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Video generation has achieved impressive quality, but it still suffers from artifacts such as temporal inconsistency and violation of physical laws. Leveraging 3D scenes can fundamentally resolve these issues by providing precise control…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Zhaofang Qian , Abolfazl Sharifi , Tucker Carroll , Ser-Nam Lim

Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck for large-scale deployment. To address this challenge, some works use LLMs and VLMs for…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Hao Shao , Letian Wang , Yang Zhou , Yuxuan Hu , Zhuofan Zong , Steven L. Waslander , Wei Zhan , Hongsheng Li

Large Language Model (LLM)-based agents have shown promise in procedural tasks, but the potential of multimodal instructions augmented by texts and videos to assist users remains under-explored. To address this gap, we propose the Visually…

Computer Vision and Pattern Recognition · Computer Science 2024-12-17 Muhammet Furkan Ilaslan , Ali Koksal , Kevin Qinhong Lin , Burak Satar , Mike Zheng Shou , Qianli Xu

In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current models still face misalignment issues (e.g., problematic spatial…

Computer Vision and Pattern Recognition · Computer Science 2023-08-15 Leigang Qu , Shengqiong Wu , Hao Fei , Liqiang Nie , Tat-Seng Chua

Building on the advances of language models, Large Multimodal Models (LMMs) have contributed significant improvements in video understanding. While the current video LMMs utilize advanced Large Language Models (LLMs), they rely on either…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Muhammad Maaz , Hanoona Rasheed , Salman Khan , Fahad Khan

Significant progress has been made in text-to-video generation through the use of powerful generative models and large-scale internet data. However, substantial challenges remain in precisely controlling individual concepts within the…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Hanxin Zhu , Tianyu He , Anni Tang , Junliang Guo , Zhibo Chen , Jiang Bian

Recent advances in diffusion-based text-to-video (T2V) models have demonstrated remarkable progress, but these models still face challenges in generating videos with multiple objects. Most models struggle with accurately capturing complex…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Aimon Rahman , Jiang Liu , Ze Wang , Ximeng Sun , Jialian Wu , Xiaodong Yu , Yusheng Su , Vishal M. Patel , Zicheng Liu , Emad Barsoum

Synthesizing motion-rich and temporally consistent videos remains a challenge in artificial intelligence, especially when dealing with extended durations. Existing text-to-video (T2V) models commonly employ spatial cross-attention for text…

Computer Vision and Pattern Recognition · Computer Science 2025-08-18 Jiasong Feng , Ao Ma , Jing Wang , Ke Cao , Zhanjie Zhang

Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Roberto Henschel , Levon Khachatryan , Hayk Poghosyan , Daniil Hayrapetyan , Vahram Tadevosyan , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

With the exponential growth of video data, there is an urgent need for automated technology to analyze and comprehend video content. However, existing video understanding models are often task-specific and lack a comprehensive capability of…

Computer Vision and Pattern Recognition · Computer Science 2023-05-24 Guo Chen , Yin-Dong Zheng , Jiahao Wang , Jilan Xu , Yifei Huang , Junting Pan , Yi Wang , Yali Wang , Yu Qiao , Tong Lu , Limin Wang

Text-to-video (T2V) synthesis has gained increasing attention in the community, in which the recently emerged diffusion models (DMs) have promisingly shown stronger performance than the past approaches. While existing state-of-the-art DMs…

Artificial Intelligence · Computer Science 2024-03-20 Hao Fei , Shengqiong Wu , Wei Ji , Hanwang Zhang , Tat-Seng Chua

Recent advances in video generation can produce realistic, minute-long single-shot videos with scalable diffusion transformers. However, real-world narrative videos require multi-shot scenes with visual and dynamic consistency across shots.…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Yuwei Guo , Ceyuan Yang , Ziyan Yang , Zhibei Ma , Zhijie Lin , Zhenheng Yang , Dahua Lin , Lu Jiang

Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality videos from text prompts or single images, personalized…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Yufan Deng , Xun Guo , Yizhi Wang , Jacob Zhiyuan Fang , Angtian Wang , Shenghai Yuan , Yiding Yang , Bo Liu , Haibin Huang , Chongyang Ma

In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Previous…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 Taeryung Lee , Fabien Baradel , Thomas Lucas , Kyoung Mu Lee , Gregory Rogez

Creating interactive scenes often involves complex programming tasks. Although large language models (LLMs) like ChatGPT can generate code from natural language, their output is often error-prone, particularly when scripting interactions…

Human-Computer Interaction · Computer Science 2025-02-10 Hui Ye , Chufeng Xiao , Jiaye Leng , Pengfei Xu , Hongbo Fu

In this paper, we introduce DirectorLLM, a novel video generation model that employs a large language model (LLM) to orchestrate human poses within videos. As foundational text-to-video models rapidly evolve, the demand for high-quality…

Computer Vision and Pattern Recognition · Computer Science 2025-09-09 Kunpeng Song , Tingbo Hou , Zecheng He , Haoyu Ma , Jialiang Wang , Animesh Sinha , Sam Tsai , Yaqiao Luo , Xiaoliang Dai , Li Chen , Xide Xia , Peizhao Zhang , Peter Vajda , Ahmed Elgammal , Felix Juefei-Xu

Layout generation is the keystone in achieving automated graphic design, requiring arranging the position and size of various multi-modal design elements in a visually pleasing and constraint-following manner. Previous approaches are either…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Tao Yang , Yingmin Luo , Zhongang Qi , Yang Wu , Ying Shan , Chang Wen Chen

The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system. However, generating long-duration, surround-view consistent driving videos…

Computer Vision and Pattern Recognition · Computer Science 2025-03-07 Rui Chen , Zehuan Wu , Yichen Liu , Yuxin Guo , Jingcheng Ni , Haifeng Xia , Siyu Xia

While recent generative models advance pixel-space video synthesis, they remain limited in producing professional educational videos, which demand disciplinary knowledge, precise visual structures, and coherent transitions, limiting their…

Computer Vision and Pattern Recognition · Computer Science 2025-10-02 Yanzhe Chen , Kevin Qinghong Lin , Mike Zheng Shou

Current text-to-image generation models often struggle to follow textual instructions, especially the ones requiring spatial reasoning. On the other hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable precision in…

Computer Vision and Pattern Recognition · Computer Science 2023-05-31 Tianjun Zhang , Yi Zhang , Vibhav Vineet , Neel Joshi , Xin Wang