DynaPose4D: High-Quality 4D Dynamic Content Generation via Pose Alignment Loss

TL;DR

DynaPose4D is a novel framework that generates high-quality 4D dynamic content from a single image by combining 4D Gaussian Splatting with pose estimation to ensure motion consistency and realistic dynamics.

Contribution

The paper introduces DynaPose4D, integrating 4D Gaussian Splatting with pose estimation to improve single-image 4D dynamic content generation, addressing temporal and geometric modeling challenges.

Findings

Achieves high coherence and fluidity in dynamic motion generation.

Demonstrates superior performance over existing methods in quality and consistency.

Validates potential applications in computer vision and animation.

Abstract

Recent advancements in 2D and 3D generative models have expanded the capabilities of computer vision. However, generating high-quality 4D dynamic content from a single static image remains a significant challenge. Traditional methods have limitations in modeling temporal dependencies and accurately capturing dynamic geometry changes, especially when considering variations in camera perspective. To address this issue, we propose DynaPose4D, an innovative solution that integrates 4D Gaussian Splatting (4DGS) techniques with Category-Agnostic Pose Estimation (CAPE) technology. This framework uses 3D Gaussian Splatting to construct a 3D model from single images, then predicts multi-view pose keypoints based on one-shot support from a chosen view, leveraging supervisory signals to enhance motion consistency. Experimental results show that DynaPose4D achieves excellent coherence, consistency, and fluidity in dynamic motion generation. These findings not only validate the efficacy of the DynaPose4D framework but also indicate its potential applications in the domains of computer vision and animation production.