VidMP3: Motion-Preserved Video Editing

• VidMP3 is a motion-preserved video editing technique that decouples motion from appearance using pose and depth priors for robust, cross-domain transformations.
• It integrates a MotionGuide module within a diffusion-based T2I framework to maintain temporal consistency and prevent subject identity drift during extensive edits.
• Evaluations show that VidMP3 outperforms previous methods in motion fidelity and flexibility, requiring minimal human intervention for structure-variable video edits.

VidMP3 refers to a video editing approach designed to preserve the original motion dynamics of a source video while allowing flexible and potentially drastic semantic and structural transformations of the subject or scene. The methodology decouples motion from appearance by representing motion with pose and position priors, enabling robust structural and cross-domain edits in automated video syntheses.

1. Definition and Core Principle

VidMP3 is defined as a motion-preserved video editing technique utilizing pose (dense correspondence maps) and position (depth maps plus positional encoding) priors to learn a generalized motion representation. This representation guides the generation of new videos, ensuring that while the edited content may diverge significantly in appearance or semantic category from the original, the motion profile remains temporally consistent. The system is specifically crafted to address the shortcomings of prior diffusion-based methods, notably temporal inconsistency and subject identity drift in structure-variable or cross-domain video edits (Mishra et al., 14 Oct 2025).

2. MotionGuide Module and Motion Representation

The MotionGuide module, denoted as φm, is the centerpiece of VidMP3’s motion abstraction. Each frame’s motion is encoded as the element-wise product of its dense correspondence map (C_N, capturing pose) and its depth map (D_N, capturing 3D position), concatenated with positional encoding P for 2D spatial location preservation. Processing follows a pipeline of convolutional layers (for local structure extraction), average pooling (scaled by object pixel occupancy α), and a final linear transformation. In the training regime, φ_m is optimized to minimize the discrepancy from the ground-truth 3D trajectory and rotation matrix T{N,6} using the quadratic loss:

$$\min_{\phi_m} \left\|T_{N,6} - \phi_m(C_N, D_N)\right\|^2.$$

This approach generalizes motion in a manner that is disentangled from subject shape and appearance, preparing VidMP3 for structure-variable and semantic edits without compromising motion fidelity.

3. Integration with Diffusion-based Video Generation

VidMP3 incorporates the learned motion priors into a temporally extended text-to-image (T2I) diffusion backbone. This is achieved by injecting motion signals into the temporal self-attention layers that underpin video generation. The value tensor in self-attention receives an additive motion signal weighted by parameter λ:

$$V = W^V\left(z_{i,j} + \lambda \phi_m(C_N, D_N)\right),$$

where $z_{i,j}$ is the feature at spatiotemporal index (i, j) and $W^V$ is the learned value projection. Self-attention is computed in the standard multi-head formulation:

$$\operatorname{Attention}(Q, K, V) = \operatorname{softmax}\left(\frac{Q K^{\top}}{\sqrt{d}}\right) V,$$

with $Q$, $K$ as projected queries and keys, respectively. Through this mechanism, explicit motion control is enforced across video frames, aligning temporal dynamics with the input source and preventing drift induced by visual or structural edits.

4. Comparison to Prior Editing Frameworks

VidMP3 is rigorously differentiated from earlier methods such as Tune-A-Video, VideoSwap, and FateZero. Prior approaches either directly operate in the structure/image space (resulting in leakage from the source, temporal disruption, and partial shape retention), or require manual intervention, such as keypoint selection. By employing dense correspondence and depth maps as external priors, VidMP3 eliminates the need for extensive manual processing and grants substantial cross-domain flexibility, handling major changes in object category, shape, or style while ensuring preserved motion consistency.

A comparative summary:

Approach	Motion Consistency	Structure Edits	Human Intervention
Tune-A-Video	Limited	Yes (with leak)	Often required
VideoSwap	Limited	Yes	Required
FateZero	Limited	Partial	Required
VidMP3	Robust	Extensive	Minimal

5. Evaluation and Experimental Results

Assessment of VidMP3 was performed using both automatic metrics and controlled human studies:

• Quantitative Metrics: CLIP-Score for image-text and image-image alignment measures semantic and content conformity post-edit; temporal consistency is established by evaluating motion alignment scores across frames.
• Qualitative Studies: Human evaluators rated Subject Identity, Motion Alignment, Temporal Consistency, and Overall Preference. VidMP3 was strongly preferred for retaining natural motion and achieving semantically faithful transformations.
• Editing Tasks: In both structure editing and cross-domain editing scenarios (e.g., swapping vehicles for animals while maintaining motion), VidMP3 exhibited superior performance—particularly in cross-domain settings where competing methods suffered from loss of motion or content identity.

6. Applications and Broader Implications

VidMP3’s methodology is suited to a wide spectrum of creative video editing applications:

• Subject Swapping: Enabling creators to replace the subject of a video (e.g., animal ↔ vehicle) while maintaining source motion trajectories.
• Structure and Style Transfers: Facilitates style or background edits, extending to personalized or artistic domains with stable temporal alignment.
• Automated Content Creation: Reduces production time and cost by minimizing manual labor and enabling rapid prototyping across diverse semantic edits.

This suggests the technique can enhance visual storytelling, allowing for the preservation of dynamic narrative even in cases where semantics or subject identity are fundamentally altered.

7. Limitations and Future Directions

Limitations acknowledged in the paper include:

• Subject Size Control: The current framework does not provide explicit control over the relative size of the edited subject, which may result in scale discrepancies post-edit.
• Dependence on Prior Extraction Quality: The performance is contingent on the accuracy of off-the-shelf correspondence and depth mapping algorithms; noisy or poor priors may degrade motion guidance.
• Research Directions: Proposed future research includes exploring diffusion correspondence features for motion representation, expanding to multi-subject editing, and refining attribute controls (such as explicit size adjustment or trajectory specification).

A plausible implication is that further improvements in correspondence map algorithms or the adoption of more sophisticated priors could yield even greater fidelity and control in motion-preserved video editing.

Summary

VidMP3 formulates a novel motion-guided video editing paradigm using pose and position priors to disentangle motion from appearance. Through direct integration of these priors into a temporally self-attention-driven diffusion model, it enables robust, temporally consistent, and semantically flexible video editing. Quantitative and qualitative evaluations establish its clear advantages over prior methods, especially in cross-domain and structure-variable editing scenarios. Limitations relating to scale control and prior quality suggest promising future research directions for broader applicability and controllability within automated video content creation (Mishra et al., 14 Oct 2025).