Summary of Pixel-aligned Multi-view Generation with Depth Guided Decoder, by Zhenggang Tang et al.
Pixel-Aligned Multi-View Generation with Depth Guided Decoder
by Zhenggang Tang, Peiye Zhuang, Chaoyang Wang, Aliaksandr Siarohin, Yash Kant, Alexander Schwing, Sergey Tulyakov, Hsin-Ying Lee
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel method for pixel-level image-to-multi-view generation addresses the misalignment issue in recent text-to-image latent diffusion models by incorporating attention layers across multi-view images in the VAE decoder. Specifically, a depth-truncated epipolar attention is introduced to focus on spatially adjacent regions while remaining memory efficient. To enhance generalization to inaccurate depth during inference, perturbations are applied to depth inputs during training. A rapid multi-view to 3D reconstruction approach, NeuS, is employed to obtain coarse depth for the depth-truncated epipolar attention. This model enables better pixel alignment across multi-view images and demonstrates efficacy in improving downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to generate multiple views of an object from just one image. Instead of relying on complex methods that require large amounts of data, this approach uses attention layers to focus on specific parts of the image while keeping track of spatial relationships between different views. This allows for better pixel alignment and improves performance in tasks like 3D reconstruction. |
Keywords
» Artificial intelligence » Alignment » Attention » Decoder » Diffusion » Generalization » Inference
