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Related papers: Sketch-based Fluid Video Generation Using Motion-G…

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Sketch animations offer a powerful medium for visual storytelling, from simple flip-book doodles to professional studio productions. While traditional animation requires teams of skilled artists to draw key frames and in-between frames,…

Graphics · Computer Science 2024-11-19 Hmrishav Bandyopadhyay , Yi-Zhe Song

Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial…

Computer Vision and Pattern Recognition · Computer Science 2023-03-31 Sihyun Yu , Kihyuk Sohn , Subin Kim , Jinwoo Shin

Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts. However, extending these techniques to video generation remains challenging, with existing text-to-video…

Computer Vision and Pattern Recognition · Computer Science 2024-08-13 Weifeng Chen , Yatai Ji , Jie Wu , Hefeng Wu , Pan Xie , Jiashi Li , Xin Xia , Xuefeng Xiao , Liang Lin

A recent frontier in computer vision has been the task of 3D video generation, which consists of generating a time-varying 3D representation of a scene. To generate dynamic 3D scenes, current methods explicitly model 3D temporal dynamics by…

Computer Vision and Pattern Recognition · Computer Science 2024-08-01 Rishab Parthasarathy , Zachary Ankner , Aaron Gokaslan

The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent space is useful for realistic video generation. However, the generated motion in the video is usually not semantically meaningful due to the difficulty of…

Computer Vision and Pattern Recognition · Computer Science 2022-10-24 Seung Hyun Lee , Gyeongrok Oh , Wonmin Byeon , Chanyoung Kim , Won Jeong Ryoo , Sang Ho Yoon , Hyunjun Cho , Jihyun Bae , Jinkyu Kim , Sangpil Kim

3D scene generation conditioned on text prompts has significantly progressed due to the development of 2D diffusion generation models. However, the textual description of 3D scenes is inherently inaccurate and lacks fine-grained control…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Minglin Chen , Longguang Wang , Sheng Ao , Ye Zhang , Kai Xu , Yulan Guo

With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number…

Computer Vision and Pattern Recognition · Computer Science 2023-10-18 Ruiqi Wu , Liangyu Chen , Tong Yang , Chunle Guo , Chongyi Li , Xiangyu Zhang

Although humans have the innate ability to imagine multiple possible actions from videos, it remains an extraordinary challenge for computers due to the intricate camera movements and montages. Most existing motion generation methods…

Computer Vision and Pattern Recognition · Computer Science 2024-08-14 Liangdong Qiu , Chengxing Yu , Yanran Li , Zhao Wang , Haibin Huang , Chongyang Ma , Di Zhang , Pengfei Wan , Xiaoguang Han

Text-to-motion generation is driven by learning motion representations for semantic alignment with language. Existing methods rely on either continuous or discrete motion representations. However, continuous representations entangle…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Dawei Guan , Di Yang , Chengjie Jin , Jiangtao Wang

Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Ying Shen , Jerry Xiong , Tianjiao Yu , Ismini Lourentzou

Unbounded 3D world generation is emerging as a foundational task for scene modeling in computer vision, graphics, and robotics. In this work, we present WorldFlow3D, a novel method capable of generating unbounded 3D worlds. Building upon a…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Amogh Joshi , Julian Ost , Felix Heide

Latent diffusion models (LDMs) dominate high-quality image generation, yet integrating representation learning with generative modeling remains a challenge. We introduce a novel generative image modeling framework that seamlessly bridges…

Computer Vision and Pattern Recognition · Computer Science 2026-01-23 Theodoros Kouzelis , Efstathios Karypidis , Ioannis Kakogeorgiou , Spyros Gidaris , Nikos Komodakis

Diffusion models have recently demonstrated their effectiveness in generating extremely high-quality images and are now utilized in a wide range of applications, including automatic sketch colorization. Although many methods have been…

Computer Vision and Pattern Recognition · Computer Science 2024-07-04 Dingkun Yan , Liang Yuan , Erwin Wu , Yuma Nishioka , Issei Fujishiro , Suguru Saito

Diffusion probabilistic models have achieved remarkable success in text guided image generation. However, generating 3D shapes is still challenging due to the lack of sufficient data containing 3D models along with their descriptions.…

Computer Vision and Pattern Recognition · Computer Science 2023-08-08 Zijie Wu , Yaonan Wang , Mingtao Feng , He Xie , Ajmal Mian

To achieve realistic immersion in landscape images, fluids such as water and clouds need to move within the image while revealing new scenes from various camera perspectives. Recently, a field called dynamic scene video has emerged, which…

Computer Vision and Pattern Recognition · Computer Science 2025-04-09 In-Hwan Jin , Haesoo Choo , Seong-Hun Jeong , Heemoon Park , Junghwan Kim , Oh-joon Kwon , Kyeongbo Kong

Text-to-image diffusion models have demonstrated tremendous success in synthesizing visually stunning images given textual instructions. Despite remarkable progress in creating high-fidelity visuals, text-to-image models can still struggle…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Taewook Kim , Ze Wang , Zhengyuan Yang , Jiang Wang , Lijuan Wang , Zicheng Liu , Qiang Qiu

Video inpainting has been challenged by complex scenarios like large movements and low-light conditions. Current methods, including emerging diffusion models, face limitations in quality and efficiency. This paper introduces the Flow-Guided…

Computer Vision and Pattern Recognition · Computer Science 2025-01-24 Bohai Gu , Yongsheng Yu , Heng Fan , Libo Zhang

The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence…

Artificial Intelligence · Computer Science 2026-02-09 Jingtong Yue , Ziqi Huang , Zhaoxi Chen , Xintao Wang , Pengfei Wan , Ziwei Liu

We propose a method for scene-level sketch-to-photo synthesis with text guidance. Although object-level sketch-to-photo synthesis has been widely studied, whole-scene synthesis is still challenging without reference photos that adequately…

Computer Vision and Pattern Recognition · Computer Science 2023-02-15 AprilPyone MaungMaung , Makoto Shing , Kentaro Mitsui , Kei Sawada , Fumio Okura

We propose DriveAnyMesh, a method for driving mesh guided by monocular video. Current 4D generation techniques encounter challenges with modern rendering engines. Implicit methods have low rendering efficiency and are unfriendly to…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Yahao Shi , Yang Liu , Yanmin Wu , Xing Liu , Chen Zhao , Jie Luo , Bin Zhou
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