Global Human and Camera Motion Estimation With COIN Diffusion
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Solution Overview
Problem
Conventional methods for estimating global human and camera motion from dynamic RGB videos fail to maintain consistency with input video observations, especially in complex environments, due to the reliance on regression models that ignore camera movements and physics-based methods that are limited to controlled scenarios, leading to inaccurate and inconsistent motion estimation.
Innovation Solution
A hybrid Control-Inpainting (COIN) score distillation sampling (SDS) algorithm that utilizes a motion diffusion model with a control branch to enforce consistency and accuracy in motion estimation by using a controlled motion denoiser and a COIN system, incorporating a human-scene relation loss to align human and camera motions with observed evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If regression models are used to estimate global orientation and trajectory from local body movements, then the estimation process is simplified, but the model fails to maintain consistency with input video observations because it ignores camera movements
Solution Approach 1:
The patent merges regression models with physics-based methods into a hybrid approach. The regression model estimates local body movements while the physics-based component models camera movements and their impact on observed motion, combining both to maintain consistency with video observations while preserving computational efficiency
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between the regression model and video observations. This intermediary models the camera motion and transforms the regression model's local body movement estimates into global motion estimates that account for camera movements, thereby maintaining consistency with input video observations
2Reliability
If physics-based methods are used to estimate motion, then the model can account for camera movements, but it fails to model complex in-the-wild environments and is limited to controlled scenarios
Solution Approach 1:
The patent creates a composite estimation approach by combining regression models (which work well in controlled scenarios) with physics-based methods. The regression model handles predictable motion patterns while the physics-based component handles camera movements, creating a robust system that adapts to both controlled and in-the-wild environments
Solution Approach 2:
The patent makes the estimation system dynamic by allowing it to adapt between regression-based and physics-based approaches depending on the environment. In controlled scenarios, the regression model dominates, while in complex in-the-wild environments, the physics-based component becomes more influential, enabling the system to handle diverse conditions
3Stability of the object's composition
If motion prior models are used to constrain human body motion in latent space, then the motion reconstruction is smooth, but the reconstructed motions do not align well with video observations
Solution Approach 1:
The patent applies partial constraint from motion prior models rather than full constraint. Instead of completely constraining motion to the latent space of motion priors, the system partially incorporates these priors to provide smoothness while allowing deviations when video observations indicate different motion patterns, achieving a balance between smoothness and observation alignment
4Device complexity
If camera motion optimization is based only on global human motion from motion prior, then the optimization is simplified, but the system fails catastrophically when initial human motion predictions are significantly incorrect
Solution Approach 1:
The patent implements feedback mechanisms where video observations continuously inform both human motion estimation and camera motion optimization. The system uses observed video data to correct and refine initial predictions iteratively, providing feedback loops that prevent catastrophic failure when initial predictions are incorrect by constantly comparing predictions with actual observations and adjusting accordingly
Data Source
AI summary
Systems and methods are disclosed that perform global human and camera motion estimation using a motion diffusion model that is attached to a control branch. For instance, using a controlled motion denoiser that comprises the motion diffusion model and the control branch, global human motions and the corresponding camera motions from “in-the-wild” videos may be estimated. Initially, SLAM may be used to initialize the camera motion and a pose estimation model may be used to estimate the local human motion. Combining the two, embodiments of the present disclosure initialize the global human motion. Then, during optimization and using a COIN system that includes the controlled motion denoiser and/or using a COIN algorithm, embodiments of the present disclosure enforce the global human and camera motion to satisfy a two-dimensional (2D) projection on videos and the motion distribution from the motion diffusion model.