Abstract: Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map.

Abstract: Embodiments of the present invention are directed towards compactly incorporating texture charts into a texture atlas. Texture charts represent three-dimensional mesh segments flattened into two-dimensional shapes. In one embodiment, a texture atlas generating engine is used to generate and evaluate compactness scores of candidate placements for a texture chart. Candidate placements generally refer to the possible locations where a texture chart can be incorporated into a texture atlas. The compactness score can be based on minimizing the distance between a texture chart being incorporated into the texture atlas and the center of mass of previously incorporated texture charts within a texture atlas. In embodiments, an infinity norm can be utilized to compute such a compactness score by outputting an average length of vectors between a texture chart being incorporated into a texture atlas and the texture atlas. Other embodiments may be described and/or claimed.

Abstract: Digital image animation techniques are described. In one example, animations are used for a single digital image to permit movement or other effects to be exhibited as part of the digital image without requiring multiple frames as in conventional techniques. Transformation of the single digital image by the animations may also be coordinated, such as to synchronize or not synchronize movement of objects to promote realism. In another example, portions and even an entirety of these techniques may be performed automatically and without user intervention. Machine learning, for instance, may be employed using a neural network to classify the digital image into one or more semantic classes. The semantic classes may then be used to recommend animations to transform the digital image.