4 citations · 10 across the 30 of their papers we have counts for
12 papers · 1 filter
MemFlow: Flowing Adaptive Memory for Consistent and Efficient Long Video Narratives
Sihui Ji, Xi Chen, Shuai Yang +3
The core challenge for streaming video generation is maintaining the content consistency in long context, which poses high requirement for the memory design. Most existing solution…
UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation
Jiehui Huang, Yuechen Zhang, Xu He +7
Recent video generation models demonstrate impressive synthesis capabilities but remain limited by single-modality conditioning, constraining their holistic world understanding. Th…
Decoupling Complexity from Scale in Latent Diffusion Model
Tianxiong Zhong, Xingye Tian, Xuebo Wang +3
Existing latent diffusion models typically couple scale with content complexity, using more latent tokens to represent higher-resolution images or higher-frame rate videos. However…
Terra: Explorable Native 3D World Model with Point Latents
Yuanhui Huang, Weiliang Chen, Wenzhao Zheng +4
World models have garnered increasing attention for comprehensive modeling of the real world. However, most existing methods still rely on pixel-aligned representations as the basi…
PhysMaster: Mastering Physical Representation for Video Generation via Reinforcement Learning
Sihui Ji, Xi Chen, Xin Tao +2
Video generation models nowadays are capable of generating visually realistic videos, but often fail to adhere to physical laws, limiting their ability to generate physically plaus…
Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance
Jincheng Zhong, Boyuan Jiang, Xin Tao +3
Existing denoising generative models rely on solving discretized reverse-time SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue in this family of model…
Easier Painting Than Thinking: Can Text-to-Image Models Set the Stage, but Not Direct the Play?
Ouxiang Li, Yuan Wang, Xinting Hu +7
Text-to-image (T2I) generation aims to synthesize images from textual prompts, which jointly specify what must be shown and imply what can be inferred, which thus correspond to two…
MIDAS: Multimodal Interactive Digital-humAn Synthesis via Real-time Autoregressive Video Generation
Ming Chen, Liyuan Cui, Wenyuan Zhang +9
Recently, interactive digital human video generation has attracted widespread attention and achieved remarkable progress. However, building such a practical system that can interac…
DiffCap: Diffusion-based Real-time Human Motion Capture using Sparse IMUs and a Monocular Camera
Shaohua Pan, Xinyu Yi, Yan Zhou +4
Combining sparse IMUs and a monocular camera is a new promising setting to perform real-time human motion capture. This paper proposes a diffusion-based solution to learn human mot…
Training-Free Efficient Video Generation via Dynamic Token Carving
Yuechen Zhang, Jinbo Xing, Bin Xia +6
Despite the remarkable generation quality of video Diffusion Transformer (DiT) models, their practical deployment is severely hindered by extensive computational requirements. This…
VFRTok: Variable Frame Rates Video Tokenizer with Duration-Proportional Information Assumption
Tianxiong Zhong, Xingye Tian, Boyuan Jiang +4
Modern video generation frameworks based on Latent Diffusion Models suffer from inefficiencies in tokenization due to the Frame-Proportional Information Assumption. Existing tokeni…
BadVideo: Stealthy Backdoor Attack against Text-to-Video Generation
Ruotong Wang, Mingli Zhu, Jiarong Ou +4
Text-to-video (T2V) generative models have rapidly advanced and found widespread applications across fields like entertainment, education, and marketing. However, the adversarial v…