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Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise. To simplify this process, we introduce MatFuse, a unified approach that harnesses the generative power of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-18 Giuseppe Vecchio , Renato Sortino , Simone Palazzo , Concetto Spampinato

The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In this work, we harness these traits and present a unified…

Computer Vision and Pattern Recognition · Computer Science 2022-11-29 Minghui Hu , Chuanxia Zheng , Heliang Zheng , Tat-Jen Cham , Chaoyue Wang , Zuopeng Yang , Dacheng Tao , Ponnuthurai N. Suganthan

Training diffusion models for audiovisual sequences allows for a range of generation tasks by learning conditional distributions of various input-output combinations of the two modalities. Nevertheless, this strategy often requires training…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Gwanghyun Kim , Alonso Martinez , Yu-Chuan Su , Brendan Jou , José Lezama , Agrim Gupta , Lijun Yu , Lu Jiang , Aren Jansen , Jacob Walker , Krishna Somandepalli

Recently, significant advances have been made in 3D object generation. Building upon the generated geometry, current pipelines typically employ image diffusion models to generate multi-view RGB images, followed by UV texture reconstruction…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Mingqi Shao , Feng Xiong , Zhaoxu Sun , Mu Xu

Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when…

Computer Vision and Pattern Recognition · Computer Science 2023-11-03 Can Qin , Shu Zhang , Ning Yu , Yihao Feng , Xinyi Yang , Yingbo Zhou , Huan Wang , Juan Carlos Niebles , Caiming Xiong , Silvio Savarese , Stefano Ermon , Yun Fu , Ran Xu

Natural and expressive human motion generation is the holy grail of computer animation. It is a challenging task, due to the diversity of possible motion, human perceptual sensitivity to it, and the difficulty of accurately describing it.…

Computer Vision and Pattern Recognition · Computer Science 2022-10-04 Guy Tevet , Sigal Raab , Brian Gordon , Yonatan Shafir , Daniel Cohen-Or , Amit H. Bermano

Advancements in language foundation models have primarily fueled the recent surge in artificial intelligence. In contrast, generative learning of non-textual modalities, especially videos, significantly trails behind language modeling. This…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Lijun Yu

Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control along predefined trajectories. However, all these methods rely…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Quanhao Li , Zhen Xing , Rui Wang , Haidong Cao , Qi Dai , Daoguo Dong , Zuxuan Wu

Notable breakthroughs in unified understanding and generation modeling have led to remarkable advancements in image understanding, reasoning, production and editing, yet current foundational models predominantly focus on processing images,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Zhiyu Tan , Hao Yang , Luozheng Qin , Jia Gong , Mengping Yang , Hao Li

Conditional visual generation has witnessed remarkable progress with the advent of diffusion models (DMs), especially in tasks like control-to-image generation. However, challenges such as expensive computational cost, high inference…

Computer Vision and Pattern Recognition · Computer Science 2024-10-03 Xiang Li , Kai Qiu , Hao Chen , Jason Kuen , Zhe Lin , Rita Singh , Bhiksha Raj

In this paper, we introduce a novel task called language-guided joint audio-visual editing. Given an audio and image pair of a sounding event, this task aims at generating new audio-visual content by editing the given sounding event…

Computer Vision and Pattern Recognition · Computer Science 2024-11-12 Susan Liang , Chao Huang , Yapeng Tian , Anurag Kumar , Chenliang Xu

Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce MultiFoley, a model…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Ziyang Chen , Prem Seetharaman , Bryan Russell , Oriol Nieto , David Bourgin , Andrew Owens , Justin Salamon

While generative video models have achieved remarkable fidelity and consistency, applying these capabilities to video editing remains a complex challenge. Recent research has explored motion controllability as a means to enhance…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Ryan Burgert , Charles Herrmann , Forrester Cole , Michael S Ryoo , Neal Wadhwa , Andrey Voynov , Nataniel Ruiz

Video models have recently been applied with success to problems in content generation, novel view synthesis, and, more broadly, world simulation. Many applications in generation and transfer rely on conditioning these models, typically…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Edoardo A. Dominici , Thomas Deixelberger , Konstantinos Vardis , Markus Steinberger

Recent advances in text-to-video diffusion models have enabled the generation of high-quality videos conditioned on textual descriptions. However, most existing text-to-video models rely solely on textual conditions, lacking general…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Yuheng Chen , Teng Hu , Jiangning Zhang , Zhucun Xue , Ran Yi , Lizhuang Ma

Reconstructing 3D scenes and synthesizing novel views from sparse input views is a highly challenging task. Recent advances in video diffusion models have demonstrated strong temporal reasoning capabilities, making them a promising tool for…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Yuqi Zhang , Guanying Chen , Jiaxing Chen , Chuanyu Fu , Chuan Huang , Shuguang Cui

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Andreas Blattmann , Robin Rombach , Huan Ling , Tim Dockhorn , Seung Wook Kim , Sanja Fidler , Karsten Kreis

Despite recent advances in video generation, existing models still lack fine-grained controllability, especially for multi-subject customization with consistent identity and interaction. In this paper, we propose PolyVivid, a multi-subject…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Teng Hu , Zhentao Yu , Zhengguang Zhou , Jiangning Zhang , Yuan Zhou , Qinglin Lu , Ran Yi

Despite impressive advancements in diffusion-based video editing models in altering video attributes, there has been limited exploration into modifying motion information while preserving the original protagonist's appearance and…

Computer Vision and Pattern Recognition · Computer Science 2024-05-31 Shuyuan Tu , Qi Dai , Zihao Zhang , Sicheng Xie , Zhi-Qi Cheng , Chong Luo , Xintong Han , Zuxuan Wu , Yu-Gang Jiang

Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results,…

Computer Vision and Pattern Recognition · Computer Science 2024-11-05 Wei Cheng , Juncheng Mu , Xianfang Zeng , Xin Chen , Anqi Pang , Chi Zhang , Zhibin Wang , Bin Fu , Gang Yu , Ziwei Liu , Liang Pan
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