MÜLLER LEA (DE)
CHOUTAS VASSILIS (DE)
TZIONAS DIMITRIOS (DE)
HUANG CHUN-HAO PAUL (DE)
WO2020156627A1 | 2020-08-06 |
US20210232924A1 | 2021-07-29 | |||
US10818062B2 | 2020-10-27 |
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CLAIMS 1. A method for training a machine learning model for estimating shapes of objects based on sensor data, the method comprising: - obtaining a training dataset comprising training sensor data and a corresponding ground truth attribute, - estimating, by the machine learning model, a shape for the training sensor data, - determining an attribute corresponding to the estimated shape, and - optimizing the machine learning model using a loss function that is based on a difference of the determined attribute compared to the ground truth attribute. 2. The method of claim 1, wherein the sensor data comprises an image. 3. The method of claim 1 or 2, wherein the object comprises a human. 4. The method of one of the previous claims, wherein the machine learning model comprises a neural network. 5. The method of one of the previous claims, wherein the attribute comprises a metric attribute, in particular a measurement, preferably a circumference and/or a height of the object. 6. The method of one of the previous claims, wherein the attribute comprises a semantic attribute and wherein preferably the determining the attribute corresponding to the estimated shape comprises using a polynomial regression model, preferably a second-degree polynomial regression model. 7. The method of one of the previous claims, wherein the attribute is a human-annotated attribute and the method preferably comprises a further step of obtaining a plurality of human-annotated attributes. 8. The method of one of the previous claims, wherein the estimated shape comprises a parametric representation of the shape, wherein in particular the parametric representation comprises SMPL-X shape coefficients. 9. The method of claim 8, wherein the parametric representation comprises a higher number of parameters than a number of attribute values of the attribute. 10. The method of one of the previous claims, wherein - the shape only comprises pose-independent information, or - the shape also comprises pose information. 11. A method for training a machine learning model to estimate shapes of objects based on sensor data, the method comprising: - obtaining a training dataset comprising training sensor data and a corresponding ground truth attribute, - estimating, by the machine learning model, a shape for the training sensor data, - determining a shape for a ground truth attribute corresponding to the training sensor data, and - optimizing the machine learning model using a loss function that is based on a difference between the shape estimated by the machine learning model and the shape determined for the ground truth attribute. 12. The method of claim 11, wherein the sensor data comprises an image. 13. The method of claim 11 or 12, wherein the object comprises a human. 14. The method of one of claims 11 to 13, wherein the machine learning model comprises a neural network. 15. The method of one of claims 11 to 14, wherein the attribute comprises a metric attribute, in particular a measurement, preferably a circumference and/or a height of the object. 16. The method of one of claims 11 to 15, wherein the attribute comprises a semantic attribute and wherein preferably the determining the attribute corresponding to the estimated shape comprises using a polynomial regression model, preferably a second-degree polynomial regression model. 17. The method of one of claims 11 to 16, wherein the attribute is a human-annotated attribute and the method preferably comprises a further step of obtaining a plurality of human-annotated attributes. 18. The method of one of claims 11 to 17, wherein the estimated shape comprises a parametric representation of the shape, in particular a parametric representation comprising SMPL-X shape coefficients. 19. The method of one of claims 11 to 18, wherein the parametric representation comprises a higher number of parameters than a number of attribute values of the attribute. 20. The method of one of claims 11 to 19, wherein - the shape only comprises pose-independent information, or - the shape also comprises pose information. 21. A method for estimating shapes of objects based on sensor data, wherein the method is based on a machine learning model that has been trained using the method of one of the previous claims. 22. A training device for training a machine learning model to estimate shapes of objects based on sensor data, wherein the training device is configured to carry out a method according to one of claims 1 to 20. 23. A machine learning model for estimating shapes of objects based on sensor data, wherein the machine learning model has been trained with a method according to one of claims 1 to 20. 24. A computer-readable storage medium storing program code, the program code comprising instructions that when executed by a processor carry out the method of one of claims 1 to 21. |
Table 8. Leave-one-out evaluation on MMTS. D.2. SHAPE ESTIMATION A2S and its variations: For completeness, Table 7 shows the results of the female A2S models in addition to the male ones. The male results are also presented in the main part above. Note that attributes improve shape reconstruction across the board. For example, in terms of P2P20K, AH2S is better than just H2S, AHW2S is better than just HW2S. It should be emphasized that even when many measurements are used as input features, i.e. height, weight, and chest/waist/hip circumference, adding attributes still improves the shape estimate, e.g. HWC2S vs. AHWC2S. Attribute/Measurement ablation: To investigate the extent to which attributes can replace ground truth measurements in network training, we train SHAPY’s variations in a leave-one-out manner: SHAPY-H uses only height and SHAPY-C only hip/waist/chest circumference. We compare these models with SHAPY-AH and SHAPY-AC, which use attributes in addition to height and circumference measurements, respectively. For completeness, we also evaluate SHAPY-HC and SHAPY-AHC, which use all measurements; the latter also uses attributes. The results are reported in Tab.8 (MMTS) and Tab.9 (HBW). The tables show that attributes are an adequate replacement for measurements. For example, in Tab. 8, the height (SHAPY-C vs. SHAPY-CA) and circumference errors (SHAPY-H vs. SHAPY-AH) are reduced significantly when attributes are taken into account. On HBW, the P2P20K errors are equal or lower, when attribute information is used, see Tab.9. Surprisingly, seeing attributes improves the height error in all three variations. This suggests that training on model images introduces a bias that A2S antagonizes. S2A: Table 10 shows the results of S2A in detail. All attributes are classified correctly with an accuracy of at least 58.05% (females) and 68.14% (males). The probability of randomly guessing the correct class is 20%. AHWC and AHWC2S noise: To evaluate AHWC’s robustness to noise in the input, we fit AHWC using the per-rater scores instead of the average score. The P2P20K↓error only increases by 1.0 mm to 6.8 when using the per-rater scores. D.3. POSE EVALUATION 3D Poses in the Wild (3DPW) [68]: This dataset is mainly useful for evaluating body pose accuracy since it contains few subjects and limited body shape variation. The test set contains a limited set of 5 subjects in indoor/outdoor videos with everyday clothing. All subjects were scanned to obtain their ground-truth body shape. The body poses are pseudo ground-truth SMPL fits, recovered from images and IMUs. We convert pose and shape to SMPL-X for evaluation. We evaluate SHAPY on 3DPW to report pose estimation accuracy (Tab. 11). SHAPY’s pose accuracy is slightly behind ExPose which also uses SMPL-X. SHAPY’s performance is better than HMR [30] and STRAPS [58]. However, SHAPY does not outperform recent pose estimation methods, e.g. HybrIK [39]. We assume that SHAPY’s pose estimation accuracy on 3DPW can be improved by (1) adding data from the 3DPW training set (similar to Sengupta et al. [59] who sample poses from 3DPW training set) and (2) creating pseudo ground-truth fits for the model data. D.4. QUALITATIVE RESULTS We show additional qualitative results in Fig.13 and Fig.15. Failure cases are shown in Fig.16. To deal with high-BMI bodies, we need to expand the set of training images and add additional shape attributes that are descriptive for high-BMI shapes. Muscle definition on highly muscular bodies is not well represented by SMPL-X, nor do our attributes capture this. The SHAPY approach, however, could be used to capture this with a suitable body model and more appropriate attributes.
Table 9. Leave-one-out evaluation on the HBW test set. Table 10. S2A evaluation. We report mean, standard deviation and percentage of correctly predicted classes per attribute on CMTS test set.
Table 11. Evaluation on 3DPW [68]. * uses body poses sampled from the 3DPW training set for training.
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