Papers › Unsupervised Training for 3D Morphable Model Regression
Unsupervised Training for 3D Morphable Model Regression
Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, William T. Freeman
We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Face Reconstruction | Florence | Unsupervised-3DMMR | Average 3D Error | 1.50 | #5 of 16 | Archive leaderboard | report |
| 3D Face Reconstruction | Florence | Genova et al. | RMSE Cooperative | 1.78 | #14 of 16 | Archive leaderboard | report |
| 3D Face Reconstruction | Florence | Genova et al. | RMSE Indoor | 1.78 | #14 of 16 | Archive leaderboard | report |
| 3D Face Reconstruction | Florence | Genova et al. | RMSE Outdoor | 1.76 | #14 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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