# Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation

arXiv: https://arxiv.org/abs/2408.16506 · HTML: https://mlsanity.com/paper/2408.16506/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 43 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 30 of 36)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [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)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [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)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://mlsanity.com/paper/2303.09535/) (2023)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [Effective Whole-body Pose Estimation with Two-stages Distillation](https://mlsanity.com/paper/2307.15880/) (2023)
- [Composer: Creative and Controllable Image Synthesis with Composable Conditions](https://mlsanity.com/paper/2302.09778/) (2023)
- [Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation](https://mlsanity.com/paper/2306.07954/) (2023)
- [TryOnDiffusion: A Tale of Two UNets](https://mlsanity.com/paper/2306.08276/) (2023)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation](https://mlsanity.com/paper/2307.11545/) (2023)
- [Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/2402.18078/) (2024)

## Built on by (most cited first, 10 of 10)

- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation](https://mlsanity.com/paper/2503.18860/) (2025)
- [DisPose: Disentangling Pose Guidance for Controllable Human Image Animation](https://mlsanity.com/paper/2412.09349/) (2024)
- [FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model](https://mlsanity.com/paper/2503.19839/) (2025)
- [Audio-visual Controlled Video Diffusion with Masked Selective State Spaces Modeling for Natural Talking Head Generation](https://mlsanity.com/paper/2504.02542/) (2025)
- [MVPortrait: Text-Guided Motion and Emotion Control for Multi-view Vivid Portrait Animation](https://mlsanity.com/paper/2503.19383/) (2025)
- [RoboStereo: Dual-Tower 4D Embodied World Models for Unified Policy Optimization](https://mlsanity.com/paper/2603.12639/) (2026)
- [Zo3T: Zero-Shot 3D-Aware Trajectory-Guided Image-to-Video Generation via Test-Time Training](https://mlsanity.com/paper/2509.06723/) (2025)
- [Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis](https://mlsanity.com/paper/2504.09885/) (2025)
- [InterAnimate: Taming Region-aware Diffusion Model for Realistic Human Interaction Animation](https://mlsanity.com/paper/2504.10905/) (2025)

## Abstract (arXiv)

Character animation is a transformative field in computer graphics and vision, enabling dynamic and realistic video animations from static images. Despite advancements, maintaining appearance consistency in animations remains a challenge. Our approach addresses this by introducing a training-free framework that ensures the generated video sequence preserves the reference image's subtleties, such as physique and proportions, through a dual alignment strategy. We decouple skeletal and motion priors from pose information, enabling precise control over animation generation. Our method also improves pixel-level alignment for conditional control from the reference character, enhancing the temporal consistency and visual cohesion of animations. Our method significantly enhances the quality of video generation without the need for large datasets or expensive computational resources.