Region-based stabilized face tracking
AssigneeSnap Inc.
InventorsChen Cao, Menglei Chai, Linjie Luo, Oliver Woodford
Life of this patent
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Abstract
Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program and a method for accessing a set of images depicting at least a portion of a face. A set of facial regions of the face is identified, each facial region of the set of facial regions intersecting another facial region with at least one common vertex that is a member of a set of facial vertices. For each facial region of the set of facial regions, a weight formed from a set of region coefficients is generated. Based on the set of facial regions and the weight of each facial region of the set of facial regions, the face is tracked across the set of images.
Claims20
A method comprising:
- accessing a set of images depicting at least a portion of a real-world object;
- identifying a set of regions of the real-world object;
- tracking the real-world object across the set of images, the tracking comprising:
- solving for rigid pose parameters and expression parameters in alternating optimizations, such that during rigid pose optimization, expression parameters are fixed while the rigid pose parameters are determined and, during expression optimization, rigid pose parameters are fixed while the expression parameters are determined;
- and
- based on the rigid pose parameters and expression parameters, tracking a first region of the set of regions based on a first model and a second region of the set of regions based on a second model;
- and
- overlaying a virtual object over a portion of the real-world object depicted in the image in accordance with tracking the real-world object across the set of images using the first and second models.
A system comprising:
- one or more processors;
- and
- a non-transitory processor-readable storage medium storing processor executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
- accessing a set of images depicting at least a portion of a real-world object;
- identifying a set of regions of the real-world object;
- tracking the real-world object across the set of images, the tracking comprising:
- solving for rigid pose parameters and expression parameters in alternating optimizations, such that during rigid pose optimization, expression parameters are fixed while the rigid pose parameters are determined and, during expression optimization, rigid pose parameters are fixed while the expression parameters are determined;
- and
- based on the rigid pose parameters and expression parameters, tracking a first region of the set of regions based on a first model and a second region of the set of regions based on a second model;
- and
- overlaying a virtual object over a portion of the real-world object depicted in the image in accordance with tracking the real-world object across the set of images using the first and second models.
A non-transitory computer-readable medium comprising instructions that, when executed by a processor, configure the processor to perform operations comprising:
- accessing a set of images depicting at least a portion of a real-world object;
- identifying a set of regions of the real-world object;
- tracking the real-world object across the set of images, the tracking comprising:
- solving for rigid pose parameters and expression parameters in alternating optimizations, such that during rigid pose optimization, expression parameters are fixed while the rigid pose parameters are determined and, during expression optimization, rigid pose parameters are fixed while the expression parameters are determined;
- and
- based on the rigid pose parameters and expression parameters, tracking a first region of the set of regions based on a first model and a second region of the set of regions based on a second model;
- and
- overlaying a virtual object over a portion of the real-world object depicted in the image in accordance with tracking the real-world object across the set of images using the first and second models.
Dependencies are inferred from the claim text.