AI大语言模型之文生3D和图生3D

Coding Alan 8个月前 (11-21) 887次浏览 0个评论 扫描二维码

多模态大语言模型世界已然很丰富, 图片领域前有Midjourney,后有DALL·E的追赶,开源界还有Stable Diffusion垫后。视频领域Runway的Gen 2一骑绝尘,也不乏大量的追赶者,Deforum配合Stable Diffusion也可实现文生视频。今天我们探讨的是3D模型,如果未来增强现实以及Apple所倡导的空间计算能够走入大众市场,那么3D模型在的未来的地位很有可能就像今天图片一样。

Instant3D(新加坡国立大学)

https://ming1993li.github.io/Instant3DProj/

Text-to-3D generation, which aims to synthesize vivid 3D objects from text prompts, has attracted much attention from the computer vision community. While several existing works have achieved impressive results for this task, they mainly rely on a time-consuming optimization paradigm. Specifically, these methods optimize a neural field from scratch for each text prompt, taking approximately one hour or more to generate one object. This heavy and repetitive training cost impedes their practical deployment. In this paper, we propose a novel framework for fast text-to-3D generation, dubbed Instant3D. Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network. We achieve this remarkable speed by devising a new network that directly constructs a 3D triplane from a text prompt. The core innovation of our Instant3D lies in our exploration of strategies to effectively inject text conditions into the network. In particular, we propose to combine three key mechanisms: cross-attention, style injection, and token-to-plane transformation, which collectively ensure precise alignment of the output with the input text. Furthermore, we propose a simple yet effective activation function, the scaled-sigmoid, to replace the original sigmoid function, which speeds up the training convergence by more than ten times. Finally, to address the Janus (multi-head) problem in 3D generation, we propose an adaptive Perp-Neg algorithm that can dynamically adjust its concept negation scales according to the severity of the Janus problem during training, effectively reducing the multi-head effect. Extensive experiments on a wide variety of benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods both qualitatively and quantitatively, while achieving significantly better efficiency.

AI大语言模型之文生3D和图生3D

AI大语言模型之文生3D和图生3D

示例:a teddy bear sitting on books and wearing a scarf and wearing a flat cap

AI大语言模型之文生3D和图生3D

Instant3D(Adobe研究中心)

https://jiahao.ai/instant3d/

Text-to-3D with diffusion models has achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from slow inference, low diversity and Janus problems, or are feed-forward methods that generate low quality results due to the scarcity of 3D training data. In this paper, we propose Instant3D, a novel method that generates high-quality and diverse 3D assets from text prompts in a feed-forward manner. We adopt a two-stage paradigm, which first generates a sparse set of four structured and consistent views from text in one shot with a fine-tuned 2D text-to-image diffusion model, and then directly regresses the NeRF from the generated images with a novel transformer-based sparse-view reconstructor. Through extensive experiments, we demonstrate that our method can generate high-quality, diverse and Janus-free 3D assets within 20 seconds, which is two order of magnitude faster than previous optimization-based methods that can take 1 to 10 hours.

AI大语言模型之文生3D和图生3D

演示效果

AI大语言模型之文生3D和图生3D

DMV3D

We propose DMV3D, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model incorporates a triplane NeRF representation and can denoise noisy multi-view images via NeRF reconstruction and rendering, achieving single-stage 3D generation in 30s on single A100 GPU. We train DMV3D on large-scale multi-view image datasets of highly diverse objects using only image reconstruction losses, without accessing 3D assets. We demonstrate state-of-the-art results for the single-image reconstruction problem where probabilistic modeling of unseen object parts is required for generating diverse reconstructions with sharp textures. We also show high-quality text-to-3D generation results outperforming previous 3D diffusion models.

AI大语言模型之文生3D和图生3D

演示效果:

AI大语言模型之文生3D和图生3D

One-2-3-45++

https://sudo-ai-3d.github.io/One2345plus_page/

Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images – two features essential for practical applications. In this paper, we present One-2-3-45++, an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially fine-tuning a 2D diffusion model for consistent multi-view image generation, followed by elevating these images to 3D with the aid of multi-view conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality, diverse 3D assets that closely mirror the original input image.
AI大语言模型之文生3D和图生3D

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