Diff-3DCap: Shape Captioning with Diffusion Models

2025-09-28

AI Summary

Existing 3D shape captioning methods rely on computationally expensive voxelization or object detection, limiting scalability and performance. Method: We propose an end-to-end cross-modal generation framework based on continuous diffusion models that bypasses explicit 3D representations. Given a sequence of multi-view images, our approach leverages a pre-trained vision-language model (VLM) to extract joint visual–textual embeddings, which serve as conditional signals for the diffusion process. Specifically, Gaussian noise is applied to the text embedding space and iteratively denoised to reconstruct natural language descriptions directly from multi-view visual features—eliminating the need for classifiers or post-processing modules. Contribution/Results: Our method significantly reduces computational overhead while achieving state-of-the-art performance on standard 3D shape captioning benchmarks. Experimental results demonstrate the effectiveness and generalizability of diffusion models for 3D vision-language generation tasks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The task of 3D shape captioning occupies a significant place within the domain of computer graphics and has garnered considerable interest in recent years. Traditional approaches to this challenge frequently depend on the utilization of costly voxel representations or object detection techniques, yet often fail to deliver satisfactory outcomes. To address the above challenges, in this paper, we introduce Diff-3DCap, which employs a sequence of projected views to represent a 3D object and a continuous diffusion model to facilitate the captioning process. More precisely, our approach utilizes the continuous diffusion model to perturb the embedded captions during the forward phase by introducing Gaussian noise and then predicts the reconstructed annotation during the reverse phase. Embedded within the diffusion framework is a commitment to leveraging a visual embedding obtained from a pre-trained visual-language model, which naturally allows the embedding to serve as a guiding signal, eliminating the need for an additional classifier. Extensive results of our experiments indicate that Diff-3DCap can achieve performance comparable to that of the current state-of-the-art methods.

Problem

• Overcoming limitations of costly voxel representations in 3D shape captioning
• Generating descriptive captions for 3D objects using diffusion models
• Eliminating need for additional classifiers through visual-language guidance

Innovation

• Uses projected views for 3D object representation
• Employs continuous diffusion model for caption generation
• Leverages pre-trained visual-language model as guidance