1,978 research outputs found
Learning to Reconstruct People in Clothing from a Single RGB Camera
We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach
SelfNeRF: Fast Training NeRF for Human from Monocular Self-rotating Video
In this paper, we propose SelfNeRF, an efficient neural radiance field based
novel view synthesis method for human performance. Given monocular
self-rotating videos of human performers, SelfNeRF can train from scratch and
achieve high-fidelity results in about twenty minutes. Some recent works have
utilized the neural radiance field for dynamic human reconstruction. However,
most of these methods need multi-view inputs and require hours of training,
making it still difficult for practical use. To address this challenging
problem, we introduce a surface-relative representation based on
multi-resolution hash encoding that can greatly improve the training speed and
aggregate inter-frame information. Extensive experimental results on several
different datasets demonstrate the effectiveness and efficiency of SelfNeRF to
challenging monocular videos.Comment: Project page: https://ustc3dv.github.io/SelfNeR
Geometry-Based Next Frame Prediction from Monocular Video
We consider the problem of next frame prediction from video input. A
recurrent convolutional neural network is trained to predict depth from
monocular video input, which, along with the current video image and the camera
trajectory, can then be used to compute the next frame. Unlike prior next-frame
prediction approaches, we take advantage of the scene geometry and use the
predicted depth for generating the next frame prediction. Our approach can
produce rich next frame predictions which include depth information attached to
each pixel. Another novel aspect of our approach is that it predicts depth from
a sequence of images (e.g. in a video), rather than from a single still image.
We evaluate the proposed approach on the KITTI dataset, a standard dataset for
benchmarking tasks relevant to autonomous driving. The proposed method produces
results which are visually and numerically superior to existing methods that
directly predict the next frame. We show that the accuracy of depth prediction
improves as more prior frames are considered.Comment: To appear in 2017 IEEE Intelligent Vehicles Symposiu
Learning to Reconstruct People in Clothing from a Single RGB Camera
We present a learning-based model to infer the personalized 3D shape of
people from a few frames (1-8) of a monocular video in which the person is
moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our
model learns to predict the parameters of a statistical body model and instance
displacements that add clothing and hair to the shape. The model achieves fast
and accurate predictions based on two key design choices. First, by predicting
shape in a canonical T-pose space, the network learns to encode the images of
the person into pose-invariant latent codes, where the information is fused.
Second, based on the observation that feed-forward predictions are fast but do
not always align with the input images, we predict using both, bottom-up and
top-down streams (one per view) allowing information to flow in both
directions. Learning relies only on synthetic 3D data. Once learned, the model
can take a variable number of frames as input, and is able to reconstruct
shapes even from a single image with an accuracy of 6mm. Results on 3 different
datasets demonstrate the efficacy and accuracy of our approach
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