TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models

• URL: http://arxiv.org/abs/2312.00651v2
• Date: Wed, 20 Mar 2024 17:28:02 GMT
• Title: TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models
• Authors: Pengxiang Li, Kai Chen, Zhili Liu, Ruiyuan Gao, Lanqing Hong, Guo Zhou, Hua Yao, Dit-Yan Yeung, Huchuan Lu, Xu Jia,
• Abstract summary: 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.
• Score: 75.20168902300166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by the necessity to manage appearance and disappearance, drastic scale changes, and ensure consistency for instances across frames. These challenges hinder the development of video generation that can faithfully mimic real-world complexity, limiting utility for applications requiring high-level realism and controllability, including advanced scene simulation and training of perception systems. To address that, we propose TrackDiffusion, a novel video generation framework affording fine-grained trajectory-conditioned motion control via diffusion models, which facilitates the precise manipulation of the object trajectories and interactions, overcoming the prevalent limitation of scale and continuity disruptions. A pivotal component of TrackDiffusion is the instance enhancer, which explicitly ensures inter-frame consistency of multiple objects, a critical factor overlooked in the current literature. Moreover, we demonstrate that generated video sequences by our TrackDiffusion can be used as training data for visual perception models. To the best of our knowledge, this is the first work to apply video diffusion models with tracklet conditions and demonstrate that generated frames can be beneficial for improving the performance of object trackers.

Related papers

• DINTR: Tracking via Diffusion-based Interpolation [12.130669304428565]
  This work proposes a novel diffusion-based methodology to formulate the tracking task. Our INterpolation TrackeR (DINTR) presents a promising new paradigm and achieves a superior multiplicity on seven benchmarks across five indicator representations.
  arXiv  Detail & Related papers  (2024-10-14T00:41:58Z)
• E-Motion: Future Motion Simulation via Event Sequence Diffusion [86.80533612211502]
  Event-based sensors may potentially offer a unique opportunity to predict future motion with a level of detail and precision previously unachievable. We propose to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework. Our findings suggest a promising direction for future research in enhancing the interpretative power and predictive accuracy of computer vision systems.
  arXiv  Detail & Related papers  (2024-10-11T09:19:23Z)
• Video Diffusion Models are Training-free Motion Interpreter and Controller [20.361790608772157]
  This paper introduces a novel perspective to understand, localize, and manipulate motion-aware features in video diffusion models. We present a new MOtion FeaTure (MOFT) by eliminating content correlation information and filtering motion channels.
  arXiv  Detail & Related papers  (2024-05-23T17:59:40Z)
• Spectral Motion Alignment for Video Motion Transfer using Diffusion Models [54.32923808964701]
  Spectral Motion Alignment (SMA) is a framework that refines and aligns motion vectors using Fourier and wavelet transforms. SMA learns motion patterns by incorporating frequency-domain regularization, facilitating the learning of whole-frame global motion dynamics. Extensive experiments demonstrate SMA's efficacy in improving motion transfer while maintaining computational efficiency and compatibility across various video customization frameworks.
  arXiv  Detail & Related papers  (2024-03-22T14:47:18Z)
• Animate Your Motion: Turning Still Images into Dynamic Videos [58.63109848837741]
  We introduce Scene and Motion Conditional Diffusion (SMCD), a novel methodology for managing multimodal inputs. SMCD incorporates a recognized motion conditioning module and investigates various approaches to integrate scene conditions. Our design significantly enhances video quality, motion precision, and semantic coherence.
  arXiv  Detail & Related papers  (2024-03-15T10:36:24Z)
• Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object Video Generation [26.292052071093945]
  We propose an unsupervised method to generate videos from a single frame and a sparse motion input. Our trained model can generate unseen realistic object-to-object interactions. We show that YODA is on par with or better than state of the art video generation prior work in terms of both controllability and video quality.
  arXiv  Detail & Related papers  (2023-06-06T19:50:02Z)
• MotionTrack: Learning Motion Predictor for Multiple Object Tracking [68.68339102749358]
  We introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2023-06-05T04:24:11Z)
• Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion [83.88829943619656]
  We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. Our guided diffusion model allows users to constrain trajectories through target waypoints, speed, and specified social groups. We propose utilizing the value function learned during RL training of the animation controller to guide diffusion to produce trajectories better suited for particular scenarios.
  arXiv  Detail & Related papers  (2023-04-04T15:46:42Z)
• Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models [18.50942770933098]
  MoDiff is an autoregressive probabilistic diffusion model over motion sequences conditioned on control contexts of other modalities. Our model integrates a cross-modal Transformer encoder and a Transformer-based decoder, which are found effective in capturing temporal correlations in motion and control modalities.
  arXiv  Detail & Related papers  (2023-04-03T08:17:08Z)

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.