# X-UniMotion: Animating Human Images with Expressive, Unified and Identity-Agnostic Motion Latents

arXiv: https://arxiv.org/abs/2508.09383 · HTML: https://mlsanity.com/paper/2508.09383/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 28 of 28)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (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)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [Motion Representations for Articulated Animation](https://mlsanity.com/paper/2104.11280/) (2021)
- [Sapiens: Foundation for Human Vision Models](https://mlsanity.com/paper/2408.12569/) (2024)
- [Thin-Plate Spline Motion Model for Image Animation](https://mlsanity.com/paper/2203.14367/) (2022)
- [MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance](https://mlsanity.com/paper/2406.19680/) (2024)
- [OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models](https://mlsanity.com/paper/2502.01061/) (2025)
- [MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion](https://mlsanity.com/paper/2311.12052/) (2023)
- [Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head Synthesis](https://mlsanity.com/paper/2211.14506/) (2022)
- [X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention](https://mlsanity.com/paper/2403.15931/) (2024)
- [Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation](https://mlsanity.com/paper/2506.09350/) (2025)
- [Animate-X: Universal Character Image Animation with Enhanced Motion Representation](https://mlsanity.com/paper/2410.10306/) (2024)
- [Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model](https://mlsanity.com/paper/2504.08685/) (2025)
- [UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer](https://mlsanity.com/paper/2504.11289/) (2025)
- [DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid Guidance](https://mlsanity.com/paper/2504.01724/) (2025)
- [X-NeMo: Expressive Neural Motion Reenactment via Disentangled Latent Attention](https://mlsanity.com/paper/2507.23143/) (2025)
- [DragEntity: Trajectory Guided Video Generation using Entity and Positional Relationships](https://mlsanity.com/paper/2410.10751/) (2024)
- [High Quality Human Image Animation using Regional Supervision and Motion Blur Condition](https://mlsanity.com/paper/2409.19580/) (2024)

## Built on by (most cited first, 13 of 13)

- [SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation](https://mlsanity.com/paper/2511.19320/) (2025)
- [SCAIL-2: Unifying Controlled Character Animation with End-to-End In-Context Conditioning](https://mlsanity.com/paper/2606.10804/) (2026)
- [CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos](https://mlsanity.com/paper/2601.10632/) (2026)
- [3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation](https://mlsanity.com/paper/2602.03796/) (2026)
- [DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning](https://mlsanity.com/paper/2601.21716/) (2026)
- [Kling-MotionControl Technical Report](https://mlsanity.com/paper/2603.03160/) (2026)
- [Wan-Animate-2: Pushing the Application Boundaries of Character Animation](https://mlsanity.com/paper/2608.06009/) (2026)
- [Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image](https://mlsanity.com/paper/2603.14772/) (2026)
- [High-Fidelity and Long-Duration Human Image Animation with Diffusion Transformer](https://mlsanity.com/paper/2512.21905/) (2025)
- [IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation](https://mlsanity.com/paper/2602.07498/) (2026)
- [HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion](https://mlsanity.com/paper/2602.11117/) (2026)
- [EMOSH: Expressive Motion and Shape Disentanglement for Human Animation](https://mlsanity.com/paper/2606.28026/) (2026)
- [Reimagine Video Dynamics](https://mlsanity.com/paper/2609.36496/) (2026)

## Abstract (arXiv)

We present X-UniMotion, a unified and expressive implicit latent representation for whole-body human motion, encompassing facial expressions, body poses, and hand gestures. Unlike prior motion transfer methods that rely on explicit skeletal poses and heuristic cross-identity adjustments, our approach encodes multi-granular motion directly from a single image into a compact set of four disentangled latent tokens -- one for facial expression, one for body pose, and one for each hand. These motion latents are both highly expressive and identity-agnostic, enabling high-fidelity, detailed cross-identity motion transfer across subjects with diverse identities, poses, and spatial configurations. To achieve this, we introduce a self-supervised, end-to-end framework that jointly learns the motion encoder and latent representation alongside a DiT-based video generative model, trained on large-scale, diverse human motion datasets. Motion-identity disentanglement is enforced via 2D spatial and color augmentations, as well as synthetic 3D renderings of cross-identity subject pairs under shared poses. Furthermore, we guide motion token learning with auxiliary decoders that promote fine-grained, semantically aligned, and depth-aware motion embeddings. Extensive experiments show that X-UniMotion outperforms state-of-the-art methods, producing highly expressive animations with superior motion fidelity and identity preservation.