2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019
DOI: 10.1109/cvpr.2019.00606
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Photo Wake-Up: 3D Character Animation From a Single Photo

Abstract: Figure 1: Given a single photo as input (far left), we create a 3D animatable version of the subject, which can now walk towards the viewer (middle). The 3D result can be experienced in augmented reality (right); in the result above the user has virtually hung the artwork with a HoloLens headset and can watch the character run out of the painting from different views. Please see all results in the supplementary video: https://youtu.be/G63goXc5MyU. AbstractWe present a method and application for animating a hum… Show more

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Cited by 118 publications
(97 citation statements)
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“…More recent works predict body parameters of the popular SMPL model [6] by either minimizing the silhouette matching error [42], joint error based on the silhouette and 2D joints [43], or an adversarial loss that can distinguish unrealistic reconstruction output [23]. Concurrent to our work, Weng et al [52] present a method to animate a person in 3D from a single image based on the SMPL model and 2D warping.…”
Section: Related Workmentioning
confidence: 93%
“…More recent works predict body parameters of the popular SMPL model [6] by either minimizing the silhouette matching error [42], joint error based on the silhouette and 2D joints [43], or an adversarial loss that can distinguish unrealistic reconstruction output [23]. Concurrent to our work, Weng et al [52] present a method to animate a person in 3D from a single image based on the SMPL model and 2D warping.…”
Section: Related Workmentioning
confidence: 93%
“…Recent deep learning methods also using retrieved data can be used at many stages of the avatar creation process. Some methods have been successfully used to create avatars from pictures by recreating full 3D meshes from a photo (Hu et al, 2017;Saito et al, 2019), meshes from multiple cameras Collet et al (2015); Guo et al (2019), reduce the generated artifacts (Blanz and Vetter, 1999;Ichim et al, 2015), as well as to improve rigging (Weng et al, 2019). Deep learning methods can also generate completely new avatars that are not representations of existing people, by using adversarial networks (Karras et al, 2019).…”
Section: Data Driven Methods and Scanningmentioning
confidence: 99%
“…Although there have been some studies involving video processing, such as video generation [115], video colorization [116], [117], video inpainting [118], motion transfer [119], and facial animation synthesis [120]- [123], the research on video using GANs is limited. In addition, although GANs have been applied to the generation and synthesis of 3D models, such as 3D colorization [124], 3D face reconstruction [125], [126], 3D character animation [127], and 3D textured object generation [128], the results are far from perfect. At present, GANs are still based on large amounts of training data.…”
Section: B Future Opportunitiesmentioning
confidence: 99%