SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion
(2403.12008)Abstract
We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to either limited views or inconsistent NVS, thereby affecting the performance of 3D object generation. In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS. We also propose improved 3D optimization techniques to use SV3D and its NVS outputs for image-to-3D generation. Extensive experimental results on multiple datasets with 2D and 3D metrics as well as user study demonstrate SV3D's state-of-the-art performance on NVS as well as 3D reconstruction compared to prior works.
Overview
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SV3D introduces an innovative approach for generating multi-view images and 3D models from a single image using latent video diffusion.
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The method adapts the Stable Video Diffusion framework to include camera poses and CLIP-embeddings, enhancing the generation process.
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SV3D achieves state-of-the-art performance in novel view synthesis and 3D model generation across various datasets and metrics.
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The introduction of a soft-masked SDS loss for 3D model refinement represents a novel contribution to improving 3D reconstructions.
SV3D: Synthesizing Novel Multi-View Images and 3D Models from a Single Image
Introduction
The synthesis of multi-view images and three-dimensional models from a single viewpoint has been a topic of increasing interest within the computer vision and AI research communities. The paper introduces Stable Video 3D (SV3D), an innovative approach to generating detailed, high-resolution multi-view images and 3D models from a single image. Utilizing latent video diffusion mechanisms, SV3D marks a significant advancement in the field by enabling dynamic orbits, achieving state-of-the-art quality in novel view synthesis, and showing remarkable generalizability across diverse objects including real-world instances.
Methodology
At the core of SV3D is the adaptation of the existing Stable Video Diffusion (SVD) framework, tailored for the generation of orbital videos around 3D objects from a single-image input. This customization includes the integration of camera poses into the noise-level embedding of the convolutional blocks in the UNet architecture of SVD. In this process, the embedding of the conditional image is concatenated to the input noisy latent, while the CLIP-embedding of the image is fed into both spatial and temporal attention layers.
The SV3D model accommodates the generation of both circular and dynamic orbits, showcasing an ability to synthesize videos with arbitrary elevations and irregular azimuths. This flexibility allows for the production of more realistic and detailed views compared to static orbits, which significantly enriches the dataset for subsequent 3D model generation.
Experimental Results
The paper reports exhaustive quantitative comparisons of SV3D with existing methodologies across multiple datasets. The evaluation metrics include LPIPS, PSNR, SSIM, MSE, and CLIP-S, where SV3D consistently outperforms previous state-of-the-art models in novel view synthesis. Moreover, the application of SV3D-generated images for 3D model reconstruction exhibits superior performance in both 2D and 3D metrics, demonstrating highly detailed and faithful texture and geometry in the output meshes.
3D Generation Pipeline
For the generation of 3D models from synthesized multi-view images, the paper introduces a two-stage optimization process. Initially, a rough estimation of the object’s shape, texture, and illumination is extracted from the SV3D-generated videos on a reference orbit. Subsequently, this model is refined through DMTet, further optimizing the 3D representation. A particularly novel aspect of SV3D’s approach is the implementation of a soft-masked SDS loss used in conjunction with SV3D guidance to handle unseen areas in the reference orbit, enhancing the fidelity and accuracy of the generated 3D model.
Theoretical Implications and Future Directions
SV3D's innovative use of latent video diffusion for novel view synthesis and 3D model generation represents a significant step forward in the domain of single-image 3D reconstructions. The method's flexibility, demonstrated by its ability to generate high-quality images across both static and dynamic orbits, points towards a new direction for future research in the field.
One of the key insights from SV3D's approach is the importance of constructing detailed, dynamic orbits for enhancing the realism and depth of synthesized views and, by extension, the generated 3D models. Furthermore, the adoption of a soft-masked SDS loss for optimizing unseen areas introduces a promising avenue for improving the consistency and completeness of 3D reconstructions from limited viewpoints.
Conclusion
The introduction of SV3D marks a notable advancement in the synthesis of multi-view images and 3D models from single images. Through the innovative application of latent video diffusion techniques, SV3D achieves unparalleled realism and detail in generated views across varying camera orbits. This capability not only enhances the quality of novel view synthesis and 3D model generation but also opens new pathways for research and development in computer vision and artificial intelligence.
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