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Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value. Many researchers have been…

Computer Vision and Pattern Recognition · Computer Science 2024-05-27 Zhen Jia , Zhang Zhang , Liang Wang , Tieniu Tan

Generating higher-resolution human-centric scenes with details and controls remains a challenge for existing text-to-image diffusion models. This challenge stems from limited training image size, text encoder capacity (limited tokens), and…

Computer Vision and Pattern Recognition · Computer Science 2024-04-09 Gwanghyun Kim , Hayeon Kim , Hoigi Seo , Dong Un Kang , Se Young Chun

In this paper, we present a diffusion model-based framework for animating people from a single image for a given target 3D motion sequence. Our approach has two core components: a) learning priors about invisible parts of the human body and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-23 Boyi Li , Junming Chen , Jathushan Rajasegaran , Yossi Gandelsman , Alexei A. Efros , Jitendra Malik

Layer compositing is one of the most popular image editing workflows among both amateurs and professionals. Motivated by the success of diffusion models, we explore layer compositing from a layered image generation perspective. Instead of…

Computer Vision and Pattern Recognition · Computer Science 2023-07-20 Xinyang Zhang , Wentian Zhao , Xin Lu , Jeff Chien

In layout-to-image (L2I) synthesis, controlled complex scenes are generated from coarse information like bounding boxes. Such a task is exciting to many downstream applications because the input layouts offer strong guidance to the…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Ruyu Wang , Xuefeng Hou , Sabrina Schmedding , Marco F. Huber

The generation of images of realistic looking, readable handwritten text is a challenging task which is referred to as handwritten text generation (HTG). Given a string and examples from a writer, the goal is to synthesize an image…

Computer Vision and Pattern Recognition · Computer Science 2024-12-23 Kai Brandenbusch

Human image generation is a very challenging task since it is affected by many factors. Many human image generation methods focus on generating human images conditioned on a given pose, while the generated backgrounds are often blurred.In…

Computer Vision and Pattern Recognition · Computer Science 2018-11-27 Dong Liang , Rui Wang , Xiaowei Tian , Cong Zou

Generating multi-view human images from a single view is a complex and significant challenge. Although recent advancements in multi-view object generation have shown impressive results with diffusion models, novel view synthesis for humans…

Computer Vision and Pattern Recognition · Computer Science 2024-12-05 Yu Feng , Shunsi Zhang , Jian Shu , Hanfeng Zhao , Guoliang Pang , Chi Zhang , Hao Wang

Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sensitive areas such as medical imaging. Medical images feature…

Computer Vision and Pattern Recognition · Computer Science 2025-10-09 Sarah Cechnicka , Matthew Baugh , Weitong Zhang , Mischa Dombrowski , Zhe Li , Johannes C. Paetzold , Candice Roufosse , Bernhard Kainz

Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL-E 2 tend to generate human images with…

Computer Vision and Pattern Recognition · Computer Science 2024-03-18 Xian Liu , Jian Ren , Aliaksandr Siarohin , Ivan Skorokhodov , Yanyu Li , Dahua Lin , Xihui Liu , Ziwei Liu , Sergey Tulyakov

This survey reviews the progress of diffusion models in generating images from text, ~\textit{i.e.} text-to-image diffusion models. As a self-contained work, this survey starts with a brief introduction of how diffusion models work for…

Computer Vision and Pattern Recognition · Computer Science 2024-11-11 Chenshuang Zhang , Chaoning Zhang , Mengchun Zhang , In So Kweon , Junmo Kim

Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic…

Computer Vision and Pattern Recognition · Computer Science 2023-08-01 Michał Stypułkowski , Konstantinos Vougioukas , Sen He , Maciej Zięba , Stavros Petridis , Maja Pantic

In spite of recent progress, image diffusion models still produce artifacts. A common solution is to leverage the feedback provided by quality assessment systems or human annotators to optimize the model, where images are generally rated in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Yiyang Wang , Xi Chen , Xiaogang Xu , Sihui Ji , Yu Liu , Yujun Shen , Hengshuang Zhao

With deeper exploration of diffusion model, developments in the field of image generation have triggered a boom in image creation. As the quality of base-model generated images continues to improve, so does the demand for further…

Graphics · Computer Science 2025-04-21 Jia Wang , Jie Hu , Xiaoqi Ma , Hanghang Ma , Xiaoming Wei , Enhua Wu

While diffusion models have shown great potential in portrait generation, generating expressive, coherent, and controllable cinematic portrait videos remains a significant challenge. Existing intermediate signals for portrait generation,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Junyi Wang , Yudong Guo , Boyang Guo , Shengming Yang , Juyong Zhang

Diffusion models have demonstrated remarkable synthesis quality and diversity in generating co-speech gestures. However, the computationally intensive sampling steps associated with diffusion models hinder their practicality in real-world…

Graphics · Computer Science 2025-03-24 Yongkang Cheng , Shaoli Huang , Xuelin Chen , Jifeng Ning , Mingming Gong

Audio-driven cospeech video generation typically involves two stages: speech-to-gesture and gesture-to-video. While significant advances have been made in speech-to-gesture generation, synthesizing natural expressions and gestures remains…

Computer Vision and Pattern Recognition · Computer Science 2025-04-14 Renda Li , Xiaohua Qi , Qiang Ling , Jun Yu , Ziyi Chen , Peng Chang , Mei HanJing Xiao

Generative models have advanced significantly in realistic image synthesis, with diffusion models excelling in quality and stability. Recent multi-view diffusion models improve 3D-aware street view generation, but they struggle to produce…

Computer Vision and Pattern Recognition · Computer Science 2026-02-13 Ji Li , Zhiwei Li , Shihao Li , Zhenjiang Yu , Boyang Wang , Haiou Liu

While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when users simultaneously specify text prompts, subject references,…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Yusuf Dalva , Guocheng Gordon Qian , Maya Goldenberg , Tsai-Shien Chen , Kfir Aberman , Sergey Tulyakov , Pinar Yanardag , Kuan-Chieh Jackson Wang

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein
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