Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction

• URL: http://arxiv.org/abs/2405.18700v2
• Date: Thu, 30 May 2024 00:46:08 GMT
• Title: Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction
• Authors: Xuehao Gao, Yang Yang, Yang Wu, Shaoyi Du, Guo-Jun Qi,
• Abstract summary: Real-world human movements are goal-directed and highly influenced by the spatial layout of their surrounding scenes. We propose a Multi-Condition Latent Diffusion network (MCLD) that reformulates the human motion prediction task as a multi-condition joint inference problem. Our network achieves significant improvements over the state-of-the-art methods on both realistic and diverse predictions.
• Score: 46.309401205546656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring 3D human motion is fundamental in many applications, including understanding human activity and analyzing one's intention. While many fruitful efforts have been made to human motion prediction, most approaches focus on pose-driven prediction and inferring human motion in isolation from the contextual environment, thus leaving the body location movement in the scene behind. However, real-world human movements are goal-directed and highly influenced by the spatial layout of their surrounding scenes. In this paper, instead of planning future human motion in a 'dark' room, we propose a Multi-Condition Latent Diffusion network (MCLD) that reformulates the human motion prediction task as a multi-condition joint inference problem based on the given historical 3D body motion and the current 3D scene contexts. Specifically, instead of directly modeling joint distribution over the raw motion sequences, MCLD performs a conditional diffusion process within the latent embedding space, characterizing the cross-modal mapping from the past body movement and current scene context condition embeddings to the future human motion embedding. Extensive experiments on large-scale human motion prediction datasets demonstrate that our MCLD achieves significant improvements over the state-of-the-art methods on both realistic and diverse predictions.

Related papers

• MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds [20.83684434910106]
  We present MoManifold, a novel human motion prior, which models plausible human motion in continuous high-dimensional motion space. Specifically, we propose novel decoupled joint acceleration to model human dynamics from existing limited motion data. Extensive experiments demonstrate that MoManifold outperforms existing SOTAs as a prior in several downstream tasks.
  arXiv  Detail & Related papers  (2024-09-01T15:00:16Z)
• Multimodal Sense-Informed Prediction of 3D Human Motions [16.71099574742631]
  This work introduces a novel multi-modal sense-informed motion prediction approach, which conditions high-fidelity generation on two modal information. The gaze information is regarded as the human intention, and combined with both motion and scene features, we construct a ternary intention-aware attention to supervise the generation. On two real-world benchmarks, the proposed method achieves state-of-the-art performance both in 3D human pose and trajectory prediction.
  arXiv  Detail & Related papers  (2024-05-05T12:38:10Z)
• Scene-aware Human Motion Forecasting via Mutual Distance Prediction [13.067687949642641]
  We propose to model the human-scene interaction with the mutual distance between the human body and the scene. Such mutual distances constrain both the local and global human motion, resulting in a whole-body motion constrained prediction. We develop a pipeline with two sequential steps: predicting the future mutual distances first, followed by forecasting future human motion.
  arXiv  Detail & Related papers  (2023-10-01T08:32:46Z)
• Executing your Commands via Motion Diffusion in Latent Space [51.64652463205012]
  We propose a Motion Latent-based Diffusion model (MLD) to produce vivid motion sequences conforming to the given conditional inputs. Our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks.
  arXiv  Detail & Related papers  (2022-12-08T03:07:00Z)
• GIMO: Gaze-Informed Human Motion Prediction in Context [75.52839760700833]
  We propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, and ego-centric views with eye gaze. Our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects. To realize the full potential of gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches.
  arXiv  Detail & Related papers  (2022-04-20T13:17:39Z)
• Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction [63.62263239934777]
  We conduct an indepth study on various pose representations with a focus on their effects on the motion prediction task. We propose a novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction. Our approach outperforms the state-of-the-art methods in short-term prediction and achieves much enhanced long-term prediction proficiency.
  arXiv  Detail & Related papers  (2021-12-30T10:45:22Z)
• Scene-aware Generative Network for Human Motion Synthesis [125.21079898942347]
  We propose a new framework, with the interaction between the scene and the human motion taken into account. Considering the uncertainty of human motion, we formulate this task as a generative task. We derive a GAN based learning approach, with discriminators to enforce the compatibility between the human motion and the contextual scene.
  arXiv  Detail & Related papers  (2021-05-31T09:05:50Z)
• Long-term Human Motion Prediction with Scene Context [60.096118270451974]
  We propose a novel three-stage framework for predicting human motion. Our method first samples multiple human motion goals, then plans 3D human paths towards each goal, and finally predicts 3D human pose sequences following each path.
  arXiv  Detail & Related papers  (2020-07-07T17:59:53Z)

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.