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Existing hands datasets are largely short-range and the interaction is weak due to the self-occlusion and self-similarity of hands, which can not yet fit the need for interacting hands motion generation. To rescue the data scarcity, we…

Computer Vision and Pattern Recognition · Computer Science 2025-04-24 Pei Lin , Sihang Xu , Hongdi Yang , Yiran Liu , Xin Chen , Jingya Wang , Jingyi Yu , Lan Xu

Interactive Generative Video (IGV) has emerged as a crucial technology in response to the growing demand for high-quality, interactive video content across various domains. In this paper, we define IGV as a technology that combines…

Computer Vision and Pattern Recognition · Computer Science 2025-05-01 Jiwen Yu , Yiran Qin , Haoxuan Che , Quande Liu , Xintao Wang , Pengfei Wan , Di Zhang , Kun Gai , Hao Chen , Xihui Liu

We propose X-Portrait, an innovative conditional diffusion model tailored for generating expressive and temporally coherent portrait animation. Specifically, given a single portrait as appearance reference, we aim to animate it with motion…

Computer Vision and Pattern Recognition · Computer Science 2024-07-29 You Xie , Hongyi Xu , Guoxian Song , Chao Wang , Yichun Shi , Linjie Luo

Music generation has attracted growing interest with the advancement of deep generative models. However, generating music conditioned on textual descriptions, known as text-to-music, remains challenging due to the complexity of musical…

Sound · Computer Science 2025-05-08 Peike Li , Boyu Chen , Yao Yao , Yikai Wang , Allen Wang , Alex Wang

Current diffusion-based text-to-video methods are limited to producing short video clips of a single shot and lack the capability to generate multi-shot videos with discrete transitions where the same character performs distinct activities…

Computer Vision and Pattern Recognition · Computer Science 2025-05-13 Ozgur Kara , Krishna Kumar Singh , Feng Liu , Duygu Ceylan , James M. Rehg , Tobias Hinz

Recent advances in diffusion models have significantly improved audio-driven human video generation, surpassing traditional methods in both quality and controllability. However, existing approaches still face challenges in lip-sync…

Computer Vision and Pattern Recognition · Computer Science 2025-11-19 Xingpei Ma , Shenneng Huang , Jiaran Cai , Yuansheng Guan , Shen Zheng , Hanfeng Zhao , Qiang Zhang , Shunsi Zhang

Recent advances in video generation have spurred the development of world models capable of simulating 3D-consistent environments and interactions with static objects. However, a significant limitation remains in their ability to model…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Anmol Agarwal , Pranay Meshram , Sumer Singh , Saurav Suman , Andrew Lapp , Shahbuland Matiana , Louis Castricato , Spencer Frazier

Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Traditional methods like morphing often lack artistic appeal and require specialized skills, limiting their effectiveness.…

Computer Vision and Pattern Recognition · Computer Science 2024-08-27 Rui Zhang , Yaosen Chen , Yuegen Liu , Wei Wang , Xuming Wen , Hongxia Wang

Realistic and diverse multi-agent driving scenes are crucial for evaluating autonomous vehicles, but safety-critical events which are essential for this task are rare and underrepresented in driving datasets. Data-driven scene generation…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Shihao Li , Naisheng Ye , Tianyu Li , Kashyap Chitta , Tuo An , Peng Su , Boyang Wang , Haiou Liu , Chen Lv , Hongyang Li

Collecting multi-view driving scenario videos to enhance the performance of 3D visual perception tasks presents significant challenges and incurs substantial costs, making generative models for realistic data an appealing alternative. Yet,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Junpeng Jiang , Gangyi Hong , Miao Zhang , Hengtong Hu , Kun Zhan , Rui Shao , Liqiang Nie

Modern video generative models based on diffusion models can produce very realistic clips, but they are computationally inefficient, often requiring minutes of GPU time for just a few seconds of video. This inefficiency poses a critical…

Computer Vision and Pattern Recognition · Computer Science 2026-01-15 Jieying Chen , Jeffrey Hu , Joan Lasenby , Ayush Tewari

Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in…

Computer Vision and Pattern Recognition · Computer Science 2022-12-02 Kangfu Mei , Vishal M. Patel

Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments. In this paper, we focus on shared world modeling, where a model generates multiple videos from a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Fan Wu , Jiacheng Wei , Ruibo Li , Yi Xu , Junyou Li , Deheng Ye , Guosheng Lin

Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However,…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Li Hu , Xin Gao , Peng Zhang , Ke Sun , Bang Zhang , Liefeng Bo

This paper introduces OmniMotion-X, a versatile multimodal framework for whole-body human motion generation, leveraging an autoregressive diffusion transformer in a unified sequence-to-sequence manner. OmniMotion-X efficiently supports…

Computer Vision and Pattern Recognition · Computer Science 2025-10-23 Guowei Xu , Yuxuan Bian , Ailing Zeng , Mingyi Shi , Shaoli Huang , Wen Li , Lixin Duan , Qiang Xu

While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Hyeonho Jeong , Chun-Hao Paul Huang , Jong Chul Ye , Niloy Mitra , Duygu Ceylan

We present a method for generating video sequences with coherent motion between a pair of input key frames. We adapt a pretrained large-scale image-to-video diffusion model (originally trained to generate videos moving forward in time from…

Computer Vision and Pattern Recognition · Computer Science 2025-02-13 Xiaojuan Wang , Boyang Zhou , Brian Curless , Ira Kemelmacher-Shlizerman , Aleksander Holynski , Steven M. Seitz

The field of image synthesis has made tremendous strides forward in the last years. Besides defining the desired output image with text-prompts, an intuitive approach is to additionally use spatial guidance in form of an image, such as a…

Computer Vision and Pattern Recognition · Computer Science 2024-08-13 Denis Zavadski , Johann-Friedrich Feiden , Carsten Rother

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Levon Khachatryan , Andranik Movsisyan , Vahram Tadevosyan , Roberto Henschel , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

This study investigates human-computer interface generation based on diffusion models to overcome the limitations of traditional template-based design and fixed rule-driven methods. It first analyzes the key challenges of interface…

Human-Computer Interaction · Computer Science 2026-01-13 Rui Liu , Liuqingqing Yang , Runsheng Zhang , Shixiao Wang
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