187 citations · 577 across the 14 of their papers we have counts for
15 papers
Flexible Isosurface Extraction for Gradient-Based Mesh Optimization
Tianchang Shen, Jacob Munkberg, Jon Hasselgren +7
This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly co…
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
Seung Wook Kim, Bradley Brown, Kangxue Yin +6
Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce…
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
Jun Gao, Tianchang Shen, Zian Wang +6
As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of…
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation
Gopal Sharma, Kangxue Yin, Subhransu Maji +3
We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. This is inspired by the observation that view-based surface represen…
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis
Zhiqin Chen, Kangxue Yin, Sanja Fidler
In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis. Previous works either a…
Hierarchical Neural Implicit Pose Network for Animation and Motion Retargeting
Sourav Biswas, Kangxue Yin, Maria Shugrina +2
We present HIPNet, a neural implicit pose network trained on multiple subjects across many poses. HIPNet can disentangle subject-specific details from pose-specific details, effect…
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
Tianchang Shen, Jun Gao, Kangxue Yin +2
We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of im…
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations
Kangxue Yin, Jun Gao, Maria Shugrina +2
We propose a method to create plausible geometric and texture style variations of 3D objects in the quest to democratize 3D content creation. Given a pair of textured source and ta…
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
Yuxuan Zhang, Huan Ling, Jun Gao +5
We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are…
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes
Towaki Takikawa, Joey Litalien, Kangxue Yin +6
Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural…
COALESCE: Component Assembly by Learning to Synthesize Connections
Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri +3
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections. To handle geometric and topolo…
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Yanran Guan, Han Liu, Kun Liu +8
We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D…
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
Zhiqin Chen, Kangxue Yin, Matthew Fisher +2
We treat shape co-segmentation as a representation learning problem and introduce BAE-NET, a branched autoencoder network, for the task. The unsupervised BAE-NET is trained with a…
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Kangxue Yin, Zhiqin Chen, Hui Huang +2
We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and c…
P2P-NET: Bidirectional Point Displacement Net for Shape Transform
Kangxue Yin, Hui Huang, Daniel Cohen-Or +1
We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e.g., meso-skeletons…