Roblox Corporation

The application describes a no coding integration approach for any web service by using a challenge interface as a middleware intermediate between a public application programming interface (API) and a challenge service so that any changes made in challenge protocols are implemented at the challenge interface. The challenge interface includes a rule engine that dynamically executes rules and evaluates suspicious signals from requests to automatically decide how many challenges and what kind of challenges are needed to satisfy a current request. The user interface provided to the user device automatically updates for different challenge types through an interceptor approach. The user interface works for all supported device types, including browsers, mobile devices, and native applications.

Implementations described herein relate to methods, systems, and computer-readable media to provide spatialized audio in virtual experiences. The spatialized audio may be used in voice communications such as, for example, voice and/or video chats. The chats may include spatialized audio that is combined at a client device, or at an online experience platform, and is targeted to a particular user. Individual audio streams may be collected from a plurality of avatars and other objects, and combined based on the target user. The audio may also include background and/or ambient sounds to provide a rich, immersive audio stream in virtual experiences.

Implementations described herein relate to methods, systems, and computer-readable media for digital content management. In some implementations, the method includes receiving, by a processor, an audio file, determining whether there is a match of a segment of the audio file with one or more reference audio files, if it is determined that there is no match, classifying the audio file as an authentic audio file, and if it is determined that there is the match, identifying, one or more designated audio segments that are semantically similar to the segment, and replacing the segment of the audio file with a particular designated audio segment of the one or more designated audio segments.

Implementations described herein relate to methods, systems, and computer-readable media for metaverse event sequencing. A method may include receiving a request to cue a scene associated with a metaverse place from a user device, the scene associated with a master timeline of events, wherein each event of the master timeline of events is associated with a respective time point on the master timeline, and wherein the request includes a first cue point. The method may also include retrieving scene data associated with the scene, identifying a set of events with time points that are at or after the first cue point, transitioning the master timeline to the first cue point, recreating the set of events based upon a current point in the master timeline after the transitioning and in a sequence based on the master timeline, and providing the recreated events for display at the user device.

Various implementations relate to methods, systems, and computer-readable media to dynamically apply creator signatures to virtual assets within a virtual platform. According to one aspect, a computer-implemented method includes receiving a request to display a virtual asset associated with a creator, where the request is linked to an avatar in a virtual environment hosted on the platform. A creator signature, which is defined by visual properties unique to the creator, is retrieved and stored separately from the virtual asset. The method includes rendering the virtual asset by rasterizing it and applying a fragment shader to overlay the creator signature as a dynamic visual element that changes over time based on predefined parameters. The rendered virtual asset with the dynamic signature is displayed within the virtual environment, providing visual attribution to the creator while preventing unauthorized embedding of the signature in the asset data.

Various implementations relate to methods, systems, and computer-readable media for detecting policy-violating content in user-generated avatar bodies on a virtual platform. According to one aspect, a computer-implemented method includes obtaining a user-generated avatar body with a 3D mesh and texture and providing the avatar body and a set of text questions to a multimodal machine-learned model. Each text question pertains to a characteristic relevant to content policy. The model generates probability values indicating the likelihood of each characteristic being present in the avatar body. A decision model evaluates the probability values to determine if the avatar body violates content policy. Based on the determination, usage of the avatar body on the virtual platform is controlled, including potential restrictions or flagging for manual review.

Various implementations relate to methods, computer-readable media, and to automatically insert a script in a virtual environment. In some implementations, a method includes receiving a natural language prompt from a user, the natural language prompt specifying one or more functional requirements for the script. The method further includes providing the natural language prompt, a default prompt, and context information to a large language model (LLM). The method further includes obtaining, as output of the LLM and in response to the providing, the script and an attachment location for the script, wherein the attachment location identifies a particular entity associated with the virtual environment. The method further includes inserting the script in the virtual environment at the attachment location, wherein the script is executable in the virtual environment after the inserting.

Implementations described herein relate to methods, systems, apparatuses, and computer-readable media to generate personalized avatars for a user, based on one or more 2D images of a face. The automatic generation may be facilitated by a generative component deployed at a virtual experience server and that is configured to generate a data structure representing a 3D mesh of the personalized avatar responsive to input of the one or more 2D images. The data structure may be converted to a polygonal mesh, and the polygonal mesh may be automatically fit and rigged onto a head portion of an avatar data model.

According to one aspect of the present disclosure, a method is provided. The method may include receiving a visual asset. The method may include receiving an indicator of a visual style. The method may include identifying a set of distinctive-style features for the visual style using a loss function calculated based on a first summation of differences among a first set of Gram Matrix pairs and a second summation of differences among a second set of Gram Matrix pairs. The method may include modifying a geometry of the visual asset to match the set of distinctive-style features identified for the visual style. After the modifying, the method may include rendering the visual asset that matches the set of distinctive-style features.

Implementations described herein relate to methods, systems, and computer-readable media to display a virtual character within a virtual environment on a display device. In some implementations, a method can include obtaining an input pose of the virtual character, wherein the virtual character is based on a rig that comprises a plurality of joints, receiving an indication of one or more of a position and an orientation of a target end effector located on the rig, determining an output pose for the rig wherein the determining comprises calculating a respective orientation and position of one or more joints of the plurality of joints of the rig based on the position of the target end effector and rotation constraints of a plurality of joints of a reference rig, and displaying the virtual character in the output pose on the display device.