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Cheating is widespread in online games, particularly in competitive games, such as first-person shooter (FPS) games. One of the most common types of cheating is extrasensory perception (ESP), which involves illicitly obtaining visual information to gain an unfair advantage over normal players. To protect the gaming experience of legitimate players and the interests of game companies, there is an urgent need for anticheating applications. In this article, we propose a general system for ESP anticheating in online FPS games, considering the business characteristics and industrial applications. We present a vision-based anticheating framework that incorporates both supervised and unsupervised solutions for comprehensive cheating detection. Based on this framework, we design and deploy a dual-audit human-in-the-loop system for industrial gaming anticheating applications. We evaluate our proposed framework from multiple online and offline perspectives and demonstrate its practical significance with superior performance.

Individuals have unique facial expression and head pose styles that reflect their personalized speaking styles. Existing one-shot talking head methods cannot capture such personalized characteristics and therefore fail to produce diverse speaking styles in the final videos. To address this challenge, we propose a one-shot style-controllable talking face generation method that can obtain speaking styles from reference speaking videos and drive the one-shot portrait to speak with the reference speaking styles and another piece of audio. Our method aims to synthesize the style-controllable coefficients of a 3D Morphable Model (3DMM), including facial expressions and head movements, in a unified framework. Specifically, the proposed framework first leverages a style encoder to extract the desired speaking styles from the reference videos and transform them into style codes. Then, the framework uses a style-aware decoder to synthesize the coefficients of 3DMM from the audio input and style codes. During decoding, our framework adopts a two-branch architecture, which generates the stylized facial expression coefficients and stylized head movement coefficients, respectively. After obtaining the coefficients of 3DMM, an image renderer renders the expression coefficients into a specific person's talking-head video. Extensive experiments demonstrate that our method generates visually authentic talking head videos with diverse speaking styles from only one portrait image and an audio clip.