Towards Implicit Text-Guided 3D Shape Generation (2203.14622v1)

Abstract: In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable of producing high-fidelity shapes with colors that match the given text description. This work has several technical contributions. First, we decouple the shape and color predictions for learning features in both texts and shapes, and propose the word-level spatial transformer to correlate word features from text with spatial features from shape. Also, we design a cyclic loss to encourage consistency between text and shape, and introduce the shape IMLE to diversify the generated shapes. Further, we extend the framework to enable text-guided shape manipulation. Extensive experiments on the largest existing text-shape benchmark manifest the superiority of this work. The code and the models are available at https://github.com/liuzhengzhe/Towards-Implicit Text-Guided-Shape-Generation.

• The paper introduces a novel framework for text-guided 3D shape generation that decouples shape and color to enhance output fidelity.
• It employs a word-level spatial transformer and cyclic consistency loss to effectively bridge the gap between textual descriptions and 3D spatial features.
• Experimental results show improved shape detail and variability, indicating significant potential for applications in design, virtual reality, and animation.

Towards Implicit Text-Guided 3D Shape Generation

The paper "Towards Implicit Text-Guided 3D Shape Generation" addresses an emerging and challenging task in the field of AI: generating high-fidelity 3D shapes guided by text descriptions. The authors propose a novel framework that surpasses previous methodologies by incorporating several key innovations.

Methodological Contributions

1. Decoupled Shape and Color Representation: The authors advocate for the separation of shape and color predictions, significantly enhancing the quality of the generated 3D shapes. This approach is built on the observation that conflating these attributes often causes blurring and distortions.
2. Word-Level Spatial Transformer: To effectively parse spatial information inherent within text descriptions, the authors introduce a word-level spatial transformer. This mechanism correlates word features with spatial features in the shape generation process, thereby managing complex spatial relations, such as "a wooden table on a metal base."
3. Cyclic Consistency Loss: This new loss function is proposed to bridge the semantic gap between textual descriptions and 3D shapes. By enforcing consistency between generated shapes and their textual descriptors, the cyclic loss enhances the semantic adherence of the generated models.
4. Style-Based Latent Shape IMLE Generator: To accommodate the inherent variation existent when mapping texts to shapes, a style-based generative model is utilized. Based on Implicit Maximum Likelihood Estimation (IMLE), this model aims to diversify outputs for a single input description, addressing the one-to-many nature of the text-to-shape task.
5. Text-Guided Shape Manipulation: Extending the framework's functionality, the authors also enable the manipulation of existing shapes via textual inputs, a task that includes attributes like color and specific features of the shape.

Experimental Validation

The authors conducted rigorous experiments using the largest existing text-shape benchmark dataset, highlighting the superior performance of their model in generating more plausible and detailed shapes. Metrics such as Intersection-over-Union (IoU), Earth Mover's Distance (EMD), and others were utilized to quantitatively evaluate performance. The results demonstrated improved fidelity and variability of generated shapes over existing methods, such as Chen et al.'s 3D text generation approach.

Implications and Future Directions

The implications of this work are manifold. Practically, the framework can significantly expedite design processes in fields such as computer-aided design, virtual reality, and animation by swiftly generating and manipulating 3D content based on textual input. Theoretically, the work enriches the understanding of text-to-shape generation, setting a foundation for further exploration in processing spatial language in neural networks.

Future research could delve into zero-shot learning capabilities, enabling the generation of shapes for unseen text categories. Moreover, refining techniques to handle intricate and highly specific descriptions that might include emotional or abstract content could be a challenging yet rewarding direction.

In summary, this research marks a significant step towards the effective generation of 3D shapes from text, with advancements in network design and training objectives that open new avenues for application and further paper.