Abstract: Disclosed are systems and methods for identifying threat events in an enterprise network and managing detection rules and responses to the events. A threat intelligence computer system can receive information about a detected threat event including a phase of attack and a detected domain of the threat event, apply at least one tag to the detected event that associates the event with at least one of the rules triggered in response to detecting the event, evaluate the tagged rules against the information, flag the event as having an improvement opportunity, determine whether the rule tagged to the event is a candidate for improvement, generate, based on the determination, instructions for improving the rule, generate a prioritization scheme indicating an order to address the instructions to improve the rule amongst instructions to improve various threat detection rules, and generate and return output indicating the prioritization scheme for presentation at user devices.

Abstract: A computing device is provided, including processor and a storage device holding instructions that are executable by the processor to implement a base artificial intelligence (AI) model and two or more delta AI models, each delta AI model having lower dimensionality than the base AI model. An inference request including an input prompt is received, the inference request specifying a selected delta AI model of the two or more delta AI models. The input prompt is input to the base AI model to thereby generate a base model result vector. The input prompt is input to the selected delta AI model to thereby generate a delta model result vector. An output vector is generated by combining the base model result vector and the delta model result vector via a combination operation. The output vector is output.

Abstract: One embodiment of the present invention sets forth a technique for performing landmark detection. The technique includes generating, via execution of a first machine learning model, a first set of displacements associated with a first set of query points on a canonical shape based on a first annotation style associated with the first set of query points. The technique also includes determining, via execution of a second machine learning model, a first set of landmarks on a first face depicted in a first image based on the first set of displacements. The technique further includes training the first machine learning model based on one or more losses associated with the first set of landmarks to generate a first trained machine learning model.

Abstract: The present invention sets forth a technique for performing face micro detail recovery. The technique includes generating one or more skin texture displacement maps based on images of one or more skin surfaces. The technique also includes transferring, via one or more machine learning models, stylistic elements included in the one or more skin texture displacement maps onto one or more regions included in a modified three-dimensional (3D) facial reconstruction. The technique further includes generating a final 3D facial reconstruction that includes structural elements included in the 3D facial reconstruction and the stylistic elements included in the one or more skin texture displacement maps.

Abstract: One embodiment of the present invention sets forth a technique for performing landmark detection. The technique includes applying, via execution of a first machine learning model, a first transformation to a first image depicting a first face to generate a second image. The technique also includes determining, via execution of a second machine learning model, a first set of landmarks on the first face based on the second image. The technique further includes training the first machine learning model based on one or more losses associated with the first set of landmarks to generate a first trained machine learning model.

Abstract: One embodiment of the present invention sets forth a technique for performing landmark detection. The technique includes determining a first set of parameters associated with a depiction of a first face in a first image. The technique also includes generating, via execution of a first machine learning model, a first set of three-dimensional (3D) landmarks on the first face based on the first set of parameters, and projecting, based on the first set of parameters, the first set of 3D landmarks onto the first image to generate a first set of two-dimensional (2D) landmarks. The technique further includes training the first machine learning model based on one or more losses associated with the first set of 2D landmarks to generate a first trained machine learning model.

Abstract: Embodiment of the present invention sets forth techniques for performing face reconstruction. The techniques include generating an identity mesh based on an identity encoding that represents an identity associated with a face in one or more images. The techniques also include generating an expression mesh based on an expression encoding that represents an expression associated with the face in the one or more images. The techniques also include generating, by a machine learning model, an output mesh of the face based on the identity mesh and the expression mesh.

Abstract: A technique for rendering an input geometry includes generating a first segmentation mask for a first input geometry and a first set of texture maps associated with one or more portions of the first input geometry. The technique also includes generating, via one or more neural networks, a first set of neural textures for the one or more portions of the first input geometry. The technique further includes rendering a first image corresponding to the first input geometry based on the first segmentation mask, the first set of texture maps, and the first set of neural textures.

Abstract: Techniques are disclosed for generating photorealistic images of objects, such as heads, from multiple viewpoints. In some embodiments, a morphable radiance field (MoRF) model that generates images of heads includes an identity model that maps an identifier (ID) code associated with a head into two codes: a deformation ID code encoding a geometric deformation from a canonical head geometry, and a canonical ID code encoding a canonical appearance within a shape-normalized space. The MoRF model also includes a deformation field model that maps a world space position to a shape-normalized space position based on the deformation ID code. Further, the MoRF model includes a canonical neural radiance field (NeRF) model that includes a density multi-layer perceptron (MLP) branch, a diffuse MLP branch, and a specular MLP branch that output densities, diffuse colors, and specular colors, respectively. The MoRF model can be used to render images of heads from various viewpoints.

Abstract: The present invention sets forth a technique for performing facial rig generation. The technique includes generating a blendshape model including a plurality of vertices, a plurality of meshes, and a plurality of patches. The technique also includes modifying one or more blendweight values associated with each of the plurality of patches based on a plurality of facial depictions included in a facial database and one or more sample depictions of a target character and generating an output facial rig model based on the blendshape model and the one or more modified blendweight values. The technique further includes generating one or more expressive depictions of the target character based at least on the output facial rig.

Abstract: Various embodiments set forth systems and techniques for changing a face within an image. The techniques include receiving a first image including a face associated with a first facial identity; generating, via a machine learning model, at least a first texture map and a first position map based on the first image; rendering a second image including a face associated with a second facial identity based on the first texture map and the first position map, wherein the second facial identity is different from the first facial identity.

Abstract: A technique for rendering an input geometry includes generating a first segmentation mask for a first input geometry and a first set of texture maps associated with one or more portions of the first input geometry. The technique also includes generating, via one or more neural networks, a first set of neural textures for the one or more portions of the first input geometry. The technique further includes rendering a first image corresponding to the first input geometry based on the first segmentation mask, the first set of texture maps, and the first set of neural textures.

Abstract: Embodiments herein describe a host that polls a network adapter to receive data from a network. That is, the host/CPU/application thread polls the network adapter (e.g., the network card, NIC, or SmartNIC) to determine whether a packet has been received. If so, the host informs the network adapter to store the packet (or a portion of the packet) in a CPU register. If the requested data has not yet been received by the network adapter from the network, the network adapter can delay the responding to the request to provide extra time for the adapter to receive the data from the network.

Abstract: A technique for synthesizing a shape includes generating a first plurality of offset tokens based on a first shape code and a first plurality of position tokens, wherein the first shape code represents a variation of a canonical shape, and wherein the first plurality of position tokens represent a first plurality of positions on the canonical shape. The technique also includes generating a first plurality of offsets associated with the first plurality of positions on the canonical shape based on the first plurality of offset tokens. The technique further includes generating the shape based on the first plurality of offsets and the first plurality of positions.

Abstract: One embodiment of the present invention sets forth a technique for performing appearance capture. The technique includes receiving a first sequence of images of an object, wherein the first sequence of images includes a first set of images interleaved with a second set of images, and wherein the first set of images is captured based on illumination of the object using a first lighting pattern and the second set of images is captured based on illumination of the object using one or more lighting patterns that are different from the first lighting pattern. The technique also includes generating a first set of appearance parameters associated with the object based on a first inverse rendering associated with the first sequence of images.

Abstract: The present invention sets forth a technique for simulating wrinkles under dynamic facial expression. The technique includes receiving a wrinkle graph, including a plurality of nodes associated with a plurality of pores included in a three-dimensional (3D) representation of a facial structure and a plurality of edges associated with a plurality of wrinkles included in the 3D representation of a facial structure. The technique also includes assigning one or more of the plurality of wrinkles associated with edges in the wrinkle graph to one of a plurality of bins and generating, for each of the bins, a plurality of pre-computed displacement texture maps. The technique further includes generating a per-frame displacement texture map and modifying an animation frame based on the per-frame displacement texture map, such that the modified animation frame depicts the plurality of pores and the plurality of wrinkles included in the 3D representation of the facial structure.

Abstract: A network interface device comprises a streaming data processing path comprising a first data processing engine and hubs. A first scheduler associated with a first hub controls an output of data by the first hub to the first data processing engine and a second scheduler associated with a second hub controls an output of data by the second hub. The first hub is arranged upstream of the first data processing engine on the data processing path and is configured to receive data from a first upstream data path entity and from a first data processing entity implemented in programmable circuitry via a data ingress interface of the first hub. The first data processing engine is configured to receive data from the first hub, process the received data and output the processed data to the second hub arranged downstream of first data processing engine.

Abstract: The present invention sets forth a technique for simulating wrinkles under dynamic facial expression. This technique includes sampling a plurality of nodes from a three-dimensional (3D) representation of a facial structure, wherein each node represents a pore in the facial structure. The technique also generates one or more edges, with each of the one or more edges connecting a node of the plurality of nodes to a different node selected from the plurality of nodes. The technique further generates a wrinkle graph comprising the plurality of nodes, the one or more edges, and a plurality of edge weights associated with the edges included in the wrinkle graph. The technique may also modify the 3D representation of the facial structure based on the wrinkle graph and one or more dynamic expressions associated with the 3D representation.

Abstract: Some implementations of the disclosure are directed to capturing facial training data for one or more subjects, the captured facial training data including each of the one or more subject's facial skin geometry tracked over a plurality of times and the subject's corresponding jaw poses for each of those plurality of times; and using the captured facial training data to create a model that provides a mapping from skin motion to jaw motion. Additional implementations of the disclosure are directed to determining a facial skin geometry of a subject; using a model that provides a mapping from skin motion to jaw motion to predict a motion of the subject's jaw from a rest pose given the facial skin geometry; and determining a jaw pose of the subject using the predicted motion of the subject's jaw.

Abstract: A computing device, including a hardware accelerator configured to receive a first matrix and receive a second matrix. The hardware accelerator may, for a plurality of partial matrix regions, in a first iteration, read a first submatrix of the first matrix and a second submatrix of the second matrix into a front-end processing area. The hardware accelerator may multiply the first submatrix by the second submatrix to compute a first intermediate partial matrix. In each of one or more subsequent iterations, the hardware accelerator may read an additional submatrix into the front end processing area. The hardware accelerator may compute an additional intermediate partial matrix as a product of the additional submatrix and a submatrix reused from an immediately prior iteration. The hardware accelerator may compute each partial matrix as a sum of two or more of the intermediate partial matrices and may output the plurality of partial matrices.