Chirpy3D: Continuous Part Latents for Creative 3D Bird Generation

2025-01-07

AI Summary

Existing 3D generative methods struggle to simultaneously ensure biological plausibility and fine-grained creative control—particularly when synthesizing novel yet anatomically credible bird models. To address this, we propose the first end-to-end framework for fine-grained creative 3D bird generation. Our method introduces (1) a continuous part-wise latent space enabling geometrically consistent, interpolatable, and sampleable part modeling; and (2) a joint optimization leveraging multi-view diffusion priors and self-supervised feature consistency loss to enforce cross-view geometric coherence and anatomical validity. The framework is the first to generate previously unseen, species-specific, and structurally sound 3D bird models. Quantitative and qualitative evaluations demonstrate substantial improvements over state-of-the-art baselines in both detail fidelity and creative expressiveness, establishing new capabilities for biologically grounded generative 3D modeling.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In this paper, we push the boundaries of fine-grained 3D generation into truly creative territory. Current methods either lack intricate details or simply mimic existing objects -- we enable both. By lifting 2D fine-grained understanding into 3D through multi-view diffusion and modeling part latents as continuous distributions, we unlock the ability to generate entirely new, yet plausible parts through interpolation and sampling. A self-supervised feature consistency loss further ensures stable generation of these unseen parts. The result is the first system capable of creating novel 3D objects with species-specific details that transcend existing examples. While we demonstrate our approach on birds, the underlying framework extends beyond things that can chirp! Code will be released at https://github.com/kamwoh/chirpy3d.

Problem

• 3D modeling
• biological features
• detail and creativity limitation

Innovation

• Chirpy3D
• Continuous Component Features
• 3D Model Generation