FacEDiT: Face Editing & Generation

• FacEDiT is a suite of frameworks for face editing and generation that employ speech-conditional infilling, CLIP-guided vector fields, and diffusion inversion.
• It unifies editing and synthesis by leveraging deep architectures like Diffusion Transformers and U-Net, ensuring temporal continuity and identity preservation.
• Benchmark results demonstrate that FacEDiT surpasses traditional methods in photorealism, edit precision, and robust performance on both static and dynamic content.

FacEDiT refers to a set of frameworks for face editing and generation, each pioneering distinct algorithmic and representational strategies for photorealistic, attribute-controllable, or physically-consistent modifications to face images and videos. Several influential systems bearing the FacEDiT name have been proposed, focusing on speech-driven talking face synthesis, interpretable image editing, text-guided diffusion inversion, and vector-field-based manipulations across both static and dynamic visual content.

1. Unifying Editing and Generation via Speech-Conditional Facial Motion Infilling

The FacEDiT framework of (Sung-Bin et al., 16 Dec 2025) reinterprets talking face editing and generation as a unified speech-conditional facial motion infilling problem. Formally, let $A \in \mathbb{R}^{N \times D}$ represent the speech features (e.g., WavLM embeddings) and $F_1,\ldots,F_T \in \mathbb{R}^{75}$ denote frame-wise facial motion latents extracted with LivePortrait. A binary mask $M \in \{0,1\}^{T \times 75}$ zeros out frames to be infilled. The system is trained via conditional flow-matching (CFM):

$$\mathcal{L}_\mathrm{CFM} = \mathbb{E}_{t, F_0, F_1} \left\|\, v_\theta(x_t, t; A, (1\!-\!M)\odot F_1) - (F_1 - F_0) \right\|_2^2,$$

where $x_t = (1 - t) F_0 + t F_1$ with $t \sim \mathrm{Uniform}[0,1]$ and $F_0 \sim \mathcal{N}(0,I)$. The model learns to complete masked facial motion sequences such that the substitutions are temporally coherent and synchronized with the provided speech.

The backbone is a 22-layer Diffusion Transformer (DiT) with 16 heads (hidden dimension 1024, feedforward 2048), leveraging masked autoencoding strategies: variable-length spans of the motion lattice are masked at train time, forcing infilling from context. Speech features are integrated via multi-head cross-attention in each DiT layer, with rotary positional encodings, yielding superior lip-sync compared to early feature concatenation.

To enforce locality in temporal attention, a sparse bias matrix $B$ restricts each frame’s attention to a temporal neighborhood. Additionally, a temporal smoothness loss

$$\mathcal{L}_{\mathrm{TS}} = \frac{1}{T-1} \sum_{k=2}^T \left\| \hat{F}_1^k - \hat{F}_1^{k-1} \right\|_2^2$$

is added, with the full loss $$\mathcal{L}_{\mathrm{total}} = \mathcal{L}_{\mathrm{CFM}} + \lambda_{\mathrm{TS}} \mathcal{L}_{\mathrm{TS}}$$ and $\lambda_{\mathrm{TS}} = 0.2$.

This setup enables insertion, deletion, and substitution edits to talking faces with seamless transitions at boundaries. FacEDiT generalizes to both local edits and from-scratch generation, with strong identity preservation and speech alignment (Sung-Bin et al., 16 Dec 2025).

2. Dataset Construction and Evaluation for Talking Face Editing

FacEDiT includes FacEDiTBench, the first rigorous benchmark for talking face editing. It consists of 250 curated samples from datasets such as HDTF, Hallo3, and CelebV-Dub. Each instance includes:

• Original and edited video
• Aligned original and edited transcripts
• Synchronized edited speech

Editing operations include substitution, insertion, and deletion across short (1–3 words), medium (4–6 words), and long (7–10 words) spans. Several novel quantitative metrics are introduced: photometric continuity $P_\mathrm{cont}$ (pixel difference across edit boundaries), motion continuity $M_\mathrm{cont}$ (optical-flow discontinuity), and identity preservation ($\mathrm{IDSIM}$, cosine similarity in face embedding space). Routinely used metrics — LSE-D, LSE-C, FVD, LPIPS — are also reported for lip synchronization, video fidelity, and perceptual quality.

3. Physically Interpretable Face Editing via Vector Flow Fields

Another FacEDiT paradigm (Meng et al., 2023) formulates face editing as estimation of per-pixel spatial ($U_s$) and color ($U_c$) vector flow fields, enabling physically interpretable, text-guided edits. The flow fields are optimized for semantic alignment in CLIP embedding space:

• Explicit “rasterized” flow: $V \in \mathbb{R}^{H \times W \times 5}$, storing displacement and RGB shift per pixel.
• Implicit neural field: $F_\theta: \mathbb{R}^2 \to \mathbb{R}^5$, a continuous MLP mapping normalized pixel positions to their flow and color shift, with Fourier positional encodings.

The loss combines CLIP alignment, smoothness priors, color consistency (in HSV), identity preservation (ArcFace feature cosine), and (for implicit fields) a weight regularizer:

$$\mathcal{L}_{\mathrm{total}} = \lambda_{\mathrm{clip}}\mathcal{L}_{\mathrm{clip}} + \lambda_{\mathrm{sm}}\mathcal{L}_{\mathrm{sm}} + \lambda_{\mathrm{color}}\mathcal{L}_{\mathrm{color}} + \lambda_{\mathrm{id}}\mathcal{L}_{\mathrm{id}} + \lambda_{\mathrm{reg}}\mathcal{L}_{\mathrm{reg}}$$

This framework supports both iterative optimization and a one-shot prediction mode via a U-Net encoder and hypernetwork architecture, generalizing to video via homography-tracked flow field propagation. Compared to StyleCLIP and diffusion-based methods, FacEDiT yields higher identity consistency (ArcFace ID$\uparrow$ 0.90 vs. StyleCLIP 0.75), lower FID, and robust out-of-domain generalization (Meng et al., 2023).

4. Zero-Shot Text-Guided Diffusion Editing with ID-Attribute Decoupling

The FacEDiT variant of (Hou et al., 13 Oct 2025) introduces zero-shot face editing via joint ID-attribute decoupled diffusion inversion. The model separates face representations into ID (via a CLIP-Vision embedding) and attribute features (via CLIP-text of a caption or prompt). Both are used as conditioning in each U-Net cross-attention block, with attention split between text and image-derived embeddings.

The editing process is as follows:

• DDIM inversion recovers a latent $z^*_T$ from a face image $x_0$.
• Reverse diffusion (conditional on a new text prompt, original ID feature fixed) generates an edited image $x_0'$.
• ID preservation is enforced by fixing the image embedding during editing and optionally via a cosine similarity loss.

This system supports precise and structurally consistent single- and multi-attribute text-guided edits with high ID similarity (cosine $\sim$0.88) and editing accuracy ($\sim$85%), matching or surpassing StyleCLIP, Collaborative Diffusion, and other prior baselines on FFHQ and CelebA-HQ (Hou et al., 13 Oct 2025).

5. Classical Visual Representation and Editing Operators

A distinct classical interpretation (Lu et al., 2016) frames FacEDiT as a process of decomposing a face image into geometry, segmentation, albedo, illumination, and a high-frequency detail map:

• Geometry: fitted 3D morphable mesh
• Segmentation: mask for face/hair/background
• Albedo: per-pixel reflectance
• Illumination: spherical harmonics
• Detail map $D(x)$: defined as $I(x) - I_{\mathrm{shaded}}(x)$, capturing fine-scale surface detail

Edits are realized by modifying $\{\rho, F, \theta, D\}$, then re-rendering:

$$I_\mathrm{edited}(x) = \mathrm{render}(\rho(x), F, \theta) + D(x)$$

This supports photorealistic relighting, identity-preserving detail transfer, and non-parametric makeup transfer, as validated by user studies (e.g., for relighting, “fooling” rates for skilled users ranged up to 47%). Each component, particularly the explicit detail map, is shown to be essential for realism and fidelity (Lu et al., 2016).

6. Comparative Performance and Practical Significance

Benchmarking across editing and generation tasks shows that FacEDiT frameworks consistently surpass prior art in identity preservation, continuity, and edit precision. For the speech-conditional framework (Sung-Bin et al., 16 Dec 2025), on the FacEDiTBench editing benchmark, it achieves LSE-D=7.135, IDSIM=0.966, FVD=61.93, and boundary continuity metrics (P_cont=2.42, M_cont=0.80) significantly surpassing the next best baseline. For image-based attribute editing (Hou et al., 13 Oct 2025), quantitative metrics such as structure distortion, ID similarity, and classification accuracy set new state-of-the-art results.

A key insight is that unifying editing and generation as facial motion infilling, or as vector-field warping under textual control, provides not only expressive editability but also robustness and consistency unattainable by traditional GAN-inversion or non-decomposed diffusion methods. Physical interpretability, enforced local structure, and decoupling of identity from editable attributes are central to the effectiveness and controllability of current FacEDiT approaches.

7. Limitations and Prospects

Known limitations include restriction to in-place edits for vector-field methods (unable to insert new objects/accessories), dependency on CLIP embeddings (for semantics and identity), and identity drift under large deformations if regularization is insufficient. Promising future directions mentioned are the addition of generative branches for novel structure synthesis, integration of neural radiance fields for 3D-consistent editing, and development of prompt-specific motion priors for richer talking-face dynamics (Meng et al., 2023, Sung-Bin et al., 16 Dec 2025).