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Found 21 papers, 3 papers with code
Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization
To tackle this problem, we propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing.
Real-Time Radiance Fields for Single-Image Portrait View Synthesis
We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e. g., face portrait) in real-time.
Learning Human Dynamics in Autonomous Driving Scenarios
In this work, we propose a holistic framework for learning physically plausible human dynamics from real driving scenarios, narrowing the gap between real and simulated human behavior in safety-critical applications.
RANA: Relightable Articulated Neural Avatars
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting.
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation
An effective approach is to supervise the training of deep neural networks with a high-fidelity dataset of desired input-output pairs, captured with a light stage.
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
We present a large-scale synthetic dataset for novel view synthesis consisting of ~300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 x 1600 pixels).
Ranked #1 on
Novel View Synthesis
on RTMV
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
In this work, we present, DRaCoN, a framework for learning full-body volumetric avatars which exploits the advantages of both the 2D and 3D neural rendering techniques.
Efficient Geometry-aware 3D Generative Adversarial Networks
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.
Frame Averaging for Equivariant Shape Space Learning
A natural way to incorporate symmetries in shape space learning is to ask that the mapping to the shape space (encoder) and mapping from the shape space (decoder) are equivariant to the relevant symmetries.
Hierarchical Neural Implicit Pose Network for Animation and Motion Retargeting
We present HIPNet, a neural implicit pose network trained on multiple subjects across many poses.
Neural Fields as Learnable Kernels for 3D Reconstruction
We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression.
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers.
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations
Given a small set of high-quality textured objects, our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation.
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts.
Neural Rerendering in the Wild
Starting from internet photos of a tourist landmark, we apply traditional 3D reconstruction to register the photos and approximate the scene as a point cloud.
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
Ranked #2 on
Stereo Depth Estimation
on sceneflow
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
Fits Like a Glove: Rapid and Reliable Hand Shape Personalization
We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.
Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics
Sparse representations have been successfully applied to signal processing, computer vision and machine learning.
Learning an Efficient Model of Hand Shape Variation From Depth Images
We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model.
