---
title: 'Wan-Move: Motion-Controllable Video Generation'
url: https://www.emergentmind.com/topics/wan-move-motion-controllable-video-generation
type: topic
---

# Wan-Move: Motion-Controllable Video Generation

Wan-Move is a motion-controllable video generation framework that achieves fine-grained, interpretable, and scalable motion guidance in generative video models. Its core innovation is the guidance of video synthesis by directly rendering the original visual condition features motion-aware via explicit propagation along dense point trajectories projected into latent space. This method enables high-fidelity video generation with precise, localized control of object and scene motion, and can be naturally integrated into large-scale image-to-video (I2V) backbones without architectural modification. Comprehensive evaluation on the MoveBench benchmark and public datasets demonstrates Wan-Move's state-of-the-art controllability, robustness, and efficiency compared to prior video generation systems [2512.08765].

## 1. Motion Control via Dense Point Trajectory Guidance

Wan-Move models scene and object motion using a dense grid of tracked point trajectories. Specifically, motion is represented as $\{\tau_i\}_{i=1}^K$ with each trajectory $\tau_i = \{(x^t_i, y^t_i)\}_{t=0}^T$ denoting the pixel-space location of track $i$ at video frame $t$. By default, trajectories are organized on a uniform $G \times G$ grid (e.g., $32 \times 32$), so $K = G^2$, covering the entire spatial extent of the first frame.

These trajectories are:

- **User-specified or automatically extracted:** Wan-Move enables user-guided motion or adapts to motion observed in a reference video.
- **Fine-grained and dense:** The grid approach covers local and global motion, supporting precise per-region edits and broad scene adjustments.
- **Flexible for compositional motion:** Trajectories can correspond to multiple objects or background, supporting multi-entity, multi-part, or holistic control.

Contextually, this dense trajectory guidance advances earlier motion control paradigms which typically leverage sparse manipulable signals (e.g., drag-points, bounding boxes, or low-frequency flow) and thus lacked fine spatial or temporal resolution [2512.08765].

## 2. Latent-Space Trajectory Projection and First-Frame Feature Propagation

Wan-Move's mechanism for motion guidance avoids adding specialized motion encoders. Instead, the trajectory set $\{\tau_i\}$ is projected directly into the VAE latent space where video synthesis occurs:

- **Spatial/temporal compression factors:** Given the VAE encoder's spatial ($f_s$) and temporal ($f_t$) compressions, trajectories are downsampled to yield latent trajectories $\tilde{\tau}_i = M(\tau_i)$.
- **Feature propagation:** For each trajectory $i$ and time-step $n$, the latent variable at $(n, h, w)$ is assigned the feature value corresponding to the original first-frame feature along $\tilde{\tau}_i$. When multiple tracks coincide at a latent index, the feature is assigned by random selection.
- **Updated latent condition:** This propagates the first-frame feature tensor $Z_{\text{image}}$ through time according to the trajectories, constructing a spatiotemporally aligned latent map dictating object/scene motion.

This process is architecture-agnostic, requiring no ControlNet or motion-encoder, and retains full compatibility with modern DiT/U-Net backbones. Notably, this sidesteps the complexity and scalability bottlenecks of systems relying on dedicated conditional modules or fused multi-modal handlers [2512.08765].

## 3. Integration and Inference in Large-Scale I2V Diffusion Models

Wan-Move inserts the motion-conditioned latent as a new image condition tensor within a pretrained I2V backbone (exemplified by Wan-I2V-14B):

- **No architecture change:** The propagated latent replaces the standard first-frame feature in the denoising diffusion model. The condition is concatenated along the channel dimension to the latent noise variable before each diffusion update.
- **Classifier-free guidance:** Sampling leverages conditional/unconditional scores with adjustable scale, maintaining performance benefits of state-of-the-art diffusion approaches.
- **Efficiency and scalability:** Motion conditioning occurs entirely as latent tensor manipulation, compatible with FSDP and large mini-batch scheduling. The backbone can be fine-tuned scalably under this paradigm (~2M videos, rapid convergence at ~30K steps) [2512.08765].

Pseudocode for the main inference loop is outlined as follows (see [2512.08765] for full algorithmic details):

```python
# Pseudocode sketch
Input: first frame I, trajectories {tau_i}, text prompt, CLIP/text features
1. Z_image = VAE_Encode(concat[I, zeros_T])
2. For each tau_i:
     Project to latent via M(tau_i)
     For n=1..T/f_t:
         Propagate: Z_image[n,h,w,:] = Z_image[0,h0,w0,:]
3. Sample x_T ~ N(0, I)
4. For t = T..1:
     v_uncond = v_theta(x_t, t, Z_image, z_global)
     v_cond = v_theta(x_t, t, Z_image, z_global, z_text)
     x_{t-1} = sampler_step(x_t, v_uncond + w*(v_cond-v_uncond))
5. Decode with VAE
```

## 4. Evaluation: MoveBench Benchmark and Quantitative Performance

The MoveBench benchmark is introduced for rigorous, large-scale motion-controllable video evaluation:

- **Composition:** 1,018 videos (5s duration, 81 frames, 480p), 54 diverse content categories, single/multi-object scenarios, annotated with dense point tracks and segmentation masks.
- **Metrics:** Video/image fidelity (FID, FVD, PSNR, SSIM), motion accuracy (end-point error, EPE), and user preference (2AFC studies) [2512.08765].
- **Results:** Wan-Move achieves FID 12.2, FVD 83.5, PSNR 17.8, SSIM 0.64, EPE 2.6 on single-object cases. Ablations reveal further gains from dense trajectory count at inference (up to EPE 1.1, PSNR 21.9, SSIM 0.79 at 1024-point density).

Comparative user studies (2AFC) demonstrate nearly unanimous preference for Wan-Move across motion accuracy (98%), visual quality (98.8%) versus strong baselines such as LeviTor, and at-par performance against high-end proprietary tools (Kling 1.5 Pro) [2512.08765]. MoveBench's scale and annotation fidelity enable robust, reproducible evaluation, setting a new standard for the community.

## 5. Ablation Studies and Comparative Analysis

Extensive ablations clarify the significance of Wan-Move's design choices:

| Strategy                  | FID (↓) | FVD (↓) | EPE (↓) | PSNR (↑) | SSIM (↑) | Latency |
|---------------------------|---------|---------|---------|----------|----------|---------|
| Pixel-level replication   |    —    |    —    | 3.7     | 15.3     | 0.56     |   —     |
| Random latent embedding   |    —    |    —    | 2.7     | 16.1     | 0.59     |   —     |
| Latent-feature replication (ours) | 12.2 | 83.5 | 2.6     | 17.8     | 0.64     |   +3s   |
| ControlNet injection      | 12.4    |   —     | 2.5     |   —      |   —      |  +225s  |

- **Latent-feature propagation delivers optimal accuracy and efficiency.**
- **Dense trajectory usage at inference monotonically improves EPE and spatiotemporal quality.**
- **Direct concatenation is computationally efficient versus adapters like ControlNet.**
- **Too many tracks at training may cause mismatch with sparse downstream tasks, while inference accepts extreme densification.**
- **Backbone/data-scale ablations confirm consistent superiority to MagicMotion, Tora, and other contemporary methods.**
- **Robustness is observed on extreme or out-of-domain scenarios (retaining up to 40% lower error)** [2512.08765].

## 6. Applications and Extensions

Wan-Move's latent trajectory guidance paradigm supports a broad set of controllable video synthesis operations:

- **Single-/multi-object dragging:** Direct region-of-interest manipulation through trajectory specification.
- **Camera motion:** Pan/dolly effects by specifying global trajectory fields.
- **3D object rotation:** Achieved by propagating first-frame features via depth-to-3D-to-2D projected tracks.
- **Motion transfer:** Application of extracted trajectories from source to arbitrary reference image frames enables motion-style transfer.
- **Compositional editing:** Multiple motion conditions can be superimposed, permitting complex scene or style replays [2512.08765].

This suggests broad utility for animation, visual effects, scientific visualization, and content creation, with direct user-editable motion fields and seamless integration atop scalable video diffusion models. The architecture's plug-and-play nature—with no need for motion encoders or fusion blocks—implies high extensibility to new backbones or modalities.

## 7. Position within the Motion-Controllable Video Generation Landscape

Wan-Move's introduction marks a shift in controllable video generation:

- **Relative to mask-based and optical flow methods:** Unlike mask guidance [2503.18386], explicit inpainting [2307.14073], or flow-based priors [2411.10836], Wan-Move achieves state-of-the-art controllability and fidelity with a minimal and generalizable procedural change at the latent condition level.
- **Advantages over token/disentanglement strategies:** Token-level approaches such as TokenMotion [2504.08181] yield fine-grained control but often require elaborate separate encoding and fusion pipelines; Wan-Move's direct manipulation in latent space achieves similar or better efficacy with simpler integration.
- **In contrast to reward or reinforcement mechanisms:** No reinforcement or reward-based objective or module is present in the Wan-Move pipeline; all supervision derives from a standard flow-matching loss on the motion-conditioned backbone.

Interpreted broadly, Wan-Move establishes latent-feature propagation along dense, user-editable trajectories as a foundational method for precise, efficient video motion control [2512.08765].

Source: https://www.emergentmind.com/topics/wan-move-motion-controllable-video-generation