4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video
4D reconstruction of equine family (e.g. horses) from monocular video is important for animal welfare. Previous mainstream 4D animal reconstruction methods require joint optimization of motion and appearance over a whole video, which is time-consuming and sensitive to incomplete observation. In this work, we propose a novel framework called 4DEquine by disentangling the 4D reconstruction problem into two sub-problems: dynamic motion reconstruction and static appearance reconstruction. For motion, we introduce a simple yet effective spatio-temporal transformer with a post-optimization stage to regress smooth and pixel-aligned pose and shape sequences from video. For appearance, we design a novel feed-forward network that reconstructs a high-fidelity, animatable 3D Gaussian avatar from as few as a single image. To assist training, we create a large-scale synthetic motion dataset, VarenPoser, which features high-quality surface motions and diverse camera trajectories, as well as a synthetic appearance dataset, VarenTex, comprising realistic multi-view images generated through multi-view diffusion. While training only on synthetic datasets, 4DEquine achieves state-of-the-art performance on real-world APT36K and AiM datasets, demonstrating the superiority of 4DEquine and our new datasets for both geometry and appearance reconstruction. Comprehensive ablation studies validate the effectiveness of both the motion and appearance reconstruction network. Project page: https://luoxue-star.github.io/4DEquine_Project_Page/.
69b2b0cf7450df3b5c089ca1
Jin Lyu
claimed_verified
2026-03-13T08:14:15.106Z
65560e8047adf6b4ebfc7062
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/a7uIL26DBYuHTiLOKSgQM.png
Jin Lyu
luoxue-star
user
luoxue-star
69b2b0cf7450df3b5c089ca2
Liang An
69b2b0cf7450df3b5c089ca3
Pujin Cheng
69b2b0cf7450df3b5c089ca4
Yebin Liu
69b2b0cf7450df3b5c089ca5
Xiaoying Tang
4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video
4D reconstruction of equine family (e.g. horses) from monocular video is important for animal welfare. Previous mainstream 4D animal reconstruction methods require joint optimization of motion and appearance over a whole video, which is time-consuming and sensitive to incomplete observation. In this work, we propose a novel framework called 4DEquine by disentangling the 4D reconstruction problem into two sub-problems: dynamic motion reconstruction and static appearance reconstruction. For motion, we introduce a simple yet effective spatio-temporal transformer with a post-optimization stage to regress smooth and pixel-aligned pose and shape sequences from video. For appearance, we design a novel feed-forward network that reconstructs a high-fidelity, animatable 3D Gaussian avatar from as few as a single image. To assist training, we create a large-scale synthetic motion dataset, VarenPoser, which features high-quality surface motions and diverse camera trajectories, as well as a synthetic appearance dataset, VarenTex, comprising realistic multi-view images generated through multi-view diffusion. While training only on synthetic datasets, 4DEquine achieves state-of-the-art performance on real-world APT36K and AiM datasets, demonstrating the superiority of 4DEquine and our new datasets for both geometry and appearance reconstruction. Comprehensive ablation studies validate the effectiveness of both the motion and appearance reconstruction network. Project page: https://luoxue-star.github.io/4DEquine_Project_Page/.
A novel 4D reconstruction framework for equine family that separates motion and appearance reconstruction using spatio-temporal transformers and 3D Gaussian avatars, achieving state-of-the-art results on real-world datasets.
spatio-temporal transformer 3D Gaussian avatar motion reconstruction appearance reconstruction monocular video equine family VarenPoser VarenTex multi-view diffusion pose regression shape regression animatable avatar