DiVE: DiT-based Video Generation with Enhanced Control
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Li Auto Inc.（理想汽车）；Tsinghua University（清华大学）；Westlake University（西湖大学）；National University of Singapore（新加坡国立大学）
Junpeng Jiang, Gangyi Hong, Lijun Zhou, Enhui Ma, Hengtong Hu, Xia Zhou, Jie Xiang, Fan Liu, Kaicheng Yu, Haiyang Sun, Kun Zhan, Peng Jia, Miao Zhang
Generating high-fidelity, temporally consistent videos in autonomous driving scenarios faces a significant challenge, e.g. problematic maneuvers in corner cases. Despite recent video generation works are proposed to tackcle the mentioned problem, i.e. models built on top of Diffusion Transformers (DiT), works are still missing which are targeted on exploring the potential for multi-view videos generation scenarios. Noticeably, we propose the first DiT-based framework specifically designed for generating temporally and multi-view consistent videos which precisely match the given bird's-eye view layouts control. Specifically, the proposed framework leverages a parameter-free spatial view-inflated attention mechanism to guarantee the cross-view consistency, where joint cross-attention modules and ControlNet-Transformer are integrated to further improve the precision of control. To demonstrate our advantages, we extensively investigate the qualitative comparisons on nuScenes dataset, particularly in some most challenging corner cases. In summary, the effectiveness of our proposed method in producing long, controllable, and highly consistent videos under difficult conditions is proven to be effective.
DiT as Real-Time Rerenderer: Streaming Video Stylization with Autoregressive Diffusion Transformer
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；XMax.AI Ltd.（XMax人工智能有限公司）；Nanyang Technological University（南洋理工大学）
Beyond Text Conditioning: A Systematic Study of MLLM-DiT Fusion for Video Generation
Chinese Academy of Sciences（中国科学院）；Microsoft Research（微软研究院）；Sun Yat-sen University（中山大学）；Zhejiang University（浙江大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Xi’an Jiaotong University（西安交通大学）
Cosh-DiT: Co-Speech Gesture Video Synthesis via Hybrid Audio-Visual Diffusion Transformers
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Baidu（百度）；Tokyo Institute of Technology（东京工业大学）；Tsinghua University（清华大学）；University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）
LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation
MMLab, CUHK（香港中文大学多媒体实验室）；GVC Lab, Great Bay University（大湾区大学GVC实验室）；ARC Lab, Tencent PCG（腾讯平台与内容事业群ARC实验室）；Tencent AI Lab（腾讯AI实验室）；SHIAE, CUHK（香港中文大学SHIAE实验室）
Once Is Enough: Lightweight DiT-Based Video Virtual Try-On via One-Time Garment Appearance Injection
School of computer science, Shanghai key laboratory of data science, Fudan University, China（计算机学院、数据科学重点实验室、复旦大学）；Tencent Youtu Lab, China（腾讯优图实验室）；Shanghai Ocean University, China（上海海洋大学）
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；SKL-IOTSC, CIS, University of Macau（澳门大学SKL-IOTSC、CIS）；Huazhong University of Science and Technology（华中科技大学）；The Chinese University of Hong Kong（香港中文大学）
UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学图像处理与智能控制重点实验室，人工智能与自动化学院）；Alibaba Group（阿里巴巴集团）；Tsinghua University（清华大学）
BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China（人工智能学院，北京师范大学，北京）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China（人工智能与未来网络研究院，北京师范大学，珠海）
MaterialPicker: Multi-Modal DiT-Based Material Generation
State Key Lab of CAD\&CG, Zhejiang University（浙江大学CAD与CG国家重点实验室）；Adobe Research（Adobe研究）；State Key Lab of CAD\&CG, Zhejiang University and ZJU-FaceUnity Joint Lab of Intelligent Graphics（浙江大学CAD与CG国家重点实验室）；ZJU-FaceUnity Joint Lab of Intelligent Graphics（浙大-面 unity 智能图形联合实验室）
本文提出 MaterialPicker，通过微调基于 DiT 的视频生成器，将材质贴图视为视频帧，支持文本与照片多模态输入，可从变形、倾斜或遮挡的材质照片中生成更多样、校正更好的高质量材质。
