201 research outputs found
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review
Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view
synthesis and 3D reconstruction method, NeRF models find applications in
robotics, urban mapping, autonomous navigation, virtual reality/augmented
reality, and more. Since the original paper by Mildenhall et al., more than 250
preprints were published, with more than 100 eventually being accepted in tier
one Computer Vision Conferences. Given NeRF popularity and the current interest
in this research area, we believe it necessary to compile a comprehensive
survey of NeRF papers from the past two years, which we organized into both
architecture, and application based taxonomies. We also provide an introduction
to the theory of NeRF based novel view synthesis, and a benchmark comparison of
the performance and speed of key NeRF models. By creating this survey, we hope
to introduce new researchers to NeRF, provide a helpful reference for
influential works in this field, as well as motivate future research directions
with our discussion section
Conditional Adversarial Synthesis of 3D Facial Action Units
Employing deep learning-based approaches for fine-grained facial expression
analysis, such as those involving the estimation of Action Unit (AU)
intensities, is difficult due to the lack of a large-scale dataset of real
faces with sufficiently diverse AU labels for training. In this paper, we
consider how AU-level facial image synthesis can be used to substantially
augment such a dataset. We propose an AU synthesis framework that combines the
well-known 3D Morphable Model (3DMM), which intrinsically disentangles
expression parameters from other face attributes, with models that
adversarially generate 3DMM expression parameters conditioned on given target
AU labels, in contrast to the more conventional approach of generating facial
images directly. In this way, we are able to synthesize new combinations of
expression parameters and facial images from desired AU labels. Extensive
quantitative and qualitative results on the benchmark DISFA dataset demonstrate
the effectiveness of our method on 3DMM facial expression parameter synthesis
and data augmentation for deep learning-based AU intensity estimation
3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping
We present 3DHumanGAN, a 3D-aware generative adversarial network that
synthesizes photorealistic images of full-body humans with consistent
appearances under different view-angles and body-poses. To tackle the
representational and computational challenges in synthesizing the articulated
structure of human bodies, we propose a novel generator architecture in which a
2D convolutional backbone is modulated by a 3D pose mapping network. The 3D
pose mapping network is formulated as a renderable implicit function
conditioned on a posed 3D human mesh. This design has several merits: i) it
leverages the strength of 2D GANs to produce high-quality images; ii) it
generates consistent images under varying view-angles and poses; iii) the model
can incorporate the 3D human prior and enable pose conditioning. Project page:
https://3dhumangan.github.io/.Comment: 9 pages, 8 figure
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