Broadcast-Footage Player Trajectories With Multimodal Diffusion
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Solution Overview
Problem
Conventional systems for modeling agent behaviors in sports, such as soccer, are limited by their focus on short-term context windows and reliance on trajectory data alone, failing to leverage auxiliary data streams for reconstructing long-term noise and providing photorealistic multi-agent trajectories.
Innovation Solution
A system combining a multimodal model with a diffusion model to generate trajectories from broadcast footage, incorporating both soccer tracking data and event data, using spatiotemporal axial attention to enhance the realism of multi-agent behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional systems use short-term context windows (≤10 seconds) for modeling agent behaviors, then the system complexity is reduced and processing speed is improved, but the ability to reconstruct long-term noise and provide photorealistic multi-agent trajectories deteriorates
Solution Approach 1:
The patent segments the trajectory reconstruction task into two distinct stages: (1) a multimodal transformer model that processes long-term context (up to 60 seconds) to capture global patterns and noise characteristics, and (2) a diffusion model that refines trajectories at shorter time scales. This segmentation allows each component to specialize - the transformer handles long-term contextual accuracy while the diffusion model optimizes for photorealistic detail, resolving the contradiction between processing speed and trajectory accuracy.
Solution Approach 2:
The patent introduces a temporal dimension extension by processing context windows up to 60 seconds instead of the conventional 10 seconds. This dimensional change in time scope enables the model to capture long-term noise patterns and contextual dependencies that are invisible in short-term windows, thereby improving trajectory reconstruction accuracy without sacrificing the efficiency of the diffusion sampling process.
2Device complexity
If conventional systems rely exclusively on trajectory data, then the data processing complexity is reduced, but the ability to leverage auxiliary data streams and provide comprehensive context deteriorates
Solution Approach 1:
The patent merges multiple data streams including broadcast footage, tracking data, and event data into a unified multimodal input for the transformer model. This combination allows the system to leverage complementary information from each source - visual context from footage, positional data from tracking, and semantic events - creating a comprehensive contextual representation that improves trajectory reconstruction while the model architecture manages the processing complexity efficiently.
3Use of energy by moving object
If conventional systems focus on short-term context windows, then computational resources are conserved, but the ability to model long-term behaviors and noise patterns deteriorates
Solution Approach 1:
The patent segments the computational workload by assigning long-term context processing (60-second windows) to the transformer model which is optimized for contextual understanding, while the diffusion model handles the computationally intensive trajectory refinement at higher temporal resolution. This segmentation allows the system to model long-term behaviors accurately without requiring the entire system to operate at high computational cost for all time scales.
Solution Approach 2:
The patent applies partial action by using the transformer model to process only the necessary long-term contextual features rather than complete trajectory details at all time scales. The diffusion model then applies excessive action by performing multiple sampling steps to refine trajectories, concentrating computational resources where they most impact photorealism while maintaining efficiency in the overall pipeline.
4Ease of manufacture
If conventional systems use traditional modeling approaches, then implementation simplicity is maintained, but the ability to generate photorealistic trajectories and capture fine-grained behaviors deteriorates
Solution Approach 1:
The patent introduces the diffusion model as an intermediary between the transformer's contextual understanding and the final trajectory output. This intermediary component specializes in generating photorealistic trajectories by modeling the conditional distribution of agent behaviors, capturing fine-grained details that traditional deterministic models miss. The diffusion intermediary bridges the gap between simple contextual processing and complex trajectory generation, achieving high photorealism while maintaining a relatively clean system architecture.
Data Source
AI summary
Systems and methods for generating trajectories for one or more players during an event include receiving broadcast footage of a sporting event, determining tracking data of one or more players in the sporting event from the broadcast footage, the tracking data including one or more vectors, receiving event data of the sporting event, and inputting the one or more vectors and event data into a multimodal model including an event encoder and a tracking decoder. A linear layer of the multimodal model may be applied to the vectors and event data to tokenize the event data and vectors. A tensor representing a sequence of the event data and tracking data may be determined. Perturbed tracking data of the sporting event and the tensor may be input into a diffusion model. The diffusion model may generate one or more trajectories for the one or more players in the sporting event.