3D Human Mesh Estimation with Diffusion-Guided Refinement
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Solution Overview
Problem
Traditional methods for 3D human mesh recovery from 2D images face challenges such as low accuracy, computational expense, sensitivity to initialization, and instability in optimization, especially in monocular settings, and the integration of diffusion models has been limited in this context.
Innovation Solution
A hybrid approach using a regression network for an initial estimate and a diffusion model for refinement, guided by a score term, iteratively aligns the human model with observations through denoising and sampling in the latent space, leveraging a pre-trained diffusion model to capture human body parameter distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optimization-based methods are used to recover 3D human mesh from 2D images, then manufacturing precision (accuracy of 3D reconstruction) is improved, but productivity (computational speed) deteriorates
Solution Approach 1:
A regression network is used to generate an initial estimate of 3D human mesh parameters before optimization begins. This preliminary action provides a warm start that is already close to the optimal solution, significantly reducing the number of optimization iterations needed and thus improving computational speed while maintaining accuracy.
Solution Approach 2:
A differentiable renderer is introduced as an intermediary component that bridges the regression network output and the optimization process. This renderer enables gradient computation and allows the optimization to efficiently refine the initial estimate by computing gradients through the rendering pipeline, improving both speed and accuracy.
2Productivity
If regression-based methods are used to directly estimate 3D human mesh parameters from images, then productivity (computational speed) is improved, but manufacturing precision (alignment accuracy with input image) deteriorates
Solution Approach 1:
The patent merges regression-based methods and optimization-based methods into a unified framework. The regression network provides fast initial estimates, while the optimization process refines these estimates to achieve precise alignment with the input image. This combination achieves both speed and accuracy that neither method can achieve alone.
3Manufacturing precision
If traditional optimization methods are used for 3D human mesh recovery, then manufacturing precision (accuracy) is improved, but stability (sensitivity to initialization and local minima) deteriorates
Solution Approach 1:
The regression network performs a preliminary action by generating an initialization that is already close to the global optimum. This warm start dramatically reduces the risk of getting trapped in local minima and makes the optimization process much more stable and less sensitive to initialization parameters.
4Manufacturing precision
If diffusion models are integrated into 3D human mesh recovery, then manufacturing precision (alignment accuracy) is improved, but device complexity (model architecture complexity) increases
Solution Approach 1:
A differentiable renderer serves as an intermediary that connects the diffusion model with the 3D human mesh parameters. This renderer enables the diffusion model to operate in a simplified latent space while still achieving precise alignment through gradient-based refinement, managing complexity while improving accuracy.
Data Source
AI summary
Systems, methods, and apparatuses for estimating a three-dimensional (3D) object. One apparatus includes at least one electronic processor and at least one memory storing a machine learning model and instructions executable by the at least one electronic processor. The machine learning model trained to receive an initial estimate of a set of model parameters corresponding to the 3D object and generated using a regression model, based on an input image, perform denoising diffusion implicit model (DDIM) inversion on the initial estimate to obtain a latent representation, generate, using a diffusion model and a score guidance term, a refined latent representation by iteratively applying a guided sampling process, and generate a refined estimate of the set of model parameters based on the refined latent representation.