7 citations · 12 across the 10 of their papers we have counts for
10 papers
SeamGen: Artist-Aligned UV Seam Generation via Graph Flow Matching
Hao Xu, Yuqing Zhang, Yiqian Wu +5
UV seam placement is a critical yet labor-intensive step in 3D content creation, requiring artists to balance chart shape, seam concealment, and alignment with semantic and geometr…
GenLCA: 3D Diffusion for Full-Body Avatars from In-the-Wild Videos
Yiqian Wu, Rawal Khirodkar, Egor Zakharov +6
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…
EgoM2P: Egocentric Multimodal Multitask Pretraining
Gen Li, Yutong Chen, Yiqian Wu +3
Understanding multimodal signals in egocentric vision, such as RGB video, depth, camera poses, and gaze, is essential for applications in augmented reality, robotics, and human-com…
Text-based Animatable 3D Avatars with Morphable Model Alignment
Yiqian Wu, Malte Prinzler, Xiaogang Jin +1
The generation of high-quality, animatable 3D head avatars from text has enormous potential in content creation applications such as games, movies, and embodied virtual assistants.…
StyleTex: Style Image-Guided Texture Generation for 3D Models
Zhiyu Xie, Yuqing Zhang, Xiangjun Tang +4
Style-guided texture generation aims to generate a texture that is harmonious with both the style of the reference image and the geometry of the input mesh, given a reference style…
Decoupling Contact for Fine-Grained Motion Style Transfer
Xiangjun Tang, Linjun Wu, He Wang +7
Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style tr…
Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior
Yiqian Wu, Hao Xu, Xiangjun Tang +5
Existing neural rendering-based text-to-3D-portrait generation methods typically make use of human geometry prior and diffusion models to obtain guidance. However, relying solely o…
Enhancing the Authenticity of Rendered Portraits with Identity-Consistent Transfer Learning
Luyuan Wang, Yiqian Wu, Yongliang Yang +2
Despite rapid advances in computer graphics, creating high-quality photo-realistic virtual portraits is prohibitively expensive. Furthermore, the well-know ''uncanny valley'' effec…
3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses
Yiqian Wu, Hao Xu, Xiangjun Tang +3
3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-q…
LPFF: A Portrait Dataset for Face Generators Across Large Poses
Yiqian Wu, Jing Zhang, Hongbo Fu +1
The creation of 2D realistic facial images and 3D face shapes using generative networks has been a hot topic in recent years. Existing face generators exhibit exceptional performan…