Source-linked AI summary
CR-GAN: Learning Complete Representations for Multi-view Generation
Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas
TL;DR
Single-pathway GANs may learn incomplete representations, limiting multi-view generation from unseen inputs. CR-GAN adds a generation pathway sharing parameters with reconstruction and uses labeled and unlabeled data for self-supervised learning, with reported high-quality generations from unseen data in wild conditions.
Problem
Single-pathway GANs may learn incomplete latent representations, limiting generalization when unseen inputs fall outside the learned subspace.
Method
CR-GAN adds a generation pathway alongside reconstruction, shares the generator between pathways, and uses labeled and unlabeled data for self-supervised learning.
Results
CR-GAN produces high-quality multi-view image generations from unseen data in wild conditions.
Takeaways & Limitations
The two-pathway framework supports complete representations and realistic generations under challenging unseen-input conditions.
Abstract
from arXiv · showhide
Generating multi-view images from a single-view input is an essential yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn "incomplete" representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network. We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning pathways collaborate and compete in a parameter-sharing manner, yielding considerably improved generalization ability to "unseen" dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. The experimental results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from "unseen" inputs in wild conditions.
1 Introduction
Multi-view generation from one image must imagine rotated appearances while preserving identity, but single-pathway GANs may learn incomplete latent representations that generalize poorly to unseen inputs. CR-GAN addresses this with two collaborating pathways and self-supervised use of labeled and unlabeled data.
- Single-view multi-view generation is challenging because the model must imagine 3D-rotated appearances while preserving object identity.
- Existing GAN-based methods commonly use an encoder-decoder followed by a discriminator, mapping inputs into a latent space before generating novel views.
- The paper reports realistic, identity-preserved multi-view images from unseen data in wild conditions.
- Limited training samples can restrict single-pathway GANs to a latent subspace, causing unseen inputs mapped outside it to produce poor generations.
- CR-GAN adds a generation path that samples embeddings across Z, shares the generator with reconstruction, and encourages complete representations.
- CR-GAN uses labeled and unlabeled data for self-supervised learning, enriching Z for natural generations and improving generation quality.
2 Related Work
Prior work spans GANs, multi-view synthesis, and pose-invariant representation learning, while CR-GAN frames complete latent representations as a two-pathway alternative to conventional single-path designs.
- Generative Adversarial Networks (GANs): GAN research includes adversarial distribution estimation, Wasserstein stabilization, auxiliary class prediction, and bidirectional representation learning.
- Multi-view Synthesis: Multi-view synthesis methods include transforming auto-encoders, projection transformers, appearance flow, and GAN-based encoder-decoder systems.
- Pose-Invariant Representation Learning: Pose-invariant representation methods use commonality analysis, cross-reconstruction, or frontalized appearance transfer, but typically rely only on labeled data.
- CR-GAN: CR-GAN contrasts with prior single-path methods by combining two pathways with self-supervised learning to learn complete representations.
3 Proposed Method
CR-GAN addresses incomplete latent representations in single-pathway multi-view generation by sharing a generator across reconstruction and random-noise generation paths. It further uses viewpoint estimation to incorporate unlabeled images into self-supervised training.
- Motivation: Single-pathway encoder-decoder networks may cover only a subspace of Z, causing poor generation when unseen inputs map outside that subspace.The toy example reports realistic Multi-PIE results but unsatisfactory IJB-A results when new images fall outside the learned subspace.
- Two-pathway architecture: CR-GAN adds a generation path in which G maps random latent embeddings to view-specific images, while sharing G with the reconstruction path.The generation path trains G and D from view labels and random noise; the reconstruction path trains E and D while keeping G fixed.
- Generation path: The generation path trains G to produce realistic images and D to distinguish real data from generated outputs using view and image-quality judgments.D estimates the correct view and image realism, while G seeks outputs that receive high scores from D.
- Reconstruction path: The reconstruction path uses same-identity images from different views so E preserves identity while G reconstructs the target view.An L1 loss enforces reconstruction, and cross-entropy loss trains E as a view estimator.
- Self-supervised learning: Self-supervised CR-GAN pre-trains viewpoint estimation on labeled data, then uses both labeled and unlabeled images to improve G.For unlabeled inputs, E predicts view probabilities, the highest-confidence view becomes the estimated label, and the image is reconstructed to itself.
4 Experiments
Experiments evaluate CR-GAN's two-pathway architecture, self-supervised learning, generalization to unseen data, identity preservation, and image quality against single-pathway and DR-GAN baselines. Across these evaluations, CR-GAN produces more realistic multi-view images, preserves identities better, and handles unseen or large-pose inputs more effectively.
- Experimental settings: Experiments assess CR-GAN on labeled Multi-PIE and 300wLP datasets and unlabeled CelebA and IJB-A data.CelebA view or identity labels are not used during training.
- Single-pathway vs. two-pathway: The two-pathway model consistently produces more natural images with more details and fewer artifacts than the single-pathway model on unseen IJB-A inputs.Both models perform desirably on Multi-PIE, but the single-pathway model degrades on IJB-A.
- Self-supervised learning: Self-supervised learning generates more realistic CelebA images than supervised learning while preserving identities across views.The self-supervised model learns richer latent features, whereas the supervised model produces artifacts and images resembling its constrained training data.
- Comparison with DR-GAN: On IJB-A, CR-GAN synthesizes reasonable profile images and preserves facial identities better than DR-GAN, which fails on large poses.The comparison includes supervised CR-GAN, DR-GAN, and self-supervised CR-GAN results.
- Identity preservation: CR-GAN produces tighter same-identity embedding clusters and outperforms DR-GAN in average feature-distance identity comparisons across datasets.The t-SNE visualization places multi-view images of the same subject closer together for CR-GAN.
- Generative and reconstruction ability: From random noise, CR-GAN generates realistic images with different styles, while DR-GAN produces blurry results and artifacts.CR-GAN also reconstructs CelebA identities better than BiGAN in the reported qualitative comparison.
5 Conclusion
The paper proposes CR-GAN to learn complete GAN representations through a two-pathway framework and self-supervised use of labeled and unlabeled data. Experiments support high-quality multi-view generation, including from unseen data in wild conditions.
- CR-GAN uses a two-pathway framework to investigate and learn complete representations of GAN models.
- The method leverages both labeled and unlabeled data for self-supervised learning and produces high-quality multi-view generations from unseen data in wild conditions.