3
The task of Image-to-Video (I2V) generation aims to synthesize a video from a reference image and a text prompt. This requires diffusion models to reconcile high-frequency visual c…
MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction
Zishan Shu, Yufan Deng, Hongyu Zhang +2
Activity cliff prediction is a critical task in drug discovery and material design. Existing computational methods are limited to handling single binding targets, which restricts t…
OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
Shenghai Yuan, Xianyi He, Yufan Deng +5
Subject-to-Video (S2V) generation aims to create videos that faithfully incorporate reference content, providing enhanced flexibility in the production of videos. To establish the…
MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
Yufan Deng, Yuanyang Yin, Xun Guo +8
We tackle the task of any-reference video generation, which aims to synthesize videos conditioned on arbitrary types and combinations of reference subjects, together with textual p…
VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning
Xianwei Zhuang, Yuxin Xie, Yufan Deng +5
In this work, we present VARGPT-v1.1, an advanced unified visual autoregressive model that builds upon our previous framework VARGPT. The model preserves the dual paradigm of next-…
CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance
Yufan Deng, Xun Guo, Yizhi Wang +7
Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality…
Comp-Attn: Present-and-Align Attention for Compositional Video Generation
Hongyu Zhang, Yufan Deng, Shenghai Yuan +5
In the domain of text-to-video (T2V) generation, reliably synthesizing compositional content involving multiple subjects with intricate relations is still underexplored. The main c…
VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model
Xianwei Zhuang, Yuxin Xie, Yufan Deng +4
We present VARGPT, a novel multimodal large language model (MLLM) that unifies visual understanding and generation within a single autoregressive framework. VARGPT employs a next-t…
VideoTetris: Towards Compositional Text-to-Video Generation
Ye Tian, Ling Yang, Haotian Yang +9
Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scen…
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…