Global Position Prediction for Interactive Motion Capture

Schreiner, Paul; Perepichka, Maksym; Lewis, Hayden; Darkner, Sune; Kry, Paul G.; Erleben, Kenny; Zordan, Victor

doi:10.1145/3479985

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…GlobalNet uses ResNet50 as the backbone of the network structure. e four solid rectangles on the left are Res2-Res5 in ResNet50, and the dashed rectangles on the right represent the feature fusion process through upsampling and summation [17]. GlobalNet has four layers, and each layer outputs one result, so the respective loss functions need to be calculated for each of the four different results.…”

Section: Human Posture Estimationmentioning

confidence: 99%

Pose Estimation-Assisted Dance Tracking System Based on Convolutional Neural Network

2022

Computational Intelligence and Neuroscience

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In the field of music-driven, computer-assisted dance movement generation, traditional music movement adaptations and statistical mapping models have the following problems: Firstly, the dance sequences generated by the model are not powerful enough to fit the music itself. Secondly, the integrity of the dance movements produced is not sufficient. Thirdly, it is necessary to improve the suppleness and rationality of long-term dance sequences. Fourthly, traditional models cannot produce new dance movements. How to create smooth and complete dance gesture sequences after music is a problem that needs to be investigated in this paper. To address these problems, we design a deep learning dance generation algorithm to extract the association between sound and movement characteristics. During the feature extraction phase, rhythmic features extracted from music and audio beat features are used as musical features, and coordinates of the main points of human bones extracted from dance videos are used for training as movement characteristics. During the model building phase, the model’s generator module is used to achieve a basic mapping of music and dance movements and to generate gentle dance gestures. The identification module is used to achieve consistency between dance and music. The self-encoder module is used to make the audio function more representative. Experimental results on the DeepFashion dataset show that the generated model can synthesize the new view of the target person in any human posture of a given posture, complete the transformation of different postures of the same person, and retain the external features and clothing textures of the target person. Using a whole-to-detail generation strategy can improve the final video composition. For the problem of incoherent character movements in video synthesis, we propose to optimize the character movements by using a generative adversarial network, specifically by inserting generated motion compensation frames into the incoherent movement sequences to improve the smoothness of the synthesized video.

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Section: Human Posture Estimationmentioning

confidence: 99%

Pose Estimation-Assisted Dance Tracking System Based on Convolutional Neural Network

2022

Computational Intelligence and Neuroscience

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“…Several motion infilling approaches are also proposed to generate complete motions from partially observed motions [23,28,41,42]. Additionally, recent work on motion capture shows that global human translations can be predicted from 3D local joint positions [83]. In contrast to prior work, our trajectory predictor does not require GT root orientations but can predict both global root translations and orientations.…”

Section: Related Workmentioning

confidence: 99%