Project demo

Reconstructs and visualizes complete game state sequences from recorded replay data, enabling frame-by-frame or accelerated playback of game events with spatial positioning and player actions. The system parses structured game logs (likely JSON or binary format) and renders them as interactive visual timelines, allowing inspection of specific moments and state transitions that occurred during gameplay.

Analyzes game replay data to identify anomalous player behavior patterns that deviate from expected gameplay norms, using statistical or heuristic-based detection rules. The system evaluates metrics like reaction time, aim accuracy, movement patterns, and decision-making consistency against baseline models or rule sets, then flags suspicious moments with confidence scores and detailed reasoning for human review.

Provides frame-accurate seeking and playback control over game replays through an interactive timeline UI, allowing users to jump to specific timestamps, adjust playback speed, and pause on individual frames. The implementation uses efficient data indexing (likely keyframe-based) to enable sub-second seek latency without re-parsing entire replay files, with synchronized visualization updates.

Enables dynamic camera perspective switching during replay playback to view the same game moment from different players' viewpoints, reconstructing each player's local game state and visible information. The system maintains separate render contexts for each player perspective, respecting fog-of-war and information visibility rules to show only what each player could have known at that moment.

Generates structured reports and exportable data artifacts from analyzed replays, including suspicion findings, event timelines, and statistical summaries in multiple formats (JSON, CSV, PDF). The system aggregates analysis results with metadata (player info, match context, detection confidence) and produces human-readable documents suitable for moderation decisions, appeals, or archival.