384 research outputs found
Deep deformable models for 3D human body
Deformable models are powerful tools for modelling the 3D shape variations for a class of objects. However, currently the application and performance of deformable models for human body are restricted due to the limitations in current 3D datasets, annotations, and the model formulation itself. In this thesis, we address the issue by making the following contributions in the field of 3D human body modelling, monocular reconstruction and data collection/annotation.
Firstly, we propose a deep mesh convolutional network based deformable model for 3D human body. We demonstrate the merit of this model in the task of monocular human mesh recovery. While outperforming current state of the art models in mesh recovery accuracy, the model is also light weighted and more flexible as it can be trained end-to-end and fine-tuned for a specific task.
A second contribution is a bone level skinned model of 3D human mesh, in which bone modelling and identity-specific variation modelling are decoupled. Such formulation allows the use of mesh convolutional networks for capturing detailed identity specific variations, while explicitly controlling and modelling the pose variations through linear blend skinning with built-in motion constraints. This formulation not only significantly increases the accuracy in 3D human mesh reconstruction, but also facilitates accurate in the wild character animation and retargetting.
Finally we present a large scale dataset of over 1.3 million 3D human body scans in daily clothing. The dataset contains over 12 hours of 4D recordings at 30 FPS, consisting of 7566 dynamic sequences of 3D meshes from 4205 subjects. We propose a fast and accurate sequence registration pipeline which facilitates markerless motion capture and automatic dense annotation for the raw scans, leading to automatic synthetic image and annotation generation that boosts the performance for tasks such as monocular human mesh reconstruction.Open Acces
Characterization of a RS-LiDAR for 3D Perception
High precision 3D LiDARs are still expensive and hard to acquire. This paper
presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with
sufficient supplies, in comparison with VLP-16. The paper also provides a set
of evaluations to analyze the characterizations and performances of LiDARs
sensors. This work analyzes multiple properties, such as drift effects,
distance effects, color effects and sensor orientation effects, in the context
of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a
cheaper and acquirable substitute of VLP-16 with similar efficiency.Comment: For ICRA201
VarifocalNet: An IoU-aware Dense Object Detector
Accurately ranking the vast number of candidate detections is crucial for
dense object detectors to achieve high performance. Prior work uses the
classification score or a combination of classification and predicted
localization scores to rank candidates. However, neither option results in a
reliable ranking, thus degrading detection performance. In this paper, we
propose to learn an Iou-aware Classification Score (IACS) as a joint
representation of object presence confidence and localization accuracy. We show
that dense object detectors can achieve a more accurate ranking of candidate
detections based on the IACS. We design a new loss function, named Varifocal
Loss, to train a dense object detector to predict the IACS, and propose a new
star-shaped bounding box feature representation for IACS prediction and
bounding box refinement. Combining these two new components and a bounding box
refinement branch, we build an IoU-aware dense object detector based on the
FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive
experiments on MS COCO show that our VFNet consistently surpasses the strong
baseline by 2.0 AP with different backbones. Our best model VFNet-X-1200
with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO
test-dev, which is state-of-the-art among various object detectors.Code is
available at https://github.com/hyz-xmaster/VarifocalNet .Comment: Accepted to CVPR 2021 as an ora
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model
As an emerging approach, deep learning plays an increasingly influential role
in channel modeling. Traditional ray tracing (RT) methods of channel modeling
tend to be inefficient and expensive. In this paper, we present a
super-resolution (SR) model for channel characteristics. Residual connection
and attention mechanism are applied to this convolutional neural network (CNN)
model. Experiments prove that the proposed model can reduce the noise
interference generated in the SR process and solve the problem of low
efficiency of RT. The mean absolute error of our channel SR model on the PL
achieves the effect of 2.82 dB with scale factor 2, the same accuracy as RT
took only 52\% of the time in theory. Compared with vision transformer (ViT),
the proposed model also demonstrates less running time and computing cost in SR
of channel characteristics
Boomerang Switch in Multiple Rounds. Application to AES Variants and Deoxys
The boomerang attack is a cryptanalysis technique that allows an attacker to concatenate two short differential characteristics. Several research results (ladder switch, S-box switch, sandwich attack, Boomerang Connectivity Table (BCT), ...) showed that the dependency between these two characteristics at the switching round can have a significant impact on the complexity of the attack, or even potentially invalidate it. In this paper, we revisit the issue of boomerang switching effect, and exploit it in the case where multiple rounds are involved. To support our analysis, we propose a tool called Boomerang Difference Table (BDT), which can be seen as an improvement of the BCT and allows a systematic evaluation of the boomerang switch through multiple rounds. In order to illustrate the power of this technique, we propose a new related-key attack on 10-round AES-256 which requires only 2 simple related-keys and 275 computations. This is a much more realistic scenario than the state-of-the-art 10-round AES-256 attacks, where subkey oracles, or several related-keys and high computational power is needed. Furthermore, we also provide improved attacks against full AES-192 and reduced-round Deoxys
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