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3D human generation is an important problem with a wide range of applications in computer vision and graphics. Despite recent progress in generative AI such as diffusion models or rendering methods like Neural Radiance Fields or Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2025-06-06 Maksym Ivashechkin , Oscar Mendez , Richard Bowden

Generating high-fidelity upper-body 3D avatars from one-shot input image remains a significant challenge. Current 3D avatar generation methods, which rely on large reconstruction models, are fast and capable of producing stable body…

Computer Vision and Pattern Recognition · Computer Science 2025-12-10 Fan Yang , Heyuan Li , Peihao Li , Weihao Yuan , Lingteng Qiu , Chaoyue Song , Cheng Chen , Yisheng He , Shifeng Zhang , Xiaoguang Han , Steven Hoi , Guosheng Lin

Leveraging pretrained 2D diffusion models and score distillation sampling (SDS), recent methods have shown promising results for text-to-3D avatar generation. However, generating high-quality 3D avatars capable of expressive animation…

Computer Vision and Pattern Recognition · Computer Science 2024-09-26 Yukun Huang , Jianan Wang , Ailing Zeng , Zheng-Jun Zha , Lei Zhang , Xihui Liu

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

Powered by large-scale text-to-image generation models, text-to-3D avatar generation has made promising progress. However, most methods fail to produce photorealistic results, limited by imprecise geometry and low-quality appearance.…

Computer Vision and Pattern Recognition · Computer Science 2023-12-27 Yuanyou Xu , Zongxin Yang , Yi Yang

While high fidelity and efficiency are central to the creation of digital head avatars, recent methods relying on 2D or 3D generative models often experience limitations such as shape distortion, expression inaccuracy, and identity…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Xiaochen Zhao , Jingxiang Sun , Lizhen Wang , Jinli Suo , Yebin Liu

We present GaussianAvatar, an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video. We start by introducing animatable 3D Gaussians to explicitly represent humans in various poses and…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Liangxiao Hu , Hongwen Zhang , Yuxiang Zhang , Boyao Zhou , Boning Liu , Shengping Zhang , Liqiang Nie

Generating and editing dynamic 3D head avatars are crucial tasks in virtual reality and film production. However, existing methods often suffer from facial distortions, inaccurate head movements, and limited fine-grained editing…

Graphics · Computer Science 2024-11-26 Yangyang Qian , Yuan Sun , Yu Guo

Creating high-fidelity 3D human head avatars is crucial for applications in VR/AR, digital human, and film production. Recent advances have leveraged morphable face models to generate animated head avatars from easily accessible data,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-24 Yuelang Xu , Zhaoqi Su , Qingyao Wu , Yebin Liu

We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones. Photorealistic 3D head avatar reconstruction from such recordings is challenging due to…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Jiapeng Tang , Davide Davoli , Tobias Kirschstein , Liam Schoneveld , Matthias Niessner

We present GenLCA, a diffusion-based generative model for generating and editing photorealistic full-body avatars from text and image inputs. The generated avatars are faithful to the inputs, while supporting high-fidelity facial and…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 Yiqian Wu , Rawal Khirodkar , Egor Zakharov , Timur Bagautdinov , Lei Xiao , Zhaoen Su , Shunsuke Saito , Xiaogang Jin , Junxuan Li

Fast generation of high-quality 3D digital humans is important to a vast number of applications ranging from entertainment to professional concerns. Recent advances in differentiable rendering have enabled the training of 3D generative…

Computer Vision and Pattern Recognition · Computer Science 2023-07-25 Zhangyang Xiong , Di Kang , Derong Jin , Weikai Chen , Linchao Bao , Shuguang Cui , Xiaoguang Han

Creating high-fidelity, animatable 3D talking heads is crucial for immersive applications, yet often hindered by the prevalence of low-quality image or video sources, which yield poor 3D reconstructions. In this paper, we introduce…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Ding-Jiun Huang , Yuanhao Wang , Shao-Ji Yuan , Albert Mosella-Montoro , Francisco Vicente Carrasco , Cheng Zhang , Fernando De la Torre

In this paper, we introduce GaussianMotion, a novel human rendering model that generates fully animatable scenes aligned with textual descriptions using Gaussian Splatting. Although existing methods achieve reasonable text-to-3D generation…

Computer Vision and Pattern Recognition · Computer Science 2025-02-18 Gyumin Shim , Sangmin Lee , Jaegul Choo

This work focuses on open-domain 4D avatarization, with the purpose of creating a 4D avatar from a portrait image in an arbitrary style. We select parametric triplanes as the intermediate 4D representation and propose a practical training…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Hongyu Liu , Xuan Wang , Ziyu Wan , Yue Ma , Jingye Chen , Yanbo Fan , Yujun Shen , Yibing Song , Qifeng Chen

In recent years, diffusion models have revolutionized visual generation, outperforming traditional frameworks like Generative Adversarial Networks (GANs). However, generating images of humans with realistic semantic parts, such as hands and…

Computer Vision and Pattern Recognition · Computer Science 2024-11-14 Benzhi Wang , Jingkai Zhou , Jingqi Bai , Yang Yang , Weihua Chen , Fan Wang , Zhen Lei

We present 3DHumanGAN, a 3D-aware generative adversarial network that synthesizes photorealistic images of full-body humans with consistent appearances under different view-angles and body-poses. To tackle the representational and…

Computer Vision and Pattern Recognition · Computer Science 2023-09-26 Zhuoqian Yang , Shikai Li , Wayne Wu , Bo Dai

As the digital and physical worlds become more intertwined, there has been a lot of interest in digital avatars that closely resemble their real-world counterparts. Current digitization methods used in 3D production pipelines require costly…

Computer Vision and Pattern Recognition · Computer Science 2025-03-20 Yifan Wang , Ivan Molodetskikh , Ondrej Texler , Dimitar Dinev

Detailed and photorealistic 3D human modeling is essential for various applications and has seen tremendous progress. However, full-body reconstruction from a monocular RGB image remains challenging due to the ill-posed nature of the…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Peng Li , Wangguandong Zheng , Yuan Liu , Tao Yu , Yangguang Li , Xingqun Qi , Xiaowei Chi , Siyu Xia , Yan-Pei Cao , Wei Xue , Wenhan Luo , Yike Guo

We present InstructHumans, a novel framework for instruction-driven {animatable} 3D human texture editing. Existing text-based 3D editing methods often directly apply Score Distillation Sampling (SDS). SDS, designed for generation tasks,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-06 Jiayin Zhu , Linlin Yang , Angela Yao
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