| Title | Wild Poses (1933) |
| File Size | 898 MegaByte |
| File | wild-poses_dqpvu.avi |
| Quality | MPEG-2 1440p HDTS |
| Launched | 87 years 1 month 1 day ago |
| Time | 02h 08 min |
Overview
Spanky's parents take their reluctant boy to get his portrait taken by a prissy photographer.
Wild Poses (1933)
Genre: Comedy
Cast: Matthew 'Stymie' Beard, Tommy Bond, Jerry Tucker, George McFarland, Oliver Hardy
Crew: Hal Roach, Robert F. McGowan, Robert F. McGowan, Francis Corby
Release: 1933-10-28
Budget: $5,877,348
Revenue: $43,911,384
Automotive Mechanic: Ms. Kitty Hoeger Jr.
Furniture Finisher: Prof. Jeromy Fahey II
Network Systems Analyst: Dr. Peyton Bradtke MD
Homeland Security: Roger Smith DDS
Religious Worker: Prof. Federico Bailey
TSA: Kristy Mills
Archeologist: Prof. Jules Sanford
Structural Iron and Steel Worker: Christelle Okuneva IV
License Clerk: Duane Wilderman
Semiconductor Processor: Americo O'Keefe
Coil Winders: Helmer Swaniawski
Heat Treating Equipment Operator: Mr. Gabriel Simonis
Oral Surgeon: Nedra Corkery
Cast: Matthew 'Stymie' Beard, Tommy Bond, Jerry Tucker, George McFarland, Oliver Hardy
Crew: Hal Roach, Robert F. McGowan, Robert F. McGowan, Francis Corby
Release: 1933-10-28
Budget: $5,877,348
Revenue: $43,911,384
Automotive Mechanic: Ms. Kitty Hoeger Jr.
Furniture Finisher: Prof. Jeromy Fahey II
Network Systems Analyst: Dr. Peyton Bradtke MD
Homeland Security: Roger Smith DDS
Religious Worker: Prof. Federico Bailey
TSA: Kristy Mills
Archeologist: Prof. Jules Sanford
Structural Iron and Steel Worker: Christelle Okuneva IV
License Clerk: Duane Wilderman
Semiconductor Processor: Americo O'Keefe
Coil Winders: Helmer Swaniawski
Heat Treating Equipment Operator: Mr. Gabriel Simonis
Oral Surgeon: Nedra Corkery
May 16, 2017 ... Wild Thing: Step-by-Step Instructions. Step 1 ... See also More Arm Balance Poses. Step 7 ... GO BACK TO A-Z POSE FINDER. Sorry, the video ....
Otto Phocus (Franklin Pangborn) is a haughty photographer hellbent on taking a formal portrait of a terrified Spanky (George McFarland). The little guy has.
Towards Large-Pose Face Frontalization in the Wild.
Jun 29, 2020 ... Wild parsnip is found in most townships, counties and along state highways in Wisconsin. The sap from the plant poses a serious health ....
Contents[show] Production Notes Length: Two Reel Producer: Robert F. McGowan Director: Robert.
Recently, remarkable advances have been achieved in 3D human pose estimationfrom monocular images because of the powerful Deep Convolutional NeuralNetworks (DCNNs). Despite their success on large-scale datasets collected inthe constrained lab environment, it is difficult to obtain the 3D poseannotations for in-the-wild images. Therefore, 3D human pose estimation in thewild is still a challenge. In this paper, we propose an adversarial learningframework, which distills the 3D human pose structures learned from the fullyannotated dataset to in-the-wild images with only 2D pose annotations. Insteadof defining hard-coded rules to constrain the pose estimation results, wedesign a novel multi-source discriminator to distinguish the predicted 3D posesfrom the ground-truth, which helps to enforce the pose estimator to generateanthropometrically valid poses even with images in the wild. We also observethat a carefully designed information source for the discriminator is essentialto boost the performance..
Wild Poses - Wikipedia.
In this paper, we tackle the problem of human motion transfer, where wesynthesize novel motion video for a target person that imitates the movementfrom a reference video. It is a video-to-video translation task in which theestimated poses are used to bridge two domains. Despite substantial progress onthe topic, there exist several problems with the previous methods. First, thereis a domain gap between training and testing pose sequences--the model istested on poses it has not seen during training, such as difficult dancingmoves. Furthermore, pose detection errors are inevitable, making the job of thegenerator harder. Finally, generating realistic pixels from sparse poses ischallenging in a single step. To address these challenges, we introduce a novelpose-to-video translation framework for generating high-quality videos that aretemporally coherent even for in-the-wild pose sequences unseen during training.We propose a pose augmentation method to minimize the training-test gap, aunified paired and.
Wild Poses is short subject in the Our Gang (The Little Rascals) series. It was produced and directed by Robert F. McGowan for Hal Roach Studios and first ....
OUR GANG SHORT "WILD POSES".
Human Motion Transfer from Poses in the Wild.
The Little Rascals Wild Poses 1933.
Jul 13, 2016 ... The Little Rascals Wild Poses 1933. Watch later. Share. Copy link. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try ....
Towards Accurate Multi-person Pose Estimation in the Wild.
Wild Poses.
Despite recent advances in face recognition using deep learning, severeaccuracy drops are observed for large pose variations in unconstrainedenvironments. Learning pose-invariant features is one solution, but needpensively labeled large-scale data and carefully designed feature learningalgorithms. In this work, we focus on frontalizing faces in the wild undervarious head poses, including extreme profile views. We propose a novel deep 3DMorphable Model (3DMM) conditioned Face Frontalization Generative AdversarialNetwork (GAN), termed as FF-GAN, to generate neutral head pose face images. Ourframework differs from both traditional GANs and 3DMM based modeling.Incorporating 3DMM into the GAN structure provides shape and appearance priorsfor fast convergence with less training data, while also supporting end-to-endtraining. The 3DMM-conditioned GAN employs not only the discriminator andgenerator loss but also a new masked symmetry loss to retain visual qualityunder occlusions, besides an identity l.
3D Human Pose Estimation in the Wild by Adversarial Learning.
Wild parsnip poses serious risk to humans.
Wild Thing.
We propose a method for multi-person detection and 2-D pose estimation thatachieves state-of-art results on the challenging COCO keypoints task. It is asimple, yet powerful, top-down approach consisting of two stages. In the first stage, we predict the location and scale of boxes which arelikely to contain people; for this we use the Faster RCNN detector. In thesecond stage, we estimate the keypoints of the person potentially contained ineach proposed bounding box. For each keypoint type we predict dense heatmapsand offsets using a fully convolutional ResNet. To combine these outputs weintroduce a novel aggregation procedure to obtain highly localized keypointpredictions. We also use a novel form of keypoint-based Non-Maximum-Suppression(NMS), instead of the cruder box-level NMS, and a novel form of keypoint-basedconfidence score estimation, instead of box-level scoring. Trained on COCO data alone, our final system achieves average precision of0.649 on the COCO test-dev set and the 0.643 test-s
Otto Phocus (Franklin Pangborn) is a haughty photographer hellbent on taking a formal portrait of a terrified Spanky (George McFarland). The little guy has.
Towards Large-Pose Face Frontalization in the Wild.
Jun 29, 2020 ... Wild parsnip is found in most townships, counties and along state highways in Wisconsin. The sap from the plant poses a serious health ....
Contents[show] Production Notes Length: Two Reel Producer: Robert F. McGowan Director: Robert.
Recently, remarkable advances have been achieved in 3D human pose estimationfrom monocular images because of the powerful Deep Convolutional NeuralNetworks (DCNNs). Despite their success on large-scale datasets collected inthe constrained lab environment, it is difficult to obtain the 3D poseannotations for in-the-wild images. Therefore, 3D human pose estimation in thewild is still a challenge. In this paper, we propose an adversarial learningframework, which distills the 3D human pose structures learned from the fullyannotated dataset to in-the-wild images with only 2D pose annotations. Insteadof defining hard-coded rules to constrain the pose estimation results, wedesign a novel multi-source discriminator to distinguish the predicted 3D posesfrom the ground-truth, which helps to enforce the pose estimator to generateanthropometrically valid poses even with images in the wild. We also observethat a carefully designed information source for the discriminator is essentialto boost the performance..
Wild Poses - Wikipedia.
In this paper, we tackle the problem of human motion transfer, where wesynthesize novel motion video for a target person that imitates the movementfrom a reference video. It is a video-to-video translation task in which theestimated poses are used to bridge two domains. Despite substantial progress onthe topic, there exist several problems with the previous methods. First, thereis a domain gap between training and testing pose sequences--the model istested on poses it has not seen during training, such as difficult dancingmoves. Furthermore, pose detection errors are inevitable, making the job of thegenerator harder. Finally, generating realistic pixels from sparse poses ischallenging in a single step. To address these challenges, we introduce a novelpose-to-video translation framework for generating high-quality videos that aretemporally coherent even for in-the-wild pose sequences unseen during training.We propose a pose augmentation method to minimize the training-test gap, aunified paired and.
Wild Poses is short subject in the Our Gang (The Little Rascals) series. It was produced and directed by Robert F. McGowan for Hal Roach Studios and first ....
OUR GANG SHORT "WILD POSES".
Human Motion Transfer from Poses in the Wild.
The Little Rascals Wild Poses 1933.
Jul 13, 2016 ... The Little Rascals Wild Poses 1933. Watch later. Share. Copy link. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try ....
Towards Accurate Multi-person Pose Estimation in the Wild.
Wild Poses.
Despite recent advances in face recognition using deep learning, severeaccuracy drops are observed for large pose variations in unconstrainedenvironments. Learning pose-invariant features is one solution, but needpensively labeled large-scale data and carefully designed feature learningalgorithms. In this work, we focus on frontalizing faces in the wild undervarious head poses, including extreme profile views. We propose a novel deep 3DMorphable Model (3DMM) conditioned Face Frontalization Generative AdversarialNetwork (GAN), termed as FF-GAN, to generate neutral head pose face images. Ourframework differs from both traditional GANs and 3DMM based modeling.Incorporating 3DMM into the GAN structure provides shape and appearance priorsfor fast convergence with less training data, while also supporting end-to-endtraining. The 3DMM-conditioned GAN employs not only the discriminator andgenerator loss but also a new masked symmetry loss to retain visual qualityunder occlusions, besides an identity l.
3D Human Pose Estimation in the Wild by Adversarial Learning.
Wild parsnip poses serious risk to humans.
Wild Thing.
We propose a method for multi-person detection and 2-D pose estimation thatachieves state-of-art results on the challenging COCO keypoints task. It is asimple, yet powerful, top-down approach consisting of two stages. In the first stage, we predict the location and scale of boxes which arelikely to contain people; for this we use the Faster RCNN detector. In thesecond stage, we estimate the keypoints of the person potentially contained ineach proposed bounding box. For each keypoint type we predict dense heatmapsand offsets using a fully convolutional ResNet. To combine these outputs weintroduce a novel aggregation procedure to obtain highly localized keypointpredictions. We also use a novel form of keypoint-based Non-Maximum-Suppression(NMS), instead of the cruder box-level NMS, and a novel form of keypoint-basedconfidence score estimation, instead of box-level scoring. Trained on COCO data alone, our final system achieves average precision of0.649 on the COCO test-dev set and the 0.643 test-s
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