Building individualization is a critical preprocessing step for refined applications of oblique photogrammetry 3D models, yet existing semantic segmentation methods encounter accuracy bottlenecks when applied to ultra-high-resolution orthophotos. To overcome this challenge, this study constructs an automated technical framework following a workflow from orthophoto generation to high-precision semantic segmentation, and finally to dynamic 3D rendering. The framework comprises three stages: (1) converting the 3D model into a 2D orthophoto to ensure that the extracted building contours can be precisely registered with the original 3D model in space; (2) utilizing the proposed Gated-ASPP High-Resolution Network (GA-HRNet) to extract building contours, enhancing segmentation accuracy by synergizing HRNet’s spatial detail preservation capability with ASPP’s multi-scale context awareness; (3) mapping the extracted 2D vector contours back to the 3D model and achieving interactive building individualization via dynamic rendering technology. Evaluated on a custom-built Hong Kong urban building dataset, GA-HRNet achieved an Intersection over Union (IoU) of 91.25%, an F1-Score of 95.41%, a Precision of 93.31%, and a Recall of 97.70%. Its performance surpassed that of various comparative models, including FCN, U-Net, MBR-HRNet, and others, with an IoU lead of 1.46 to 5.62 percentage points. This method enables precise building extraction and dynamic highlighting within 3D scenes, providing an efficient and reliable technical path for the refined application of large-scale urban oblique photogrammetry models.