Diffusion Time-step Curriculum for Efficient and High-Fidelity One Image to 3D Generation

Khái niệm cốt lõi

A diffusion time-step curriculum that collaborates the teacher diffusion model and 3D student model can significantly improve the photo-realism and multi-view consistency of image-to-3D generation.

Tóm tắt

The paper proposes the Diffusion Time-step Curriculum one-image-to-3D pipeline (DTC123) to address the limitations of existing Score Distillation Sampling (SDS) based methods.
Key highlights:

SDS-based methods often encounter geometric artifacts and texture saturation due to the overlooked indiscriminate treatment of diffusion time-steps during optimization.
DTC123 involves both the teacher and student models collaborating with the time-step curriculum in a coarse-to-fine manner.
Larger time steps capture coarse-grained concepts like geometry formation, while smaller time steps focus on enhancing fine-grained details like texture nuance.
DTC123 leverages progressive student representation, coarse-to-fine teacher guidance, and geometric regularization techniques to generate high-fidelity and multi-view consistent 3D assets.
Extensive experiments on benchmark datasets demonstrate the superiority of DTC123 over state-of-the-art methods in terms of reconstruction quality, view consistency, and generation robustness.

Thống kê

The paper presents several key metrics to evaluate the performance of image-to-3D generation:

PSNR: Measures the reconstruction quality of the generated 3D model.
LPIPS: Measures the perceptual similarity between the generated and ground-truth images.
CLIP-Similarity: Measures the view consistency of the generated 3D model.

Trích dẫn

"SDS-based methods often encounter collapsed geometry and limited fidelity. Such issues arise primarily from the confusion of holistic structures and local details."
"An optimal SDS should follow a diffusion time-step curriculum: larger time steps capture coarse-grained knowledge like geometry formation and smaller time steps focus on enhancing fine-grained details like texture nuance."

Thông tin chi tiết chính được chắt lọc từ

Diffusion Time-step Curriculum for One Image to 3D Generation

by Xuanyu Yi,Zi... lúc arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04562.pdf

Yêu cầu sâu hơn

How can the proposed diffusion time-step curriculum be extended to other 3D generation tasks beyond image-to-3D, such as text-to-3D?

The diffusion time-step curriculum proposed in the DTC123 pipeline can be extended to other 3D generation tasks, such as text-to-3D, by adapting the teacher and student models to the specific requirements of the task. In the case of text-to-3D generation, the teacher diffusion model can be conditioned on text descriptions instead of images. This text-conditioned teacher model can guide the student model in generating 3D assets based on textual inputs. The time-step curriculum can be adjusted to focus on capturing the coarse-grained concepts from the text descriptions initially and then refining the fine-grained details as the training progresses. By incorporating text embeddings and language models, the teacher model can provide guidance on the structure and details of the 3D objects based on the textual input. This extension would involve training the models on text datasets and optimizing them to generate accurate and realistic 3D representations from textual descriptions.

What are the potential limitations of the current DTC123 pipeline, and how can they be addressed in future work?

One potential limitation of the current DTC123 pipeline could be the reliance on pre-trained teacher diffusion models, which may introduce biases or limitations in the generated 3D assets. To address this, future work could focus on training more diverse and specialized teacher models that are tailored to specific tasks or datasets. Additionally, the pipeline may face challenges in handling complex scenes with multiple objects or intricate details. To overcome this limitation, the pipeline could be enhanced with multi-instance generation capabilities and improved handling of diverse and challenging scenes. Another limitation could be the computational complexity of the pipeline, which may hinder scalability and real-time applications. Future work could explore optimization techniques and model architectures to improve efficiency and speed without compromising quality.

How can the collaboration between the teacher diffusion model and student 3D model be further improved to achieve even higher-quality and more efficient 3D generation?

To enhance the collaboration between the teacher diffusion model and student 3D model for higher-quality and more efficient 3D generation, several strategies can be implemented. Firstly, the teacher model can provide more detailed and informative guidance to the student model by incorporating advanced features like attention mechanisms or reinforcement learning. This would enable the teacher model to focus on specific areas of improvement for the student model, leading to more accurate and realistic 3D asset generation. Secondly, the student model can be optimized to better utilize the guidance provided by the teacher model by incorporating feedback mechanisms and adaptive learning strategies. This would allow the student model to adapt and improve based on the feedback received from the teacher model, leading to continuous enhancement in the quality of the generated 3D assets. Additionally, exploring novel architectures and training techniques that leverage the strengths of both models could further improve the collaboration and overall performance of the pipeline.