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He admitted they could have shot him or added him to their hostage list, adding it was not pleasant to look down to barrel of a machine gun. The raid lasted just 11 minutes and all but one of the six gunmen were shot and killed. From the side-by-side comparisons, one can easily see the difference between the rendered results against the ground truth. Our results maintain low MPJPE compared to the other methods. Fig. 7 plots the MPJPE curves over time (around 1400 frames) on two selected joints: the left ankle from Walking S9 and the left elbow from Smoking S9. To visually demonstrate the significance of the estimation improvement, we apply animation retargeting to a 3D avatar by synthesizing the captured motion from the same frame of the Walking S9 and Posing S9 sequences as shown in Fig. 9. With the support of additional mesh surface driven by the pose, it helps magnify the degree of body part arrangement that enhance the contrast of estimation.


The architecture of the causal attention model is shown in Fig. 12. The architecture is similar to the one described in Fig. 2, but here we only consider the left half of the input video sequence. The corresponding pose estimation results by different approaches are shown in each of the following rows. For the Posing S9 in the first row, our results bear the closest similarity as the ground truth with the right arm of the character naturally hanging down the side of the body, while others present more distinct arm gesture. 2019) while ours stay more aligned to the ground truth. 2019) or Cascaded Pyramid Network (CPN) (Chen and Ramanan 2017) together with Mask R-CNN (He et al. 2017) as the backbone. 2017) and ResNet-101-FPN (Lin et al. Figure 11 shows more retargeting results on the same dataset for different frames. 2016) on the MPII dataset to extract 2D keypoint locations within the ground-truth bounding boxes. MPII has 16 joints which missed the neck/nose joint in the Human3.6M dataset. We also applied the results of fine-tuned SH model on the Human3.6M dataset developed by Martinez et al. 2019), our results yield low errors consistently through learning the long-range dependencies using the multi-scale dilation convolution (Figs.


Specifically, the shadows of the legs and the right hand are differently rendered due to the erroneous pose estimated using the method in Pavllo et al. 2018), Deep high-resolution representation for human pose estimation (HRnet) (Sun et al. The quantified MPJPE for each joint estimation is shown in the correponding histograms right below it. The big difference between the pre-trained and fine-tuned models are the 2D human joints estimation accuracy and number of joints. We applied the pre-trained SH, fine-tuned SH, and www.johnsonclassifieds.com fine-tuned CPN models (Pavllo et al. 2018; Pavllo et al. Figure 17 shows satisfactory results are achieved, given the additional noises. Figure 10 shows the testing results on two scenes of the Human3D dataset: smoking S9 and photo S9. Based on the results of our experiment, our network can learn different joint label information. Thanks to the attention model that successfully extracts temporal information from neighbor frames, the full 3D pose is correctly recovered (Figs.


It’s also possible to record Full HD video at a range of frame rates, topping out at 120fps, which is great for slow motion playback. Helpfully, there’s also a View Assist function that previews the footage in Full HD standard BT.709 equivalent to make it easier to assess what is being captured in OM-Log400 mode. Calls to phone numbers listed for the couple went unanswered, and their lawyer declined to make them available for an interview. Over the last couple years the once-revolutionary HD video standard has been swept aside by the rise of cameras capable of producing footage in 4K resolution. Two years after his breakthrough result at Wimbledon, father John was arrested and jailed for eight months for headbutting Bernard's hitting partner Thomas Drouet. Though it is hard to see the difference from the single frame, from the MPJPE (the green number on the top-left corner of each pose result), our attention-based model delivers the best result. One can see our approach consistently yields less MPJPE over the frames.