Multi-view Image Prompts Enhance 3D Object Generation Performance

Alapfogalmak

Using multiple image prompts instead of a single image prompt can improve the performance of multi-view and 3D object generation.

Kivonat

The paper investigates the potential of using multiple image prompts, instead of a single image prompt, for 3D object generation. It builds on the ImageDream model, a state-of-the-art image-to-3D generation method, and extends its local and pixel controllers to support multi-image prompts.
The key highlights are:

The authors identify that existing image-to-3D methods have room for improvement, especially in terms of consistent quality and texture across varying viewpoints.

They propose MultiImageDream, which leverages the pre-trained ImageDream model and extends it to accommodate multi-image prompts without the need for fine-tuning.

MultiImageDream shows quantitative and qualitative improvements over the baseline ImageDream, suggesting that using multiple image prompts can enhance the performance of multi-view and 3D object generation.

The authors discuss future research directions, such as fine-tuning the model with multi-image prompts and exploring the effect of 3D consistency of multi-view inputs on the quality of 3D generated output.

Statisztikák

The paper provides quantitative results comparing MultiImageDream with the baseline ImageDream model on various metrics like Quality-only Inception Score (QIS), CLIP Text (CLIP(TX)), and CLIP Image (CLIP(IM)) scores.

Idézetek

"Using image as prompts for 3D generation demonstrate particularly strong performances compared to using text prompts alone, for images provide a more intuitive guidance for the 3D generation process."
"We observe that at other viewpoints, the generated 3D object often shows to lack details, or shows inconsistencies in texture or lighting e.g. whitened texture towards the back of the object."
"MultiImageDream shows to outperform ImageDream both in terms of quantitative metrics and qualitative assessment without any finetuning."

Főbb Kivonatok

Multi-view Image Prompted Multi-view Diffusion for Improved 3D Generation

by Seungwook Ki... : arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17419.pdf

Mélyebb kérdések

How can the performance of 3D generation be further improved by fine-tuning the MultiImageDream model with multi-image prompts?

Fine-tuning the MultiImageDream model with multi-image prompts can significantly enhance the performance of 3D generation in several ways. Firstly, by fine-tuning the model, we can optimize the network's parameters to better leverage the information provided by multiple image prompts. This process allows the model to learn more effectively from the additional visual cues present in the multi-image prompts, leading to improved feature extraction and representation learning.
Moreover, fine-tuning the MultiImageDream model with multi-image prompts enables the network to adapt its weights and biases to the specific characteristics of the input data. This adaptation can help in capturing intricate details, textures, and lighting variations across different viewpoints, resulting in more realistic and consistent 3D object generation.
Additionally, fine-tuning the model can help in refining the cross-view interactions between the multi-image prompts, enhancing the overall coherence and consistency of the generated 3D objects. By iteratively adjusting the model's parameters based on the multi-image prompts, the network can better align the generated outputs with the input prompts, leading to higher-quality and more accurate 3D reconstructions.

What are the potential drawbacks or limitations of using generated images as multi-view input prompts, and how can the 3D consistency of the input prompts be ensured?

Using generated images as multi-view input prompts may introduce certain drawbacks or limitations that need to be addressed to ensure the 3D consistency of the input prompts and improve the overall quality of the generated 3D objects. One potential limitation is the risk of introducing artifacts or inconsistencies in the generated images, which can negatively impact the quality of the 3D reconstructions.
To ensure the 3D consistency of the input prompts when using generated images, it is essential to implement validation mechanisms that assess the coherence and alignment of the multi-view images. This can involve incorporating additional constraints or regularization techniques during the generation process to enforce consistency across different viewpoints.
Furthermore, leveraging techniques such as multi-view geometry constraints or structure-from-motion algorithms can help in verifying the spatial relationships between the generated views and ensuring that the 3D objects maintain realistic shapes and proportions across different perspectives. By integrating these methods into the training pipeline, the model can learn to generate multi-view images that are more consistent and aligned with the underlying 3D structure.

How can the insights from this work be extended to other 3D generation tasks, such as text-to-3D or video-to-3D, to leverage the benefits of multi-view prompting?

The insights gained from this work on multi-view image prompted 3D generation can be extended to other 3D generation tasks, such as text-to-3D or video-to-3D, to harness the advantages of multi-view prompting and improve the quality of the generated outputs.
For text-to-3D tasks, incorporating multi-view prompting can enhance the interpretability and richness of the textual descriptions by providing multiple visual references for the 3D object generation process. By integrating multi-view image prompts into text-based generative models, the network can better understand and translate textual descriptions into coherent and detailed 3D representations.
Similarly, in video-to-3D tasks, leveraging multi-view prompting can facilitate the extraction of spatial and temporal information from video sequences to generate 3D objects with consistent shapes and appearances across different frames. By utilizing multi-view images extracted from video frames as input prompts, the model can capture dynamic changes and viewpoints, leading to more accurate and realistic 3D reconstructions.