Motion Edits Framework

• Motion Edits Framework is a suite of computational and algorithmic techniques that disentangle and manipulate motion signals in videos with precise control.
• The framework employs neural models, diffusion pipelines, and geometric transformations to achieve non-rigid motion edits while preserving background fidelity.
• It addresses challenges such as temporal coherence, overfitting to original motion, and prompt-induced mixing by using motion embeddings and attention mechanisms.

A Motion Edits Framework comprises computational, algorithmic, and representational methods that enable rigorous, fine-grained modification of motion in videos. The term encompasses both the representation and disentangling of motion signals (e.g., trajectories, skeletons, or latent factors), and the deployment of neural or combinatorial pipelines (diffusion models, attention-based architectures, geometric transformations) to generate plausible, spatiotemporally coherent motion edits—ranging from non-rigid pose changes to structure-preserving camera/object manipulations—often with minimal auxiliary supervision or handcrafted masks. The frameworks surveyed here (NeuEdit (Yoon et al., 2023), VidMP3 (Mishra et al., 14 Oct 2025), MotionEditor (Tu et al., 2023), MotionV2V (Burgert et al., 25 Nov 2025), and others) form the current state-of-the-art in generative and editing-based video manipulation.

1. Goals and Core Challenges in Motion Editing

The principal goal of a Motion Edits Framework is to modify the motion profile of a target object or person within a video (e.g., changing gestures, poses, or dynamic trajectories), while preserving fidelity in unedited regions (background, unchanged structural elements). Non-rigid motion editing—such as altering limb poses or synthesizing complex movements (e.g., “jumping,” “thumbs-up”)—poses significant challenges:

• Over-fidelity to original motion: Standard diffusion-based editors tend to “lock in” pre-existing motions, making substitution or dramatic pose changes infeasible.
• Spurious reliance on source prompts: Mixing old and new motion factors due to entanglement of source and target semantics within prompt conditioning.
• Lack of explicit localization signals: Precise motion regions are difficult to identify and attenuate without auxiliary segmentation, masks, or keypoint annotations.
• Temporal coherence: Maintaining perceptual and feature-level consistency across frames during and after heavy motion manipulation.

These challenges motivate frameworks that decouple appearance from motion, neutralize uneditable factors, and architect model-agnostic protocols for motion transfer and synthesis (Yoon et al., 2023, Mishra et al., 14 Oct 2025, Tu et al., 2023).

2. Disentangling Motion: Neutralization and Factorization Schemes

A recurring motif is the formal separation of "motion factors" from appearance or background content, thereby localizing edits to relevant regions or semantics.

Textual and Visual Neutralization (NeuEdit)

Textual factors are disentangled by encoding a prompt $\mathcal{T}$ into token features $W\in\mathbb{R}^{M\times d}$ (e.g., via CLIP) and comparing against per-frame video features $V\in\mathbb{R}^{L\times d}$. A semantic importance score $z_{\mathcal{T}} = 1 - \mathrm{mean\_rows}(WV^T)$ identifies misaligned tokens, which are then attenuated:

$$w_n = z_{\mathcal{T}} \circ (\alpha w) + (1 - z_{\mathcal{T}}) \circ w,\quad 0\leq\alpha < 1$$

Visual factor identification uses patch-wise cross-attention maps, aggregating to a pixel importance mask $z_\mathcal{V}$ that guides a spatially localized Gaussian blur for motion attenuation:

$$\mathcal{V}_n = z_\mathcal{V} \circ (G*\mathcal{V}) + (1 - z_\mathcal{V}) \circ \mathcal{V}$$

This soft-regularization enables decoupling of motion signatures from source content, preventing the model from overfitting to previous motion during re-tuning (Yoon et al., 2023).

Priors for Motion Preservation (VidMP3)

VidMP3 extracts dense correspondence ($C_t$) and depth ($D_t$) priors per frame, forming an input

$$I_t = C_t \odot D_t \in \mathbb{R}^{H \times W \times 2}$$

which is then encoded to a compact “motion embedding” $M_t$ via a convolutional MotionGuide $\varphi_m$. By injecting $M_t$ into temporal self-attention (“Value” stream), the system preserves frame-to-frame motion even when high-level semantics are radically altered by the text prompt (Mishra et al., 14 Oct 2025).

3. Algorithmic Pipelines: Tuning, Inference, and Control

Broadly, the pipeline comprises two interacting phases: model-tuning (or parameter adaptation) on motion-neutral data, and inference (DDIM or DDPM-based sampling) under the true editing prompt.

NeuEdit Workflow

• Neutral prompt tuning: The model is fine-tuned (VQ-VAE encoding, noise injection, denoising loss minimization) on the neutralized data $(\mathcal{T}_n, \mathcal{V}_n)$.
• Inference: Motion editing is achieved by denoising from an inverted latent of the neutralized video, conditioned on the target prompt (Yoon et al., 2023).

VidMP3 and Control Branch Injection

• MotionGuide extraction: One-shot computation and injection of motion embeddings.
• Cross-domain/structure edits: The motion embedding mediates between content tokens and edited semantics to stabilize identity and motion (Mishra et al., 14 Oct 2025).

MotionEditor High-Fidelity Attention Injection

A dual-branch U-Net structure is deployed: a reconstruction branch maintains appearance priors, while an editing branch injects new motion (typically controlled via pose-skeletons). High-fidelity cross-attention mechanisms (foreground/localized) inject keys/values from the reconstruction branch into the editing branch, thereby maintaining the source's identity and background fidelity even under drastic motion changes (Tu et al., 2023).

Framework	Motion Factoring Method	Control Signal Type
NeuEdit	Token/pixel neutralization	Text + Visual (no mask)
VidMP3	Convolutional motion embedding	Correspondence + Depth
MotionEditor	Content-aware cross-attn adaptor	Pose (skeleton keypoints)
MotionV2V	Sparse trajectory deviation	2D tracking points

4. Representation and Conditioning Modalities

Motion Edits Frameworks leverage diverse representations for both source and target motion:

• CLIP-based semantic alignment (token relevance, visual-prompt alignment) (Yoon et al., 2023).
• Dense 2D/3D correspondences and depth (SD-DINO, DepthAnything, 3D point tracks) encode object-centric motion, enabling cross-domain retargeting (e.g., animal-to-animal or vehicle-to-animal transformation) (Mishra et al., 14 Oct 2025, Lee et al., 1 Dec 2025).
• Explicit pose skeletons (via keypoint detectors; ControlNet interfaces) for direct motion transfer or pose retargeting (Tu et al., 2023, Zuo et al., 7 May 2024).
• Sparse 2D or 3D trajectories for direct manipulation and counterfactual motion construction, as in MotionV2V (trajectory edits $\Delta\tau$ drive the generative process, supporting arbitrary timestamp propagations) (Burgert et al., 25 Nov 2025).
• Latent-space manipulations (embedding-level editing; key byproducts are minimal reliance on segmentation masks or optical flow) (Yoon et al., 2023, Bai et al., 20 Feb 2024).

5. Evaluation, Benchmarks, and Comparative Performance

Empirical validation spans a variety of large-scale benchmarks—DAVIS, UCF101, TGVE, VideoEdit, in-the-wild datasets—and leverages both automatic metrics (CLIP-based alignment, PSNR, LPIPS, SSIM, FVD) and human user studies.

• NeuEdit consistently outperforms Tune-A-Video, Video-P2P, and FateZero by 5–6 points on text-video CLIP, increases PSNR by 4–6, decreases LPIPS and FVD, and is preferred by a 60–86% vote (Yoon et al., 2023).
• VidMP3 attains highest subject identity, motion alignment, and temporal consistency in head-to-head human studies. Frame-to-frame consistency and cross-domain edit robustness are significantly improved (Mishra et al., 14 Oct 2025).
• MotionEditor achieves the best CLIP score (28.86 vs. 28.07), lowest LPIPS (0.273 source, 0.124 inter-frame), and is preferred (~78%) by annotators for both appearance and motion alignment (Tu et al., 2023).
• MotionV2V shows user win rates of ≈70% (content preservation and motion accuracy) against prior baselines, with superior L2, SSIM, and LPIPS metrics (Burgert et al., 25 Nov 2025).

6. Limitations and Future Directions

Current frameworks exhibit several sources of bias and constraints:

• Editing bias: Global scene shifts (e.g., background accidentally changing to “snow” for “snowboard trick” prompts) (Yoon et al., 2023).
• Still-pose and multi-subject limitations: Difficulty editing static poses or handling multiple entities simultaneously (Yoon et al., 2023, Mishra et al., 14 Oct 2025).
• Segmentation and mask accuracy: Some approaches (e.g., high-fidelity attention adapters) underperform when foreground segmentation is poor (Tu et al., 2023, Tu et al., 30 May 2024).
• Parameter sensitivity: Hyperparameters (e.g., text-deform α, blur σ, motion embedding λ) require careful tuning per scenario (Yoon et al., 2023, Mishra et al., 14 Oct 2025).
• Generalization to real-world video: Real-data fine-tuning and reliance on synthetic pair bootstrapping are current requirements; future end-to-end approaches may further reduce the need for explicit annotation or hand-crafted priors (Lee et al., 1 Dec 2025).
• Temporal drift/jitter: In cases of extreme or unseen editing, minor frame-level artifacts or incoordination can occur (Mishra et al., 14 Oct 2025, Burgert et al., 25 Nov 2025).

Future work targets learned factor disentanglers, explicit motion priors, multi-scale or instance-specific adaptation, and integration of weak supervision (masks or instance cues) for robust, fine-grained spatiotemporal edits.

7. Synthesis: Unifying Principles Across Motion Edits Frameworks

Motion Edits Frameworks systematically separate and control motion factors through explicit representation (semantic neutralization, pose/depth priors), neutralized fine-tuning, and advanced cross-modal conditioning—inverting traditional entanglement of motion/appearance in video generation. They universalize the editing process across object categories, input modalities, and deformation types, steering video diffusion models to enable non-rigid motion edits, robust cross-domain transfers, and high-fidelity content preservation without reliance on handcrafted or supervised auxiliary signals. The paradigm outlined in NeuEdit and its contemporaries delineates a research direction toward fully general, plug-and-play video motion editing—making complex, non-rigid, appearance-preserving motion synthesis accessible via purely algorithmic manipulation of source and prompt information (Yoon et al., 2023, Mishra et al., 14 Oct 2025, Tu et al., 2023, Burgert et al., 25 Nov 2025).