Humans navigate the social world by rapidly perceiving social features from other people and their interaction. Recently, large-language models (LLMs) have achieved high-level visual capabilities for detailed object and scene content recognition and description. This raises the question whether LLMs can infer complex social information from images and videos, and whether the high-dimensional structure of the feature annotations aligns with that of humans. We collected evaluations for 138 social features from GPT-4V for images (N = 468) and videos (N = 234) that are derived from social movie scenes. These evaluations were compared with human evaluations (N = 2,254). The comparisons established that GPT-4V can achieve human-like capabilities at annotating individual social features. The GPT-4V social feature annotations also express similar structural representation compared to the human social perceptual structure (i.e., similar correlation matrix over all social feature annotations). Finally, we modeled hemodynamic responses (N = 97) to viewing socioemotional movie clips with feature annotations by human observers and GPT-4V. These results demonstrated that GPT-4V based stimulus models can also reveal the social perceptual network in the human brain highly similar to the stimulus models based on human annotations. These human-like annotation capabilities of LLMs could have a wide range of real-life applications ranging from health care to business and would open exciting new avenues for psychological and neuroscientific research.