61 citations · 204 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
Efficient Geometry-aware 3D Generative Adversarial Networks
Eric R. Chan, Connor Z. Lin, Matthew A. Chan +9
Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing…
Frame Averaging for Equivariant Shape Space Learning
Matan Atzmon, Koki Nagano, Sanja Fidler +2
The task of shape space learning involves mapping a train set of shapes to and from a latent representation space with good generalization properties. Often, real-world collections…
Neural Fields as Learnable Kernels for 3D Reconstruction
Francis Williams, Zan Gojcic, Sameh Khamis +4
We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art results wh…
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Wenzheng Chen, Joey Litalien, Jun Gao +5
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers. Many previous learning-based approaches fo…
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…