Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (1601.00706v1)

Abstract: An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a neural network to address the problem if we restrict our attention to specific object categories (in our case faces and chairs) for which we can gather ample training data. In this paper, we propose a novel recurrent convolutional encoder-decoder network that is trained end-to-end on the task of rendering rotated objects starting from a single image. The recurrent structure allows our model to capture long-term dependencies along a sequence of transformations. We demonstrate the quality of its predictions for human faces on the Multi-PIE dataset and for a dataset of 3D chair models, and also show its ability to disentangle latent factors of variation (e.g., identity and pose) without using full supervision.

• The paper’s main contribution is a recurrent model that synthesizes 3D views from a single image by disentangling latent identity and pose features.
• It employs a curriculum learning strategy that progressively extends transformation sequences, significantly reducing mean squared error over baseline methods.
• This approach provides an effective alternative to traditional geometry-based methods, enhancing photorealistic rendering and image-based 3D modeling.

Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis

The paper entitled "Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis" presents an innovative approach in synthesizing novel 3D object views from a single image input. This research addresses the deeply challenging task of reconstructing and rendering 3D models with transformations applied, such as rotations, a problem characterized by its inherent ill-posedness due to the partial observation available from a 2D image.

Central to this paper is the development of a recurrent convolutional encoder-decoder network, which is trained in an end-to-end manner to handle the rotation of objects starting from a single image, with specific applications to objects of predefined categories, like faces and chairs. The architecture leverages long-term dependencies across a sequence of transformations, enabling the network to efficiently perform tasks typically reliant on explicit 3D model recovering processes, like those seen in classic geometry-based methods.

The methodology includes disentangling latent factors such as identity and pose without full supervision. The disentangling process is facilitated by the recurrent nature of the network, which processes sequences, allowing the identity features to be invariant across transformations. The model utilizes a curriculum learning strategy, progressively increasing the difficulty by elongating the sequence of transformations during training, thereby enhancing both the synthesis quality and the discriminative nature of the generated features.

The empirical evaluations on datasets such as Multi-PIE for human faces and a dataset of 3D chair models showcase the model's capability to synthesize high-fidelity rotated views. Quantitative metrics, notably the mean squared error, highlight the improvement over baselines like k-nearest neighbors. Specifically, the experiments demonstrate a significant reduction in error when the recurrent model is extended via curriculum training over longer rotation sequences.

Practical implications of this research indicate a promising step towards more effective photorealistic rendering using neural networks without the need for detailed 3D models, thus broadening applicability in fields that require image-based modeling and understanding. Theoretically, it underscores the importance of recurrent structures and disentangling latent factors for improved model performance across transformations, suggesting future exploration into expanding actions beyond rotation and handling more complex scenes.

Future research directions may focus on extending this framework to incorporate additional transformations or actions, addressing more complex and diverse object categories, and improving its generalization capabilities by leveraging unsupervised or semi-supervised approaches. This line of work has substantial implications for advancing AI's capability in generating and interacting with 3D environments from limited 2D data sources.