Meta-Learned Initialization For 3D Human Recovery

We propose a novel framework for 3D human recovery from a single image that integrates meta-learning with test-time optimization. Compared to previous optimization-based or learning-based methods that showed limited generalization ability, the test-time optimization framework enables us to estimate an optimal network parameter, which yields better performance and generalization ability, but it is highly sensitive to an initial network parameter. To alleviate this, we present a meta-learning framework to learn better initial parameters for test-time optimization. Experimental results on standard benchmarks show that the proposed framework boost the test-time optimization performance compared to state-of-the-arts.