3 citations · 10 across the 14 of their papers we have counts for
14 papers
Strips as Tokens: Artist Mesh Generation with Native UV Segmentation
Rui Xu, Dafei Qin, Kaichun Qiao +8
Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token ordering strategies employed by e…
MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly
Rui Xu, Tianyang Xue, Qiujie Dong +9
Scaling artist-designed meshes to high triangle numbers remains challenging for autoregressive generative models. Existing transformer-based methods suffer from long-sequence bottl…
PDT: Point Distribution Transformation with Diffusion Models
Jionghao Wang, Cheng Lin, Yuan Liu +7
Point-based representations have consistently played a vital role in geometric data structures. Most point cloud learning and processing methods typically leverage the unordered an…
CrossGen: Learning and Generating Cross Fields for Quad Meshing
Qiujie Dong, Jiepeng Wang, Rui Xu +10
Cross fields play a critical role in various geometry processing tasks, especially for quad mesh generation. Existing methods for cross field generation often struggle to balance c…
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning
Yuming Feng, Zhiyang Dou, Ling-Hao Chen +7
Modeling temporal characteristics and the non-stationary dynamics of body movement plays a significant role in predicting human future motions. However, it is challenging to captur…
DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image
Qingxuan Wu, Zhiyang Dou, Sirui Xu +11
Reconstructing 3D hand-face interactions with deformations from a single image is a challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges stem…
Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images
Xiaoxiao Long, Yuhang Zheng, Yupeng Zheng +6
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric…
Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization
Zimeng Wang, Zhiyang Dou, Rui Xu +7
We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of…
DiffusionPhase: Motion Diffusion in Frequency Domain
Weilin Wan, Yiming Huang, Shutong Wu +4
In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques…
Disentangled Clothed Avatar Generation from Text Descriptions
Jionghao Wang, Yuan Liu, Zhiyang Dou +7
In this paper, we introduce a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated ava…
EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation
Wenyang Zhou, Zhiyang Dou, Zeyu Cao +7
We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation. Current state-of-the-art generative diffusion models have produced impressiv…
StructRe: Rewriting for Structured Shape Modeling
Jiepeng Wang, Hao Pan, Yang Liu +3
Man-made 3D shapes are naturally organized in parts and hierarchies; such structures provide important constraints for shape reconstruction and generation. Modeling shape structure…
TLControl: Trajectory and Language Control for Human Motion Synthesis
Weilin Wan, Zhiyang Dou, Taku Komura +3
Controllable human motion synthesis is essential for applications in AR/VR, gaming and embodied AI. Existing methods often focus solely on either language or full trajectory contro…
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models
Zhengming Yu, Zhiyang Dou, Xiaoxiao Long +9
We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models. Previous methods explored shape generation wit…