Method for rendering video images in vr scenes

An electronic device, comprising:

• memory storing one or more computer programs;
• and
• one or more processors communicatively coupled to the memory,
• wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors, cause the electronic device to:
• provide a video image at a current time point,
• divide the video image at the current time point into a plurality of sub-regions, inputting image feature information of the sub-regions and acquired user viewpoint feature information into a trained attention model for processing to obtain attention coefficients of the sub-regions indicating probability values at which user viewpoints at a next time point fall into the sub-regions,
• render the sub-regions based on the attention coefficients of the sub-regions to obtain a rendered video image at the current time point,
• input the attention coefficients of the sub-regions and the image feature information of the sub-regions into a trained user eyes trajectory prediction model for processing,
• obtain user eyes trajectory information in a current time period, and
• divide, for video images at subsequent time points within the current time period, the video images at the subsequent time points into a plurality of sub-regions, calculating attention coefficients of the sub-regions in a video image at each of the subsequent time points within the current time period respectively based on the user eyes trajectory information in the current time period, and
• render the corresponding sub-regions based on the attention coefficients of the sub-regions to obtain a rendered video image at each of the subsequent time points.

A system for rendering video images in virtual reality (VR) scenes, the system comprising:

• a processing unit for video image at current time point;
• a user eyes trajectory information prediction unit;
• and
• a processing unit for video image in current time period,
• wherein the processing unit for video image at current time point is configured to:
• provide a video image at a current time point, divide the video image at the current time point into a plurality of sub-regions,
• input image feature information of the sub-regions and acquired user viewpoint feature information into a trained attention model for processing to obtain attention coefficients of the sub-regions indicating probability values at which user viewpoints at a next time point fall into the sub-regions, and
• render the sub-regions based on the attention coefficients of the sub-regions to obtain a rendered video image at the current time point,
• wherein the user eyes trajectory information prediction unit is configured to:
• input the attention coefficients of the sub-regions and the image feature information of the sub-regions into a trained user eyes trajectory prediction model for processing, and
• obtain user eyes trajectory information in a current time period, and
• wherein the processing unit for video image in current time period is configured to:
• divide, for video images at subsequent time points within the current time period, the video images at the subsequent time points into a plurality of sub-regions,
• calculate attention coefficients of the sub-regions in a video image at each of the subsequent time points within the current time period respectively based on the user eyes trajectory information in the current time period, and
• render the corresponding sub-regions based on the attention coefficients of the sub-regions to obtain a rendered video image at each of the subsequent time points.

One or more non-transitory computer-readable storage media storing one or more computer programs including computer-executable instructions that, when executed by one or more processors of an electronic device, cause the electronic device to perform operations, the operations comprising:

• providing a video image at a current time point;
• dividing the video image at the current time point into a plurality of sub-regions;
• inputting image feature information of the sub-regions and acquired user viewpoint feature information into a trained attention model for processing to obtain attention coefficients of the sub-regions indicating probability values at which user viewpoints at a next time point fall into the sub-regions;
• rendering the sub-regions based on the attention coefficients of the sub-regions to obtain a rendered video image at the current time point;
• inputting the attention coefficients of the sub-regions and the image feature information of the sub-regions into a trained user eyes trajectory prediction model for processing;
• obtaining user eyes trajectory information in a current time period;
• dividing, for video images at subsequent time points within the current time period, the video images at the subsequent time points into a plurality of sub-regions, calculating attention coefficients of the sub-regions in a video image at each of the subsequent time points within the current time period respectively based on the user eyes trajectory information in the current time period;
• and
• rendering the corresponding sub-regions based on the attention coefficients of the sub-regions to obtain a rendered video image at each of the subsequent time points.