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Face2Face: Real-Time Face Capture and Reenactment of RGB Videos

A novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video) that addresses the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling and re-render the manipulated output video in a photo-realistic fashion.
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Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision

We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly…
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VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

This work presents the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera and shows that the approach is more broadly applicable than RGB-D solutions, i.e., it works for outdoor scenes, community videos, and low quality commodity RGB cameras.
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BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration

This work systematically addresses issues with a novel, real-time, end-to-end reconstruction framework, which outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness.
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GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB

This work proposes a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network, and uses a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images.
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NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

Experiments show that NeuS outperforms the state-of-the-arts in high-quality surface reconstruction, especially for objects and scenes with complex structures and self-occlusion.
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MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction

A novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image and can be trained end-to-end in an unsupervised manner, which renders training on very large real world data feasible.
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Single-Shot Multi-person 3D Pose Estimation from Monocular RGB

We propose a new single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our approach uses novel occlusion-robust pose-maps (ORPM) which enable full body…
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Neural Sparse Voxel Fields

This work introduces Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering that is over 10 times faster than the state-of-the-art (namely, NeRF) at inference time while achieving higher quality results.
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StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis

StyleNeRF is a 3D-aware generative model for photo-realistic high-resolution image synthesis with high multi-view consistency and enables control of camera poses and different levels of styles, which can generalize to unseen views and supports challenging tasks, including style mixing and semantic editing.
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