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Found 15 papers, 5 papers with code
Unsupervised Occupancy Learning from Sparse Point Cloud
Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio.
Mixing-Denoising Generalizable Occupancy Networks
While current state-of-the-art generalizable implicit neural shape models rely on the inductive bias of convolutions, it is still not entirely clear how properties emerging from such biases are compatible with the task of 3D reconstruction from point cloud.
Contact-conditioned hand-held object reconstruction from single-view images
Reconstructing the shape of hand-held objects from single-view color images is a long-standing problem in computer vision and computer graphics.
Neural Mesh-Based Graphics
We also perform competitively with respect to the state-of-the-art method SVS, which has been trained on the full dataset (DTU and Tanks and Temples) and then scene finetuned, in spite of their deeper neural renderer.
Learning Generalizable Light Field Networks from Few Images
We explore a new strategy for few-shot novel view synthesis based on a neural light field representation.
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space
We explore a new idea for learning based shape reconstruction from a point cloud, based on the recently popularized implicit neural shape representations.
Monocular Human Shape and Pose with Dense Mesh-borne Local Image Features
We propose to improve on graph convolution based approaches for human shape and pose estimation from monocular input, using pixel-aligned local image features.
Neural Human Deformation Transfer
We consider the problem of human deformation transfer, where the goal is to retarget poses between different characters.
Dual Mesh Convolutional Networks for Human Shape Correspondence
Convolutional networks have been extremely successful for regular data structures such as 2D images and 3D voxel grids.
Cross-modal Deep Face Normals with Deactivable Skip Connections
Core to our approach is a novel module that we call deactivable skip connections, which allows integrating both the auto-encoded and image-to-normal branches within the same architecture that can be trained end-to-end.
Domain Partitioning Network
Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game.
A Decoupled 3D Facial Shape Model by Adversarial Training
Data-driven generative 3D face models are used to compactly encode facial shape data into meaningful parametric representations.
3D Hand Shape and Pose from Images in the Wild
We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild.
Ranked #10 on
3D Hand Pose Estimation
on FreiHAND
(PA-MPVPE metric)
DGPose: Deep Generative Models for Human Body Analysis
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
Surface Motion Capture Transfer With Gaussian Process Regression
We address the problem of transferring motion between captured 4D models.
