MEGA: Masked Generative Autoencoder for Human Mesh Recovery

• URL: http://arxiv.org/abs/2405.18839v3
• Date: Thu, 14 Nov 2024 10:27:51 GMT
• Title: MEGA: Masked Generative Autoencoder for Human Mesh Recovery
• Authors: Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda, Francesc Moreno-Noguer,
• Abstract summary: Human Mesh Recovery from a single RGB image is a highly ambiguous problem. Most HMR methods overlook this issue and make a single prediction without accounting for this ambiguity. This work proposes a new approach based on masked generative modeling.
• Score: 33.26995842920877
• License:
• Abstract: Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous problem, as an infinite set of 3D interpretations can explain the 2D observation equally well. Nevertheless, most HMR methods overlook this issue and make a single prediction without accounting for this ambiguity. A few approaches generate a distribution of human meshes, enabling the sampling of multiple predictions; however, none of them is competitive with the latest single-output model when making a single prediction. This work proposes a new approach based on masked generative modeling. By tokenizing the human pose and shape, we formulate the HMR task as generating a sequence of discrete tokens conditioned on an input image. We introduce MEGA, a MaskEd Generative Autoencoder trained to recover human meshes from images and partial human mesh token sequences. Given an image, our flexible generation scheme allows us to predict a single human mesh in deterministic mode or to generate multiple human meshes in stochastic mode. Experiments on in-the-wild benchmarks show that MEGA achieves state-of-the-art performance in deterministic and stochastic modes, outperforming single-output and multi-output approaches.

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