# Dynamic Realms: 4D Content Analysis, Recovery and Generation with Geometric, Topological and Physical Priors

arXiv: https://arxiv.org/abs/2409.14692 · HTML: https://mlsanity.com/paper/2409.14692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [TLControl: Trajectory and Language Control for Human Motion Synthesis](https://mlsanity.com/paper/2311.17135/) (2023)
- [EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation](https://mlsanity.com/paper/2312.02256/) (2023)
- [C·ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters](https://mlsanity.com/paper/2309.11351/) (2023)
- [Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-Number Field](https://mlsanity.com/paper/2304.11605/) (2023)
- [Disentangled Clothed Avatar Generation from Text Descriptions](https://mlsanity.com/paper/2312.05295/) (2023)
- [Coverage Axis: Inner Point Selection for 3D Shape Skeletonization](https://mlsanity.com/paper/2110.00965/) (2021)
- [Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization](https://mlsanity.com/paper/2401.12946/) (2024)
- [DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image](https://mlsanity.com/paper/2406.17988/) (2024)
- [Top-Down Shape Abstraction Based on Greedy Pole Selection](https://mlsanity.com/paper/1910.08954/) (2019)

## Built on by (most cited first, 1 of 1)

- [Advances in 4D Generation: A Survey](https://mlsanity.com/paper/2503.14501/) (2025)

## Abstract (arXiv)

My research focuses on the analysis, recovery, and generation of 4D content, where 4D includes three spatial dimensions (x, y, z) and a temporal dimension t, such as shape and motion. This focus goes beyond static objects to include dynamic changes over time, providing a comprehensive understanding of both spatial and temporal variations. These techniques are critical in applications like AR/VR, embodied AI, and robotics. My research aims to make 4D content generation more efficient, accessible, and higher in quality by incorporating geometric, topological, and physical priors. I also aim to develop effective methods for 4D content recovery and analysis using these priors.