2022-06-25 更新
Variable Bitrate Neural Fields
Authors:Towaki Takikawa, Alex Evans, Jonathan Tremblay, Thomas Müller, Morgan McGuire, Alec Jacobson, Sanja Fidler
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations. State-of-the-art results are obtained by conditioning a neural approximation with a lookup from trainable feature grids that take on part of the learning task and allow for smaller, more efficient neural networks. Unfortunately, these feature grids usually come at the cost of significantly increased memory consumption compared to stand-alone neural network models. We present a dictionary method for compressing such feature grids, reducing their memory consumption by up to 100x and permitting a multiresolution representation which can be useful for out-of-core streaming. We formulate the dictionary optimization as a vector-quantized auto-decoder problem which lets us learn end-to-end discrete neural representations in a space where no direct supervision is available and with dynamic topology and structure. Our source code will be available at https://github.com/nv-tlabs/vqad.
PDF SIGGRAPH 2022. Project Page: https://nv-tlabs.github.io/vqad/
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NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis
Authors:Radu Alexandru Rosu, Sven Behnke
Multi-View Stereo (MVS) is a core task in 3D computer vision. With the surge of novel deep learning methods, learned MVS has surpassed the accuracy of classical approaches, but still relies on building a memory intensive dense cost volume. Novel View Synthesis (NVS) is a parallel line of research and has recently seen an increase in popularity with Neural Radiance Field (NeRF) models, which optimize a per scene radiance field. However, NeRF methods do not generalize to novel scenes and are slow to train and test. We propose to bridge the gap between these two methodologies with a novel network that can recover 3D scene geometry as a distance function, together with high-resolution color images. Our method uses only a sparse set of images as input and can generalize well to novel scenes. Additionally, we propose a coarse-to-fine sphere tracing approach in order to significantly increase speed. We show on various datasets that our method reaches comparable accuracy to per-scene optimized methods while being able to generalize and running significantly faster. We provide the source code at https://github.com/AIS-Bonn/neural_mvs
PDF Accepted for International Joint Conference on Neural Networks (IJCNN) 2022. Code available at https://github.com/AIS-Bonn/neural_mvs
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EventNeRF: Neural Radiance Fields from a Single Colour Event Camera
Authors:Viktor Rudnev, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik
Learning coordinate-based volumetric 3D scene representations such as neural radiance fields (NeRF) has been so far studied assuming RGB or RGB-D images as inputs. At the same time, it is known from the neuroscience literature that human visual system (HVS) is tailored to process asynchronous brightness changes rather than synchronous RGB images, in order to build and continuously update mental 3D representations of the surroundings for navigation and survival. Visual sensors that were inspired by HVS principles are event cameras. Thus, events are sparse and asynchronous per-pixel brightness (or colour channel) change signals. In contrast to existing works on neural 3D scene representation learning, this paper approaches the problem from a new perspective. We demonstrate that it is possible to learn NeRF suitable for novel-view synthesis in the RGB space from asynchronous event streams. Our models achieve high visual accuracy of the rendered novel views of challenging scenes in the RGB space, even though they are trained with substantially fewer data (i.e., event streams from a single event camera moving around the object) and more efficiently (due to the inherent sparsity of event streams) than the existing NeRF models trained with RGB images. We will release our datasets and the source code, see https://4dqv.mpi-inf.mpg.de/EventNeRF/.
PDF 14 pages, 10 figures, 2 tables
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Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
Authors:Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, HongSheng Li
Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, these methods focus on 2D image generative models, which are prone to producing inconsistent face images under large expression and pose changes. In this paper, we propose a new NeRF-based conditional 3D face synthesis framework, which enables 3D controllability over the generated face images by imposing explicit 3D conditions from 3D face priors. At its core is a conditional Generative Occupancy Field (cGOF) that effectively enforces the shape of the generated face to commit to a given 3D Morphable Model (3DMM) mesh. To achieve accurate control over fine-grained 3D face shapes of the synthesized image, we additionally incorporate a 3D landmark loss as well as a volume warping loss into our synthesis algorithm. Experiments validate the effectiveness of the proposed method, which is able to generate high-fidelity face images and shows more precise 3D controllability than state-of-the-art 2D-based controllable face synthesis methods. Find code and demo at https://keqiangsun.github.io/projects/cgof.
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FWD: Real-time Novel View Synthesis with Forward Warping and Depth
Authors:Ang Cao, Chris Rockwell, Justin Johnson
Novel view synthesis (NVS) is a challenging task requiring systems to generate photorealistic images of scenes from new viewpoints, where both quality and speed are important for applications. Previous image-based rendering (IBR) methods are fast, but have poor quality when input views are sparse. Recent Neural Radiance Fields (NeRF) and generalizable variants give impressive results but are not real-time. In our paper, we propose a generalizable NVS method with sparse inputs, called FWD, which gives high-quality synthesis in real-time. With explicit depth and differentiable rendering, it achieves competitive results to the SOTA methods with 130-1000x speedup and better perceptual quality. If available, we can seamlessly integrate sensor depth during either training or inference to improve image quality while retaining real-time speed. With the growing prevalence of depths sensors, we hope that methods making use of depth will become increasingly useful.
PDF CVPR 2022. Project website https://caoang327.github.io/FWD/
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RigNeRF: Fully Controllable Neural 3D Portraits
Authors:ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu
Volumetric neural rendering methods, such as neural radiance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not support the editing of objects, such as a human head, within a scene. In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video. We model changes in head pose and facial expressions using a deformation field that is guided by a 3D morphable face model (3DMM). The 3DMM effectively acts as a prior for RigNeRF that learns to predict only residuals to the 3DMM deformations and allows us to render novel (rigid) poses and (non-rigid) expressions that were not present in the input sequence. Using only a smartphone-captured short video of a subject for training, we demonstrate the effectiveness of our method on free view synthesis of a portrait scene with explicit head pose and expression controls. The project page can be found here: http://shahrukhathar.github.io/2022/06/06/RigNeRF.html
PDF The project page can be found here: http://shahrukhathar.github.io/2022/06/06/RigNeRF.html
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VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Authors:Katja Schwarz, Axel Sauer, Michael Niemeyer, Yiyi Liao, Andreas Geiger
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields. While demonstrating impressive results, querying an MLP for every sample along each ray leads to slow rendering. Therefore, existing approaches often render low-resolution feature maps and process them with an upsampling network to obtain the final image. Albeit efficient, neural rendering often entangles viewpoint and content such that changing the camera pose results in unwanted changes of geometry or appearance. Motivated by recent results in voxel-based novel view synthesis, we investigate the utility of sparse voxel grid representations for fast and 3D-consistent generative modeling in this paper. Our results demonstrate that monolithic MLPs can indeed be replaced by 3D convolutions when combining sparse voxel grids with progressive growing, free space pruning and appropriate regularization. To obtain a compact representation of the scene and allow for scaling to higher voxel resolutions, our model disentangles the foreground object (modeled in 3D) from the background (modeled in 2D). In contrast to existing approaches, our method requires only a single forward pass to generate a full 3D scene. It hence allows for efficient rendering from arbitrary viewpoints while yielding 3D consistent results with high visual fidelity.
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