FSViewFusion: Few-Shots View Generation of Novel Objects

Core Concepts

The author explores the capabilities of a pretrained stable diffusion model for view synthesis without explicit 3D priors, introducing FSViewFusion as a learning strategy for generating novel views efficiently.

Abstract

FSViewFusion leverages Dreambooth's ability to adapt to specific novel objects with few shots. The method disentangles and transfers view concepts to new objects efficiently. Extensive experiments demonstrate reliable view sample generation for in-the-wild images.
Key points:

Novel view synthesis challenges addressed by diffusion models.
Dreambooth's personalized text-to-image model used for efficient adaptation.
Learning strategy FSViewFusion introduced for transferring views to new objects.
Method demonstrated through extensive experiments on DTU dataset.
Contributions summarized in empirical evidence and design of learning strategy.

Stats

"Dreambooth produces high fidelity to the subject context given with as few as 3-4 samples of the context."
"Our method operates under a few-shots constraint, using as few as a single sample of an object to learn a view LoRA and 3-4 samples to learn a novel object LoRA."

Quotes

"We provide empirical evidence that a view can be treated as a concept to train contextually personalized diffusion models."
"Our method, albeit simple, is efficient in generating reliable view samples for in the wild images."

Key Insights Distilled From

FSViewFusion

by Rukhshanda H... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06394.pdf

Deeper Inquiries

Why does FSViewFusion require one view per LoRA?

FSViewFusion requires one view per LoRA primarily to avoid catastrophic forgetting during training. When multiple views are included in a single LoRA, the number of training samples required increases significantly, which goes against the few-shot learning paradigm that FSViewFusion aims to achieve. By keeping each LoRA specific to a single view, the model can focus on learning and disentangling the high-level concept of that particular view without interference from other views. This approach ensures better performance and reliability in capturing and transferring the viewpoint concept effectively.

Can FSViewFusion effectively work on complex objects like humans?

Yes, FSViewFusion has demonstrated effectiveness in working with complex objects like humans. Through proper reference images for both object and view concepts, FSViewFusion can reliably reconstruct views of human subjects by leveraging its ability to learn high-level concepts such as camera viewpoints without explicit 3D priors. The method has shown promising results in synthesizing novel views of human subjects even when trained on simpler objects initially.

How does changing the background affect the effectiveness of view transfer in FSViewFusion?

Changing the background can have an impact on the effectiveness of view transfer in FSViewFusion. Backgrounds with anchoring artifacts or spatial cues (such as edges, positions of objects) provide additional context for understanding camera viewpoints relative to other elements in the scene. In such cases, where backgrounds offer clear spatial relationships between objects and surroundings, FSViewFusion tends to produce more faithful reconstructions of views due to enhanced understanding of viewpoint concepts based on these cues. On the other hand, backgrounds lacking distinct features may result in variable or less reliable reconstructions as they do not provide sufficient information for accurate viewpoint transfer.