Multi-object tracking (MOT) aims to estimate the bounding boxes and ID labels of objects in videos. The challenging issue in this task is to alleviate competitive learning between the detection and tracking subtasks, for which, two-stage Tracking-By-Detection (TBD) optimizes the two subtasks individually, and the single-stage Joint Detection and Tracking (JDT) adjusts the complex network architectures finely in an end-to-end pipeline. In this paper, we propose a new MOT method, i.e., Proposal Propagation via Diffusion Models, called Pro2Diff, which integrates a diffusion model into the proposal propagation in multi-object tracking, focusing on the model training process rather than complex network design. Specifically, using a generative approach, Pro2Diff generates a considerable number of noisy proposals for the tracking image sequence in the forward process, and subsequently, Pro2Diff learns the discrepancies between these noisy proposals and the actual bounding boxes of the tracked objects, gradually optimizing these noisy proposals to obtain the initial sequence of real tracked objects. By introducing the denoising diffusion process into multi-object tracking, we have made three further important findings: 1) Generative methods can effectively handle multi-object tracking tasks; 2) Without the need to modify the model structure, we propose self-conditional proposal propagation to enhance model performance effectively during inference; 3) By adjusting the numbers of proposals and iterations appropriately for different tracking sequences, the optimal performance of the model can be achieved. Extensive experimental results on MOT17 and DanceTrack datasets demonstrate that Pro2Diff outperforms current end-to-end multi-object tracking methods. We achieve 61.9 HOTA on DanceTrack and 57.6 HOTA on MOT17, reaching the competitive result of the JDT approach.

We introduce GS-SFS, a method that utilizes a camera array with wide baselines for high-quality multiple human mesh reconstruction in large-scale sports scenes. Traditional human reconstruction methods in sports scenes, such as Shape-from-Silhouette (SFS), struggle with sparse camera setups and small human targets, making it challenging to obtain complete and accurate human representations. Despite advances in differentiable rendering, including 3D Gaussian Splatting (3DGS), which can produce photorealistic novel-view renderings with dense inputs, accurate depiction of surfaces and generation of detailed meshes is still challenging. Our approach uniquely combines 3DGS's view synthesis with an optimized SFS method, thereby significantly enhancing the quality of multiperson mesh reconstruction in large-scale sports scenes. Specifically, we introduce body shape priors, including the human surface point clouds extracted through SFS and human silhouettes, to constrain 3DGS to a more accurate representation of the human body only. Then, we develop an improved mesh reconstruction method based on SFS, mainly by adding additional viewpoints through 3DGS and obtaining a more accurate surface to achieve higher-quality reconstruction models. We implement a high-density scene resampling strategy based on spherical sampling of human bounding boxes and render new perspectives using 3D Gaussian Splatting to create precise and dense multi-view human silhouettes. During mesh reconstruction, we integrate the human body's 2D Signed Distance Function (SDF) into the computation of the SFS's implicit surface field, resulting in smoother and more accurate surfaces. Moreover, we enhance mesh texture mapping by blending original and rendered images with different weights, preserving high-quality textures while compensating for missing details. The experimental results from real basketball game scenarios demonstrate the significant improvements of our approach for multiple human body model reconstruction in complex sports settings.