22 citations · 46 across the 12 of their papers we have counts for
13 papers
Stable Velocity: A Variance Perspective on Flow Matching
Donglin Yang, Yongxing Zhang, Xin Yu +5
While flow matching is elegant, its reliance on single-sample conditional velocities leads to high-variance training targets that destabilize optimization and slow convergence. By…
Self-Evaluation Unlocks Any-Step Text-to-Image Generation
Xin Yu, Xiaojuan Qi, Zhengqi Li +6
We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data simi…
LaFiTe: A Generative Latent Field for 3D Native Texturing
Chia-Hao Chen, Zi-Xin Zou, Yan-Pei Cao +6
Generating high-fidelity, seamless textures directly on 3D surfaces, what we term 3D-native texturing, remains a fundamental open challenge, with the potential to overcome long-sta…
SeqTex: Generate Mesh Textures in Video Sequence
Ze Yuan, Xin Yu, Yangtian Sun +4
Training native 3D texture generative models remains a fundamental yet challenging problem, largely due to the limited availability of large-scale, high-quality 3D texture datasets…
UniGeo: Taming Video Diffusion for Unified Consistent Geometry Estimation
Yang-Tian Sun, Xin Yu, Zehuan Huang +5
Recently, methods leveraging diffusion model priors to assist monocular geometric estimation (e.g., depth and normal) have gained significant attention due to their strong generali…
ObjectMover: Generative Object Movement with Video Prior
Xin Yu, Tianyu Wang, Soo Ye Kim +5
Simple as it seems, moving an object to another location within an image is, in fact, a challenging image-editing task that requires re-harmonizing the lighting, adjusting the pose…
TEXGen: a Generative Diffusion Model for Mesh Textures
Xin Yu, Ze Yuan, Yuan-Chen Guo +6
While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets…
EscherNet: A Generative Model for Scalable View Synthesis
Xin Kong, Shikun Liu, Xiaoyang Lyu +3
We introduce EscherNet, a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with a specialised camera p…
Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting
Ziyi Yang, Xinyu Gao, Yangtian Sun +6
The recent advancements in 3D Gaussian splatting (3D-GS) have not only facilitated real-time rendering through modern GPU rasterization pipelines but have also attained state-of-th…
GO-NeRF: Generating Objects in Neural Radiance Fields for Virtual Reality Content Creation
Peng Dai, Feitong Tan, Xin Yu +3
Virtual environments (VEs) are pivotal for virtual, augmented, and mixed reality systems. Despite advances in 3D generation and reconstruction, the direct creation of 3D objects wi…
SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes
Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang +3
Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent…
UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation
Zexiang Liu, Yangguang Li, Youtian Lin +7
Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D…
Text-to-3D with Classifier Score Distillation
Xin Yu, Yuan-Chen Guo, Yangguang Li +3
Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models. Wh…