# FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance

arXiv: https://arxiv.org/abs/2505.13437 · HTML: https://mlsanity.com/paper/2505.13437/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 74 of its 93 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 30 of 74)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [PhysDiff: Physics-Guided Human Motion Diffusion Model](https://mlsanity.com/paper/2212.02500/) (2022)
- [Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos](https://mlsanity.com/paper/2304.01186/) (2023)

## Built on by (most cited first, 9 of 9)

- [Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation](https://mlsanity.com/paper/2508.10858/) (2025)
- [TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models](https://mlsanity.com/paper/2511.13704/) (2025)
- [ANYPORTAL: Zero-Shot Consistent Video Background Replacement](https://mlsanity.com/paper/2509.07472/) (2025)
- [Human detectors are surprisingly powerful reward models](https://mlsanity.com/paper/2601.14037/) (2026)
- [DiReCT: Disentangled Regularization of Contrastive Trajectories for Physics-Refined Video Generation](https://mlsanity.com/paper/2603.25931/) (2026)
- [InterPhys: Physics-aware Human Motion Synthesis in a Dynamic Scene](https://mlsanity.com/paper/2605.01036/) (2026)
- [RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data](https://mlsanity.com/paper/2605.13775/) (2026)
- [GenShield: Unified Detection and Artifact Correction for AI-Generated Images](https://mlsanity.com/paper/2605.16122/) (2026)
- [LaDy: Lagrangian-Dynamic Informed Network for Skeleton-based Action Segmentation via Spatial-Temporal Modulation](https://mlsanity.com/paper/2603.24097/) (2026)

## Abstract (arXiv)

Despite significant advances in video generation, synthesizing physically plausible human actions remains a persistent challenge, particularly in modeling fine-grained semantics and complex temporal dynamics. For instance, generating gymnastics routines such as "switch leap with 0.5 turn" poses substantial difficulties for current methods, often yielding unsatisfactory results. To bridge this gap, we propose FinePhys, a Fine-grained human action generation framework that incorporates Physics to obtain effective skeletal guidance. Specifically, FinePhys first estimates 2D poses in an online manner and then performs 2D-to-3D dimension lifting via in-context learning. To mitigate the instability and limited interpretability of purely data-driven 3D poses, we further introduce a physics-based motion re-estimation module governed by Euler-Lagrange equations, calculating joint accelerations via bidirectional temporal updating. The physically predicted 3D poses are then fused with data-driven ones, offering multi-scale 2D heatmap guidance for the diffusion process. Evaluated on three fine-grained action subsets from FineGym (FX-JUMP, FX-TURN, and FX-SALTO), FinePhys significantly outperforms competitive baselines. Comprehensive qualitative results further demonstrate FinePhys's ability to generate more natural and plausible fine-grained human actions.