Learned Vertex Descent: a new direction for 3D human model fitting

Conference Article


European Conference on Computer Vision (ECCV)





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We propose a novel optimization-based paradigm for 3D human model fitting on images and scans. In contrast to existing approaches that directly regress the parameters of a low-dimensional statistical body model (e.g. SMPL) from input images, we train an ensemble of per vertex neural fields network. The network predicts, in a distributed manner, the vertex descent direction towards the ground truth, based on neural features extracted at the current vertex projection. At inference, we employ this network, dubbed LVD, within a gradient-descent optimization pipeline until its convergence, which typically occurs in a fraction of a second even when initializing all vertices into a single point. An exhaustive evaluation demonstrates that our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art. LVD is also applicable to 3D model fitting of humans and hands, for which we show a significant improvement to the SOTA with a much simpler and faster method.


computer vision.

Scientific reference

E. Corona, G. Pons-Moll, G. Alenyà and F. Moreno-Noguer. Learned Vertex Descent: a new direction for 3D human model fitting, 17th European Conference on Computer Vision, 2022, Tel Aviv (Israel), in Computer Vision – ECCV 2022 , Vol 13666 of Lecture Notes in Computer Science, pp. 146--165, 2022.