Qualcomm Incorporated

Ke-Li Cheng

Michel Adib Sarkis

Ning Bi

Kuang-Man Huang

Chieh-Ming Kuo

Yingyong Qi

Ajit Deepak Gupte

Gerhard Reitmayr

Chiranjib Choudhuri

Matthew Fischler

Anupama S

Avani Rao

Ying Hai Chen

Lei Wang

Zhen Wang

Yang Zhou

Scott David Beith

Stephane Pierre Villette

Suzana Arellano

Xuan Zou

Chen-Lan Chester Yen

Peng Liu

Min-Hui Lin

Systems and techniques are provided for generating a three-dimensional (3D) facial model. For example, a process can include obtaining at least one input image associated with a face. In some aspects, the process can include obtaining a pose for a 3D facial model associated with the face. In some examples, the process can include generating, by a machine learning model, the 3D facial model associated with the face. In some cases, one or more parameters associated with a shape component of the 3D facial model are conditioned on the pose. In some implementations, the 3D facial model is configured to vary in shape based on the pose for the 3D facial model associated with the face.

View dependent three-dimensional morphable models

Systems and techniques are provided for generating a texture for a three-dimensional (3D) facial model. For example, a process can include obtaining a first frame, the first frame including a first portion of a face. In some aspects, the process can include generating a 3D facial model based on the first frame and generating a first facial feature corresponding to the first portion of the face. In some examples, the process includes obtaining a second frame, the second frame including a second portion of the face. In some cases, the second portion of the face at least partially overlaps the first portion of the face. In some examples, the process includes combining the first facial feature with the second facial feature to generate an enhanced facial feature, wherein the combining is performed to enhance an appearance of select areas of the enhanced facial feature.

Facial texture synthesis for three-dimensional morphable models

A device includes a memory and one or more processors configured to process sensor data to determine a semantical context associated with the sensor data. The one or more processors are also configured to generate adjusted face data based on the determined semantical context and face data. The adjusted face data includes an avatar facial expression that corresponds to the semantical context.

Avatar facial expressions based on semantical context

Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

Object scanning using planar segmentation

Systems and techniques are provided for generating one or more models. For example, a process can include obtaining a plurality of input images corresponding to faces of one or more people during a training interval. The process can include determining a value of the coefficient representing at least the portion of the facial expression for each of the plurality of input images during the training interval. The process can include determining, from the determined values of the coefficient representing at least the portion of the facial expression for each of the plurality of input images during the training interval, an extremum value of the coefficient representing at least the portion of the facial expression during the training interval. The process can include generating an updated bounding value for the coefficient representing at least the portion of the facial expression based on the initial bounding value and the extremum value.

Adaptive bounding for three-dimensional morphable models

Techniques are provided for generating one or more three-dimensional (3D) models. In one example, an image of an object (e.g., a face or other object) is obtained, and a 3D model of the object in the image is generated. The 3D model includes geometry information. Color information for the 3D model is determined, and a fitted 3D model of the object is generated based on a modification of the geometry information and the color information for the 3D model. In some cases, the color information (e.g., determination and/or modification of the color information) and the fitted 3D model can be based on one or more vertex-level fitting processes. A refined 3D model of the object is generated based on the fitted 3D model and depth information associated with the fitted 3D model. In some cases, the refined 3D model can be based on a pixel-level refinement or fitting process.

Object reconstruction with texture parsing

Systems and techniques are provided for facial image augmentation. An example method can include obtaining a first image capturing a face. Using the first image, the method can determine, using a prediction model, a UV face position map including a two-dimensional (2D) representation of a three-dimensional (3D) structure of the face. The method can generate, based on the UV face position map, a 3D model of the face. The method can generate an extended 3D model of the face by extending the 3D model to include region(s) beyond a boundary of the 3D model. The region(s) can include a forehead region, a region surrounding at least a portion of the face, and/or other region. The method can generate, based on the extended 3D model, a second image depicting the face in a rotated position relative to a position of the face in the first image.

Image augmentation for analytics

A method performed by an electronic device is described. The method includes receiving an image. The image depicts a face. The method also includes detecting at least one facial landmark of the face in the image. The method further includes receiving a depth image of the face and determining at least one landmark depth by mapping the at least one facial landmark to the depth image. The method also includes determining a plurality of scales of depth image pixels based on the at least one landmark depth and determining a scale smoothness measure for each of the plurality of scales of depth image pixels. The method additionally includes determining facial liveness based on at least two of the scale smoothness measures. Determining the facial liveness may be based on a depth-adaptive smoothness threshold and/or may be based on a natural face size criterion.

Systems and methods for facial liveness detection

Methods, systems, and devices for image processing are described. The method may include identifying a face in a first image based on identifying one or more biometric features of the face, determining an angular direction of one or more pixels of the identified face, identifying an anchor point on the identified face, sorting each of one or more pixels of the identified face into one of a set of pixel bins based on a combination of the determined angular direction of the pixel and a distance between the pixel and the identified anchor point, and outputting an indication of authenticity associated with the face based on a number of pixels in each bin.

Depth image based face anti-spoofing

Provided are methods, apparatus, and computer-readable mediums for tracking objects that intersect with an exclusion zone defined for a scene being captured by a video camera. An exclusion zone can delineate an area of a video frame where background objects may be moving. The exclusion zone informs an object tracking system that objects within the exclusion zone should not be tracked. In various implementations, the object tracking system can determine that a bounding box for a blob intersects with the exclusion zone. The object tracking system can further, based on the bounding box intersecting with the exclusion zone, prevent outputting of a blob tracker associated with the blob.

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San Diego, CA, United States of America

Ke-Li Cheng