3D Crowd Reconstruction With Group Motion Recovery for Occlusions
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Solution Overview
Problem
Existing methods struggle to achieve coherent, realistic, and occlusion-robust dynamic 3D crowd reconstruction from large-scene videos due to spatial and temporal discontinuities caused by high density and frequent occlusions, leading to inaccurate and unsmooth motion in large-scene environments.
Innovation Solution
A method involving adaptive image cropping, crowd grouping optimization, asynchronous motion consistency loss, and a human motion prior model using a variational autoencoder to enhance the reconstruction process, addressing occlusions and improving temporal coherence and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing single-person dynamic reconstruction methods are used, then local position tracking is achieved, but temporal stability and motion smoothness deteriorate due to weak perspective projection assumptions and loss of key position information
Solution Approach 1:
The patent merges multiple single-person reconstruction results into a unified crowd reconstruction framework. By combining detection results, tracking information, and 3D pose estimates from multiple individuals while considering their spatial relationships, the system achieves both accurate local position tracking and temporally stable 3D motion reconstruction for the entire crowd.
Solution Approach 2:
The patent implements feedback mechanisms where tracking results from previous frames inform current frame reconstruction, and 3D pose estimates are refined iteratively using temporal consistency constraints. This feedback loop ensures that key position information is preserved and temporal stability is maintained while achieving accurate local position tracking.
2Productivity
If end-to-end methods are used to estimate SMPL models, then reconstruction speed is improved, but medium and small individuals are lost due to scaling large-scene images to fit network input resolution
Solution Approach 1:
The patent segments the large-scene image into multiple smaller patches or regions, each processed independently by the end-to-end SMPL estimation network. This segmentation allows medium and small individuals to be captured in at least one patch with sufficient resolution, preventing information loss while maintaining fast reconstruction speed through parallel processing of multiple patches.
Solution Approach 2:
The patent transitions from processing the entire large-scene image in a single dimension to processing multiple smaller patches across spatial dimensions. This dimensional transformation enables the network to maintain high input resolution for each patch, preserving details of medium and small individuals while achieving fast end-to-end reconstruction through efficient parallel computation.
3Quantity of substance
If Crowd3D or GroupRec methods are used, then 3D positions, poses, and shapes of hundreds of individuals are reconstructed from a single image, but temporal coherence deteriorates when applied to video frames due to heavy or complete occlusion causing object loss and unstable motion
Solution Approach 1:
The patent ensures continuity of useful action by implementing temporal smoothing and motion prediction mechanisms that maintain coherent 3D motion across video frames. When occlusion causes object loss in individual frames, the system uses temporal information from adjacent frames and motion models to predict and restore missing data, ensuring continuous and stable 3D reconstruction of all hundreds of individuals throughout the video sequence.
Solution Approach 2:
The patent applies beforehand cushioning by using temporal information from previous and future frames to compensate for occlusion-induced data loss in the current frame. Motion prediction models and temporal smoothing techniques are prepared in advance to cushion against the destabilizing effect of occlusions, maintaining temporal coherence even when heavy or complete occlusion causes object loss in individual frames.
4Area of stationary object
If high density of moving individuals and frequent occlusion events are present, then scene coverage is improved, but reconstruction accuracy deteriorates due to spatial and temporal discontinuities
Solution Approach 1:
The patent segments the dense crowd into multiple local groups or clusters based on spatial proximity and motion similarity. This segmentation reduces the complexity of reconstructing hundreds of individuals simultaneously, allowing the system to maintain high measurement precision within each local group while achieving comprehensive scene coverage through the aggregation of all segmented results.
Solution Approach 2:
The patent introduces tracking information and motion models as intermediary elements that bridge spatial and temporal discontinuities caused by high density and frequent occlusions. These intermediaries provide continuous motion constraints and spatial relationships, enabling the system to maintain high reconstruction accuracy across the entire densely populated scene by connecting local reconstructions through temporal and spatial consistency.
Data Source
AI summary
This invention focuses on the 3D reconstruction of dynamic crowds in large-scene videos and introduces the DyCrowd framework, which reconstructs 3D position, pose, and shape of hundreds of people from a large-scene video. Our approach addresses frequent occlusions and modeling challenges in high-density crowds through a top-down strategy. This includes pre-reconstruction, matching individual movement sequences, and multi-stage iterative optimization. During the optimization process, we introduce a group optimization method with an asynchronous motion consistency loss. This method clusters individuals with similar trajectories, using high-quality and unoccluded movements within the group to guide the recovery of occluded individuals, thereby mitigating long-term occlusion issues. Furthermore, to address the lack of ground-truth human reconstruction labels in current large-scene datasets, we introduce a virtual benchmark dataset called VirtualCrowd for dynamic crowd reconstruction in large-scene videos.