Advances and Trends in the 3D Reconstruction of the Shape and Motion of Animals

2025-08-21

AI Summary

This paper addresses the non-invasive 3D reconstruction of animal geometry, pose, and motion from RGB images or videos. We present the first systematic survey of deep learning approaches for this task, introducing a four-dimensional analytical framework—spanning input modalities, geometric representations, reconstruction paradigms, and training mechanisms—to structurally categorize and comparatively evaluate monocular and multi-view animal 3D reconstruction methods. For the first time, we characterize the applicability boundaries and limitations of mainstream techniques—including neural radiance fields (NeRF), implicit functions, and parametric models (e.g., SMAL)—and explicitly identify three core challenges in real-world settings: poor cross-individual generalization, low robustness to texture absence, and severe scarcity of annotated data. Our synthesis provides methodological foundations and a technical roadmap for biological observation, wildlife conservation, and digital twin applications.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Reconstructing the 3D geometry, pose, and motion of animals is a long-standing problem, which has a wide range of applications, from biology, livestock management, and animal conservation and welfare to content creation in digital entertainment and Virtual/Augmented Reality (VR/AR). Traditionally, 3D models of real animals are obtained using 3D scanners. These, however, are intrusive, often prohibitively expensive, and difficult to deploy in the natural environment of the animals. In recent years, we have seen a significant surge in deep learning-based techniques that enable the 3D reconstruction, in a non-intrusive manner, of the shape and motion of dynamic objects just from their RGB image and/or video observations. Several papers have explored their application and extension to various types of animals. This paper surveys the latest developments in this emerging and growing field of research. It categorizes and discusses the state-of-the-art methods based on their input modalities, the way the 3D geometry and motion of animals are represented, the type of reconstruction techniques they use, and the training mechanisms they adopt. It also analyzes the performance of some key methods, discusses their strengths and limitations, and identifies current challenges and directions for future research.

Problem

• Reconstructing 3D animal shape and motion non-intrusively
• Overcoming limitations of traditional intrusive 3D scanning methods
• Surveying deep learning techniques from RGB image/video data

Innovation

• Deep learning-based 3D reconstruction techniques
• Non-intrusive shape and motion reconstruction
• RGB image and video input modalities