FluencyVE: Video Editing & Fluency Scoring

• FluencyVE is a video editing system that leverages Temporal-Aware Mamba modules and Bypass Attention to deliver temporally consistent edits.
• It employs state-space models for efficient global temporal coherence, reducing common artifacts like flickering compared to traditional methods.
• The framework achieves high performance with improved CLIP scores and user preferences, while also extending its design for speech fluency evaluation.

FluencyVE refers to a distinct set of technical innovations in both text-driven video editing and automatic fluency evaluation, integrating state-space modeling, low-rank efficient adaptation, and multi-modal architectural enhancements. The term commonly denotes either (a) a state-of-the-art one-shot video editing framework that leverages Temporal-Aware Mamba modules and Bypass Attention for efficient, temporally consistent editing, or (b) a family of self-supervised phonetic and prosody-aware learning approaches for fluency scoring and assessment in speech technologies. This entry emphasizes the video editing system as defined in "FluencyVE: Marrying Temporal-Aware Mamba with Bypass Attention for Video Editing" (Cai et al., 24 Dec 2025), with interconnections to speech-based assessment pipelines where appropriate.

1. Problem Setting and Motivation

FluencyVE addresses the limitation in recent video editing approaches that extend pretrained text-to-image diffusion models (notably Stable Diffusion) to video tasks via additional temporal attention modules. Prior approaches suffer from restricted temporal attention windows—only attending to a few adjacent frames—resulting in flickering artifacts and limited ability to capture global frame correlations. Furthermore, full-parameter or LoRA-based fine-tuning of attention layers incurs high computational overhead and risks degrading the high-fidelity generative power of the original image models.

The main goal is to perform one-shot, text-driven editing of an input video $V$ given its original prompt $P$, to yield an output $V^*$ that follows a new prompt $P^*$ while preserving per-frame spatial details and ensuring high temporal consistency.

2. Temporal-Aware Mamba Module Integration

The architectural core of FluencyVE is the replacement of all temporal self-attention layers in the inflated U-Net backbone (as in Tune-A-Video) with Temporal-Aware Mamba (TA-Mamba) blocks—a form of linear-time State-Space Model (SSM) sequence processing module.

State-Space Model Overview

Take the continuous-time SSM:

$h'(t) = A\,h(t) + B\,x(t), \quad y(t) = C\,h(t)$

Discretizing by zero-order hold gives:

$h_t = A_d h_{t-1} + B_d x_t,\quad y_t = C h_t$

where $A_d = \exp(\Delta A)$, $B_d = (\Delta A)^{-1} (\exp(\Delta A) - I)\Delta B$ for step $\Delta$.

Mamba Block Integration

Within FluencyVE, TA-Mamba modules process feature tensors along both spatial and temporal axes with a four-directional scanning strategy:

• Spatial-forward, temporal-forward (SF-TF)
• Spatial-forward, temporal-reverse (SF-TR)
• Spatial-reverse, temporal-forward (SR-TF)
• Spatial-reverse, temporal-reverse (SR-TR)

For each spatiotemporal flip $i$, the SSM transform is applied to the (optionally padded) feature tensor. Outputs from all four scans are fused (summed after reversing flipping).

Mamba’s O(NT) complexity—where $N$ is the number of tokens per frame and $T$ the number of frames—permits global modeling without the quadratic overhead of attention or its sparse approximations.

Learnable Frame Embeddings

To enable Mamba to discriminate between positions in the frame sequence, each frame tensor $x_t$ is padded with learnable frame-specific embeddings $\theta_{\text{frame}}^{(t)}$, extending the tensor shape from $H\times W \times C$ to $(H+2) \times (W+2) \times C$. This scaffolding improves inter-frame temporal content cohesion.

3. Bypass Attention: Low-Rank Parametric Adaptation

FluencyVE introduces "Bypass Attention" to enable efficient and stable cross-frame conditioning in the video editing diffusion U-Net, while restricting the trainable parameter count.

Low-Rank Query/Key Parameterization

Original cross-attention computes:

$$Q = W_Q v_i,\quad K = W_K [v_1;\, v_{i-1}],\quad V = W_V [v_1;\, v_{i-1}]$$

Instead of tuning $W_Q$ and $W_K$ (each $d\times d$), FluencyVE adds adapters $W'_Q,\, W'_K \in \mathbb{R}^{d\times k}$ with $k\ll d$ (typically $k=12$). The new low-rank attention map is computed as:

$A_\phi'(Q, K) = \text{Softmax}(Q (W'_Q W'_K^{T}) K^T / \sqrt{d})\, V$

Initialization uses SVD of $W_Q W_K^T$ or a Johnson-Lindenstrauss random matrix for approximating the original transformation.

Weighted Averaging of Attention Maps

Inference uses a weighted sum:

$$A_{\text{full}}(Q, K) = (1-\varphi)\, A_\phi'(Q, K) + \varphi\, A_\phi(Q, K)$$

where $\varphi\in[0,1]$ ensures a smooth training transition from pretrained attention to low-rank adaptation. During fine-tuning, only $W'_Q, W'_K$ are updated; all other parameters remain frozen.

Effects and Parameter Efficiency

This methodology enables fine-tuning with as few as 4.8 million parameters—approximately 6% of the full Stable Diffusion U-Net—leading to significant memory and compute savings (59% of memory and 29 s/inference for 32 frames), with minimal fidelity compromise.

4. Training and Inference Protocol

The video editing pipeline operates as follows:

• Input: Video frames ($T=32$), original and editing prompts, with each frame resized to $512 \times 512$.
• Model: Pretrained Stable Diffusion U-Net with pseudo-3D inflations, temporal self-attention replaced by TA-Mamba, and all cross-attention layers equipped with Bypass Attention.
• Objective: Follows the standard stable diffusion denoising loss generalized to video:

$$L = \mathbb{E}_{z_0,\,\varepsilon\sim \mathcal{N}(0,1),\, t\in [1,T],\, c=\text{CLIP}(P^*)}\|\varepsilon - \varepsilon_\theta(z_t, t, c)\|^2$$

• Schedule: 500 gradient steps, Adam optimizer, batch size 1, learning rate $3\times 10^{-5}$.
• Inference: DDIM sampling, 50 steps, classifier-free guidance scale 12.5.

No explicit temporal consistency loss is imposed; all temporal smoothness derives from the architectural innovations.

5. Quantitative and Qualitative Evaluation

Extensive evaluation on the LOVEU-TGVE benchmark (53 videos) includes CLIP-based metrics for frame consistency (CLIP-Frame Score), semantic match (CLIP-Text Score), user-preference models, and human studies. The following table summarizes FluencyVE’s results in comparison to prior work:

Metric	FluencyVE	Best Prior (Slicedit/VidToMe)
CLIP-Frame Score	96.47	95.62
CLIP-Text Score	29.42	28.84
User pref. temporal	22.7%	20.3%
User pref. fidelity	26.6%	22.3%
Pick Score	20.74	20.70

Qualitative analysis demonstrates fluent, semantically controlled edits, including complex background replacement, object swaps, and style transfers. Ablation studies confirm:

• TA-Mamba is critical for frame coherence (frame consistency drops 2.9–6.5 points without)
• Learnable frame padding outperforms fixed or null padding
• Optimal Mamba depth (2 layers) versus overfitting for deeper stacks
• Low-rank dimension $k=12$ gives best editing quality

6. Architectural Impact and Broader Influences

FluencyVE establishes a new design paradigm for temporally consistent, high-fidelity, and compute-efficient video editing. Key contributions include:

• Global temporal modeling via linear-time state-space modules rather than local, quadratic-cost attention
• parameter-efficient fine-tuning, made possible through low-rank adapter strategies and controlled attention mixing
• Preservation of original text-to-image model’s generative fidelity, enabling edit semantic generality
• Substantially improved inference speed—a nearly twofold reduction compared to prior art—without notable loss in accuracy

Analogous design principles have been adopted for speech-driven fluency evaluation (e.g., CBF-AFA (Wade et al., 25 Jun 2025)), where chunk-based segmentation, multi-SSL embedding fusion, and prosodic marker integration yield measurable advances in fluency classification.

7. Limitations and Future Research Directions

Current FluencyVE implementations require approximately 500 gradient steps for per-video adaptation, meaning zero-shot generalization is not realized. Memory demands scale with video length, constrained by Stable Diffusion’s native latent dimensionality. The fixed four-directional scan may be suboptimal compared to a learned scan schedule or dynamic routing through spatiotemporal content. Prospective research directions articulated in (Cai et al., 24 Dec 2025) include:

• Adapter-style or prompt-only video conditioning to bypass all gradient-based fine-tuning
• Dynamic frame scheduling or nonuniform rate handling via scheduling-aware embeddings
• Extending Mamba to hierarchical, cross-window spatio-temporal or multimodal (audio-video) processing
• Further reduction in adaptation latency for rapid or interactive video editing use cases

FluencyVE thus exemplifies the fusion of state-space modeling and parameter-efficient adaptation as a means to scale video and fluency assessment while minimizing resource demand and maximizing editability, temporal coherence, and semantic fidelity (Cai et al., 24 Dec 2025).