JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation

• URL: http://arxiv.org/abs/2509.22522v1
• Date: Fri, 26 Sep 2025 16:04:00 GMT
• Title: JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
• Authors: Guillem Capellera, Luis Ferraz, Antonio Rubio, Alexandre Alahi, Antonio Agudo,
• Abstract summary: Generative models often treat continuous data and discrete events as separate processes, creating a gap in modeling complex systems where they unify synchronously.<n>To bridge this gap, we introduce JointDiff, a novel diffusion framework designed to interact these two processes by simultaneously generating continuous-temporal data and synchronous discrete events.<n>JointDiff achieves state-of-the-art performance, demonstrating that joint modeling is crucial for building realistic and controllable models for interactive systems.
• Score: 75.58351043849385
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generative models often treat continuous data and discrete events as separate processes, creating a gap in modeling complex systems where they interact synchronously. To bridge this gap, we introduce JointDiff, a novel diffusion framework designed to unify these two processes by simultaneously generating continuous spatio-temporal data and synchronous discrete events. We demonstrate its efficacy in the sports domain by simultaneously modeling multi-agent trajectories and key possession events. This joint modeling is validated with non-controllable generation and two novel controllable generation scenarios: weak-possessor-guidance, which offers flexible semantic control over game dynamics through a simple list of intended ball possessors, and text-guidance, which enables fine-grained, language-driven generation. To enable the conditioning with these guidance signals, we introduce CrossGuid, an effective conditioning operation for multi-agent domains. We also share a new unified sports benchmark enhanced with textual descriptions for soccer and football datasets. JointDiff achieves state-of-the-art performance, demonstrating that joint modeling is crucial for building realistic and controllable generative models for interactive systems.

Related papers

• Diffusion Forcing for Multi-Agent Interaction Sequence Modeling [52.769202433667125]
  MAGNet is a unified autoregressive diffusion framework for multi-agent motion generation.<n>It supports a wide range of interaction tasks through flexible conditioning and sampling.<n>It captures both tightly synchronized activities and loosely structured social interactions.
  arXiv  Detail & Related papers  (2025-12-19T18:59:02Z)
• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching [64.10695425442164]
  We introduce NExT-OMNI, an open-source omnimodal foundation model that achieves unified modeling through discrete flow paradigms.<n>Trained on large-scale interleaved text, image, video, and audio data, NExT-OMNI delivers competitive performance on multimodal generation and understanding benchmarks.<n>To advance further research, we release training details, data protocols, and open-source both the code and model checkpoints.
  arXiv  Detail & Related papers  (2025-10-15T16:25:18Z)
• Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production [0.0]
  We develop a hybrid approach that combines autoregressive and diffusion models for Sign Language Production (SLP)<n>To capture fine-grained body movements, we design a Multi-Scale Pose Representation module that separately extracts detailed features from distinct articulators.<n>We introduce a Confidence-Aware Causal Attention mechanism that utilizes joint-level confidence scores to dynamically guide the pose generation process.
  arXiv  Detail & Related papers  (2025-07-12T01:34:50Z)
• Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces [10.85468238780625]
  We propose a novel framework for building multimodal diffusion models on arbitrary state spaces.<n>By introducing an innovative decoupled noise schedule for each modality, we enable both unconditional and modality-conditioned generation within a single model simultaneously.
  arXiv  Detail & Related papers  (2025-06-09T16:20:20Z)
• LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation [94.84458417662404]
  LangTraj is a language-conditioned scene-diffusion model that simulates the joint behavior of all agents in traffic scenarios.<n>By conditioning on natural language inputs, LangTraj provides flexible and intuitive control over interactive behaviors.<n>LangTraj demonstrates strong performance in realism, language controllability, and language-conditioned safety-critical simulation.
  arXiv  Detail & Related papers  (2025-04-15T17:14:06Z)
• Multi-Resolution Generative Modeling of Human Motion from Limited Data [3.5229503563299915]
  We present a generative model that learns to synthesize human motion from limited training sequences. The model adeptly captures human motion patterns by integrating skeletal convolution layers and a multi-scale architecture.
  arXiv  Detail & Related papers  (2024-11-25T15:36:29Z)
• Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs.<n>Specifically, we introduce a Ghost Spatial Masking (GSM) module, embedded within a Transformer encoder, for spatial feature extraction.<n>We benchmark three practical sports datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models [75.20168902300166]
  We propose TrackDiffusion, a novel video generation framework affording fine-grained trajectory-conditioned motion control. A pivotal component of TrackDiffusion is the instance enhancer, which explicitly ensures inter-frame consistency of multiple objects. generated video sequences by our TrackDiffusion can be used as training data for visual perception models.
  arXiv  Detail & Related papers  (2023-12-01T15:24:38Z)
• Collaborative Diffusion for Multi-Modal Face Generation and Editing [34.16906110777047]
  We present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. Specifically, we propose dynamic diffuser, a meta-network that adaptively hallucinates multi-modal denoising steps by predicting the spatial-temporal influence functions for each pre-trained uni-modal model.
  arXiv  Detail & Related papers  (2023-04-20T17:59:02Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• Unified Discrete Diffusion for Simultaneous Vision-Language Generation [78.21352271140472]
  We present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix. Our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks.
  arXiv  Detail & Related papers  (2022-11-27T14:46:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.