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

Jing Yang, Yufeng Yang

TL;DR

DynaPose4D tackles the challenge of generating coherent 4D dynamic content from a single image by fusing 4D Gaussian Splatting with Category-Agnostic Pose Estimation (CAPE). The method builds a static 3D model via 3D Gaussian Splatting, extracts pose keypoints with CAPE, and drives temporal deformation using Pose Alignment Loss, which combines Keypoint Match Loss $L_{KML}$ and Spatio-temporal Consistency Loss $L_{SCL}$ to enforce alignment with predicted keypoints and smooth temporal evolution. A 4D deformation framework uses $S'_ au = \phi(S, \tau)$ and a reference-driven loss $L_{Ref}$ initialized via Score Distillation Sampling, producing temporally coherent 4D content guided by one-shot pose cues. On Consistent4D and Animate124, DynaPose4D outperforms state-of-the-art baselines in PSNR, SSIM, and LPIPS, with ablation showing pose supervision is essential for maintaining motion fidelity and spatial consistency, enabling robust applications in animation and AR/VR content creation.

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.

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

TL;DR

DynaPose4D tackles the challenge of generating coherent 4D dynamic content from a single image by fusing 4D Gaussian Splatting with Category-Agnostic Pose Estimation (CAPE). The method builds a static 3D model via 3D Gaussian Splatting, extracts pose keypoints with CAPE, and drives temporal deformation using Pose Alignment Loss, which combines Keypoint Match Loss and Spatio-temporal Consistency Loss to enforce alignment with predicted keypoints and smooth temporal evolution. A 4D deformation framework uses and a reference-driven loss initialized via Score Distillation Sampling, producing temporally coherent 4D content guided by one-shot pose cues. On Consistent4D and Animate124, DynaPose4D outperforms state-of-the-art baselines in PSNR, SSIM, and LPIPS, with ablation showing pose supervision is essential for maintaining motion fidelity and spatial consistency, enabling robust applications in animation and AR/VR content creation.

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.
Paper Structure (11 sections, 5 equations, 4 figures, 2 tables)

This paper contains 11 sections, 5 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Prior work
  3. Method
  4. Neural Rendering for 4D Content
  5. Pose Alignment Loss
  6. Experiments
  7. Evaluation Metrics
  8. Comparative Analysis
  9. Ablation Study
  10. Discussion
  11. Conclusion

Figures (4)

  • Figure 1: Overview of DynaPose4D. From a single image, 3D Gaussians are deformed into 4D content. Temporal consistency is enforced by $\mathcal{L}_{\text{SCL}}$, while $\mathcal{L}_{\text{Ref}}+\mathcal{L}_{\text{KML}}$ supervise visual consistency and refine pose transitions.
  • Figure 2: KML minimizes MSE between poses extracted from rendered and predicted frames under one-shot support.
  • Figure 3: visual comparisons among different methods. DynaPose4D produces more coherent and realistic dynamic content, with better temporal consistency and motion smoothness.
  • Figure 4: Result of ablation studies. Pose keypoint supervision enhances 4D visual consistency and yields 3D geometry better aligned with the source image.