human-shape
Papers with tag human-shape
2022
- Human Body Measurement Estimation with Adversarial AugmentationNataniel Ruiz, Miriam Bellver, Timo Bolkart, Ambuj Arora, Ming C. Lin, Javier Romero, and Raja BalaIn 2022
We present a Body Measurement network (BMnet) for estimating 3Danthropomorphic measurements of the human body shape from silhouette images.Training of BMnet is performed on data from real human subjects, and augmentedwith a novel adversarial body simulator (ABS) that finds and synthesizeschallenging body shapes. ABS is based on the skinned multiperson linear (SMPL)body model, and aims to maximize BMnet measurement prediction error withrespect to latent SMPL shape parameters. ABS is fully differentiable withrespect to these parameters, and trained end-to-end via backpropagation withBMnet in the loop. Experiments show that ABS effectively discovers adversarialexamples, such as bodies with extreme body mass indices (BMI), consistent withthe rarity of extreme-BMI bodies in BMnet’s training set. Thus ABS is able toreveal gaps in training data and potential failures in predictingunder-represented body shapes. Results show that training BMnet with ABSimproves measurement prediction accuracy on real bodies by up to 10%, whencompared to no augmentation or random body shape sampling. Furthermore, ourmethod significantly outperforms SOTA measurement estimation methods by as muchas 3x. Finally, we release BodyM, the first challenging, large-scale dataset ofphoto silhouettes and body measurements of real human subjects, to furtherpromote research in this area. Project website:https://adversarialbodysim.github.io
human-shape@inproceedings{2210.05667, title = {Human Body Measurement Estimation with Adversarial Augmentation}, author = {Ruiz, Nataniel and Bellver, Miriam and Bolkart, Timo and Arora, Ambuj and Lin, Ming C. and Romero, Javier and Bala, Raja}, year = {2022}, tags = {human-shape}, }