Transformation-Grounded Image Generation Network for Novel 3D View Synthesis (1703.02921v1)

Abstract: We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion. Specifically, we both predict a flow to move the pixels from the input to the novel view along with a novel visibility map that helps deal with occulsion/disocculsion. Next, conditioned on those intermediate results, we hallucinate (infer) parts of the object invisible in the input image. In addition to the new network structure, training with a combination of adversarial and perceptual loss results in a reduction in common artifacts of novel view synthesis such as distortions and holes, while successfully generating high frequency details and preserving visual aspects of the input image. We evaluate our approach on a wide range of synthetic and real examples. Both qualitative and quantitative results show our method achieves significantly better results compared to existing methods.

• The paper introduces a two-stage network combining disocclusion-aware appearance flow and view completion for synthesizing novel 3D views.
• It employs visibility maps and adversarial losses to minimize artifacts, achieving lower L1 error and higher SSIM scores.
• The approach advances 3D synthesis in applications like VR, AR, and robotics, paving the way for real-world image generation improvements.

An Analysis of the Transformation-Grounded Image Generation Network for Novel 3D View Synthesis

The paper presents a novel transformation-grounded image generation network aimed at synthesizing new 3D views from a single image, addressing the complexities associated with view transformation and image synthesis. This methodology departs from the conventional direct generation approach and focuses on a two-stage process integrating deep learning techniques with transformation grounding.

Overview of the Methodology

The proposed network architecture consists of two primary stages: the Disocclusion-aware Appearance Flow Network (DOAFN) and a subsequent View Completion Network. Initially, the DOAFN predicts pixel movements and visibility maps to transform the input view to the desired novel viewpoint. The subsequent completion network refines this intermediate result by hallucinating disoccluded sections and enhancing the image through feature reconstruction and adversarial losses.

Disocclusion-aware Appearance Flow Network (DOAFN): The network predicts a dense flow field to determine how pixels in the input image should be relocated to form part of the output image. The network incorporates a visibility map that identifies which portions of the scene should not carry over due to occlusions. This explicit understanding of geometry is essential in reducing artifacts often prevalent in view synthesis tasks.

View Completion Network: This stage leverages an encoder-decoder architecture to refine and complete the entire frame, particularly the disoccluded regions identified by the DOAFN. Additional losses, such as a pre-trained VGG16-based perceptual loss, support the preservation of semantic coherence, while an adversarial loss promotes the generation of visually plausible textures and details.

Evaluation and Results

The transformation-grounded network was evaluated using synthetic datasets derived from the ShapeNet repository, covering categories like cars and chairs. Compared to existing approaches such as the appearance flow network (AFN), the proposed method demonstrated superior qualitative and quantitative results, highlighted through reduced L1 error and increased Structural Similarity Index Measure (SSIM) scores.

Novel Findings:

• The integration of visibility maps drastically reduces artifacts by preventing erroneous pixel usage in occluded areas.
• Utilizing combined feature reconstruction and adversarial losses significantly reduces common issues such as blurriness and enhances detail preservation.

Implications and Future Directions

This transformation-grounded approach holds implications for several fields, including virtual and augmented reality, autonomous navigation, and robotics. The ability to accurately synthesize unseen perspectives of objects improves environmental perception and interaction in both synthetic and real-world imagery scenarios.

For future exploration, addressing arbitrary transformations and adapting the network to handle dynamic, real-world scenes composed of multiple objects and complex interactions will enhance its applicability. Expansion to additional object categories and real datasets through improved simulated training approaches can further strengthen the generalization capacity of the network.

In conclusion, the paper presents a significant advancement in the synthesis of novel views via transformation-grounded methodology, effectively utilizing deep learning to address challenges intrinsic to view synthesis tasks. The compelling results establish a foundation for further exploration into the practical applications of these techniques in real-world scenarios, potentially broadening the scope and impact of computational image generation research.