Pixel-Perfect Puppetry: Precision-Guided Enhancement for Face Image and Video Editing
Authors: Yan Li
Zhenyi Wang
Guanghao Li
Wei Xue
Wenhan Luo
Yike Guo
Venue: ICLR 2026 Poster (2026)
Keywords: FaceVideo Editing
Face Image Editing
Precision Guidance
Primary area: generative models

Abstract: Preserving identity while precisely manipulating attributes is a central challenge
in face editing for both images and videos. Existing methods often introduce visual artifacts or fail to maintain temporal consistency. We present **FlowGuide**,
a unified framework that achieves fine-grained control over face editing in diffusion models. Our approach is founded on the local linearity of the UNet bottleneck’s latent space, which allows us to treat semantic attributes as corresponding
to specific linear subspaces, providing a mathematically sound basis for disentanglement. FlowGuide first identifies a set of orthogonal basis vectors that span
these semantic subspaces for both the original content and the target edit, a representation that efficiently captures the most salient features of each. We then
introduce a novel guidance mechanism that quantifies the geometric alignment
between these bases to dynamically steer the denoising trajectory at each step.
This approach offers superior control by ensuring edits are confined to the desired
attribute’s semantic axis while preserving orthogonal components related to identity. Extensive experiments demonstrate that FlowGuide achieves state-of-the-art
performance, producing high-quality edits with superior identity preservation and
temporal coherence. Our code is available at: https://github.com/yl4467/flow_edit.

Published as a conference paper at ICLR 2026
PIXEL-PERFEC' PUPPETRY: PRECISION-GUDDE EN- HANCEMENT FOR FACE IMAGE AND VIDEO EDITING
Yan Li'
Zhenyi Wang² Guanghao Li
Wei Xue' Wenhan Luo Yike Guo
The Hong Kong University of Science and Technology. UUiiversity of Central Florida, Tsinghua Shenzhen International Graduate School, Tsinghua University
ABSTRACT
Preserving identity while precisely manipulating attributes is a central challenge in face editing for both images and videos. Existing methods often introduce vi- sual artifacts or fail to maintain temporal consistency. We present FlowGuide, a unified framework that achieves fine-gu fine-grained control over face editing in diffusion models. Our approach is founded on the local linearity of the UNet bottleneck's 's la- tent space, which allows us to treat semantic attributes as corresponding to specific linear subspaces, providing a mathematically sound basis for disentanglement, FlowGuide first identifies a set of orthogonal basis vectors that span these seman- tic subspaces for both the original content and the target edit, representation that efficiently captures the most salient features of each. We then introduce a novel guidance mechanism that quantifies the geometric alignment between these bases to dynamically steer the denoising trajectory at each step. This approach offers superior control by ensuring edits confined the desired attribute's semantic axis while preserving orthogonal components related to identity. Extensive experi- ments demonstrate that FlowGuide achieves state-of-the- art performance, produc- ing high-quality edits with superior identity preservation and temporal coherence. Our code is available at: https: //g thub. cwmmyl/l//// 44677 ow _ow_eiit
INTRODUCTION
Face attribute editing has emerged as an essential task in computer vision, with applications ranging from film production to virtual reality, social media content, and digital avatars (Zhan et al., 2023
Kim et al. 2023 Yao et al. 2021 Zhang el al., 2018a Zhu et al. 2020 Ye et al.) (025a). This task encompasses both face image editing (FIE) and face video editing (FVE), each presenting unique challenges. FIE demands precise attribute manipulation while preserving identity and avoiding un- intended artifacts (Shen et al. 2020 Wang al., 2022). FVE inherits these challenges but adds the critical requirement of temporal consistency across frames (Wang et al.) 2024 Ceylan et al., (023). Current methods often struggle to satisfy all these constraints simultancously. To address this, we propose unified, pixel-level solution for both FIE and FVE that enhances editing precision while maintaining identity and temporal coherence.
Early approaches to face editing predominantly relied GAN-based methods (Tzaan al. 2022 Patashnik et al., 2021; Karras et al.) 2019; [hen et al., 2020). which utilize pre-trained StyleGAN models and GAN inversion techniques (Karras al. 2020 Xia al. 2022). These methods map input images or video frames into a latent space where edits can be applied. However, the quality of edits heavily depends the accuracy of GAN inversion, which often struggles to faithfully recon- struct the original input, leading to identity loss and editing artifacts (Preechakul et al., 2022). For video editing, GAN-based methods face additional challenges maintaining temporal coherence, often resulting in flickerigg or inconsistent edits across frames.
Recent advances in diffusion models have shown superior performance in face editing tasks (Batifol et al.) 22025: Kim et al., 20233 Preechakul et al.) [0022] Zhang et al.) 2023) Croitoru et al.) 2023). These methods perform editing as a conditional generation process, where target attributes are pro- gressively introduced during the denoising steps. While diffusion models offer better reconstruction
"Corresponding author

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quality and more stable generation compared to GANs, they still lack precise control over the editing process (Zhao et al. 2024 Yu et al. 2023). Without proper constraints, introducing target attributes can inadvertently affect other facial features, identity, or background elements problem that be- comes particularly pronounced in video editing where such errors accumulate across frames.
To address these limitations, propose FlowGuide, unified framework that achieves precise face editing by introducing a novel guidance mechanism operating within the diffusion model's latent space. Our approach is founded on the local linearity of the UNet bottleneck's 's latent space (Park) et al. 2023: Kwon et al. 2022), which allows us to treat semantic attributes as corresponding to specific linear subspaces. To disentangle identity from attributes, our Latent Basis Extraction (LBE) module first identifies set of orthogonal basis vectors that span these key semantic directions for both original and edited content. The core of our method is an Implicit Basis Guidance (IBG) mech- anism that quantifies the semantic change by measuring the geometric alignment between these two sets of basis vectors. This alignment score informs a corrective update the predicted noise at each denoising step, effectively steering the generation trajectory along the desired attribute's semantic axis while preserving components orthogonal to it, which correspond to identity and other preserved features. This ensures precise, localized edits for images and naturally extends to temporally coher- ent modifications for videos.
We summarize the contributions of proposed method as follows:
We propose FlowGuide, a unified framework for face image and video editing that intro- duces novel guidance mechanism to achieve precise attribute control in diffusion models.
We treat semantic attributes as linear subspaces within the UNet bottleneck's 's latent space, designing a Latent Basis Extraction (LBE) module to identify orthogonal basis vectors that span these subspaces to isolate the identity from the attributes in the latent space.
We introduce an Implicit Basis Guidance (IBG) mechanism that computes the geometric alignment between these bases to dynamically steer the denoising trajectory, which fines edits to the target attribute's 's semantic axis while preserving the identity.
Extensive experiments demonstrate that FlowGuide achieves state-of-the-art editing qual- ity, with superior identity preservation, attribute modification, and temporal coherence.
2
RELATED WORK
2.1 INVERSION-BASED IMAGE EDITING
Inversion-based editing in diffusion models began with deterministic methods like DDIM in- version (Song et al.) 2020). To improve identity preservation, subsequent optimization-based approaches like Null-text Inversion (NTI) (Mokady et al., 2023) and Prompt Tuning Inversion (PTI) (Roich et al. 2022) fine-tuned text embeddings, though at significant computational cost. To address this inefficiency, a variety of optimization-fue methods were developed. Negative Prompt Inversion (NPI) (Miyake al. 2023) and ProxNPI (Han et al. 2024) bypass direct optimization of embeddings, while others like PnP Inversion (Ju et al. (023)) and Noise Map Guidance (NMG) (Cho) use guidance directly incorporate reconstruction differences into the editing update.
al. 2024)
More recent works have explored alternative strategies beyond direct deterministic inversion. For instance, Edit Friendly (EF) (Huberman al.) (024) and its successor LEDITS+ (Brack et al.) 2024) employ random inversion to achieve good reconstruction without requiring attention map adjustments. Concurrently, methods like h-Edit (Nguyen et al. 2025) have introduced hierarchical frameworks for more granular semantic control. Despite this progress, most training free methods remain limited to text-guided editing and struggle to achieve precise control over attributes without affecting non-target regions. Our work addresses this by introducing guidance mechanism that operates on the fundamental geometric structure of the latent space, enabling more precise and disentangled control.
2.2
FACE VIDEO EDITING
Face video editing (FVE) aims to modify facial attributes in videos while preserving identity and temporal consistency. Early FVE methods often relied GANs, particularly StyleGAN (Karras)
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et al. 2019). Approaches like StyleCLIP (Patashnik et al. 2021 and Stitch it in Time (STIT) (Tz
aban et al. 2022) perform GAN inversion to project video frames into StyleGAN's latent space for editing. However, these methods are often limited by the quality of GAN inversion Patashnik et al. (2021 [Shen et al. (2020). which can lead to identity loss and artifacts, and they struggle to maintain temporal coherence across frames PPreehakul et al. (2022).
Recent works have further advanced diffusion-based video editing through various ap-
proaches Geyer al. (2023); Yang et al. ( Qi al. (2023); Ouyang al. (2024); Kara et al.
2024);
Anand et al. 2025): : Li et al. (2025): Ye et al. (2025b): Kong et al. (2024). RAVE (Kara
al.
2024) introduces randomized noise shuffling for fast and consistent editing but focuses pn-
marlly
on semantic scene editing rather than fine-graines grained facial control. IP-FaceDiff (Anand et al.)
2025
specifically targets identity preservation in facial videos, while Qffusion (Li et al.] 2025) em-
ploys quadrant-grid attention learning for controllable portrait editing. V-LASIK (Shalev-Arkushin
al. 2024) addresses the specific challenge of consistent glasses removal using synthetic data.
Other notable advances include (Liao & Deng 2023) extends ControlNet to video generation with cross-frame attention, and (Lu et al. 2024) performs high-fidelity video editing via multi-source diffusion. While effective, Luffusion-based approaches often lack fine-grained control, leading to unintended modifications of non-target attributes. Our work addresses this limitation by introduc- ing mechanism for precise, guided control within the diffusion framework, ensuring that edits confined to the desired attributes while preserving identity and temporal stability.
3 METHODOLOGY
3.1 PRELIMINARIES; DIFFUSION-BASE BASED EDITING
Let Xo represent an input frame. Our method supports processing multiple frames simultaneously; for simplicity, we Xo to denote the input in the following sections. Our dual-path framework, and diffusion-based editing in general, operates by first inverting Xo into noisy latent represen- tation, which is then denoised. Each frame processed independently through this pipeline. We reconstruction path with a superscript
denote variables associated with the and variables for the editing path with a superscript
The inversion process is a deterministic DDIM-based procedure that progressively adds noise to create the starting latent for the reconstruction path, Xf. The transition from less noisy latent X to more noisy latent X; under the original condition Cedit is modeled as:
(X7 Cedta) = NXT:: (oda)
(I)
where the mean is a function of the predicted noise e" c"(X; (X[-1,t, Ceate)): Me Ho(TT-11 1.t, Cedst) (X - a./v1 aaa" t,
ar is the noise schedule coefficient.
The denoising process generates the edited image by iteratively removing noise, guided by target condition C The editing path starts from the same noisy latent as the reconstruction path, X². Xy. The denoising step for the editing path is defined as:
ps (Xf ,²)) = (X 2XE,4
(2)
where the mean μa is function of the noise (Xf;, 1, (o) predicted under the target condition.
To improve consistency, the edit-friendle guidance (Huberman et al. 2024) can be introduced into the denoising process. which explicitly links the reconstruction and editing paths. The intuition is to ground the editing process in the reconstruction process to prevent it from deviating too far. The odit-friendly guidance is defined as:
X = X (o(T t, Cedi)) + po (Xf, Et Co)
(3)
While this technique enforces a strong structural prior from the reconstruction path, it often proves to be too restrictive. The guidance is not adaptive; it does not dynamically measure how much the edit should differ from the original. Consequently, such methods often lack the fine- tine-grained con- trol needed to robustly preserve identity while making significant. targeted attribute changes. This fundamental limitation motivates our work, which introduces a more advanced, adaptive guidance mechanism to enhance editing accuracy.
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3.2 OVERVIEW OF FLOWGUIDE
Our method operates through two parallel processes: reconstruction and editing to achieve pre- cise attribute manipulation while preserving identity, as illustrated in Figure [ Both processes invert the input frames to noisy latents but use different conditions during the denoising phase. The reconstruction process uses the original conditions (Corg. C*) to establish an identit--reseerving ref- erence path. Concurrently, the editing process uses the target conditions (Cedit, C to introduce the desired attribute modifications.
Video Frames Image
Constructed Frames or Image
+C"
Corg
X
E
Inversion
(a) Reconstruction
of.of
LBE
Edit Frames Image
Cedit
Decempooee
a 818.0
X
+
26
E
Inversion
5,
(b) Editing with Implicit Basis Guidance
The framework of proposed FlowGuide. (a) The reconstruction process shows how original frames are inverted to noisy latents Xy with original condition Corg. then denoised back with condition C", establishing the baseline for identity preservation.
Figure 1:
(b) The editing process of our method: first invert latent representations to Xi with editing condition Cedit, during denoising with target condition C we extract latent basis vectors from the UNet bottleneck layer, and apply implicit basis guidance to ensure edits are confined to target attributes.
During each step of the parallel denoising, our I Latent Basis Extraction (LBE) module (Section 3.3
applied to the UNet bottleneck of both paths. This yields two sets of basis vectors: V for the original content and V for the edited content. Our key contribution, the Implicit Basis Guidance (IBG) mechanism (Section 3.4). then computes the similarity between V and V to quantify semantic change. This similarity dynamically steers the denoising direction of the editing path, ensuring modifications are confined to target attributes while preserving all other characteristics from the reconstruction baseline.
This dual-process framework naturally extends to both single images and video sequences, where temporal consistency emerges from the coherent application of basis guidance across frames. De- tailed inversion procedures for image and video modalities are provided in Appendix E.2 and F.1 respectively.
3.3 LATENT BASIS EXTRACTION
Building on the dual-process framework described above, the noisy representations X7 and X are fed into pre-trained UNet F predict the noise of each frame. Within this architecture, we use F. and Fa to denote the encoder and decoder components of the UNet. respectively. Since the process of extracting the latent basis is the same for both paths (though the resulting bases /" and V differ), we use XG. as an example for simplicity. To streamline the presentation, we let X' represent Xç. H denote the latent variable, and C represent C at time step l.
The latent variable 7t in the bottleneck layer of the U-Net has been shown to exhibit a locally linear structure (Kwon et al., 2022), which makes it suitable for using the Euclidean metric to measure changes in 7 (Kim al. 2023). In the denoising process, the transformation from the input repre- sentations to the latent space can be expressed as F. X. H. where F. maps the input and the editing conditions C to the latent variable H. However, since X' contains lot of information unrelated to the specific editing direction, the variability it introduces into H. might not align with the desired editing directions. To overcome this issue, focus primarily on how C (the editing
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condition) influences H. effectively isolating the impact of the target attribute from other unrelated aspects of X. This approach enables us to better control the editing process by only adjusting the components of H that relevant to the intended changes, ensuring more precise and consistent video edits.
Since the video editing process incorporates the addi- tional condition into the denoising steps, C directly in- fluences key features in the latent space TH. where T() denotes the vector space. Therefore, our goal is to iden- tify the local latent vectors exhibit significant variability within the tangent space of the latent variable H. denoted as TH- By focusing on these local latent vectors, we can effectively capture the key aspects of the editing direction that drive changes in the latent space, ensuring that the manipulation of the video aligns with the intended attribute modifications while preserving other important details such identity and background. We provide detailed analysis of the impact of the latent basis on the editing process in Appendix C
IG
Jzc
XA X
{vi..... .. Un Te that
(V
Decompose
Figure 2: The illustration of extracting the latent basis.
The linear relationship between C and H. can be expressed transformation is described by the Jacobian matrix Jc, which captures how a vector v E Te is mapped vector TH through the relation u = Jev. Given the local linearity of H. in the latent space, the pullback of H allows us to assign meaningful geometric structure to C, enabling more precise control over the editing process by understanding how changes in C affect the latent space H. the norm of can be measured:
linear map: Tc Tre This linear
|| =< u, u >== T Je Jev
where < u, u >H== u the dot product of u defined in the Euclidean space with the local linearity of H.
The vectors V "U,,..... } Tc that maximize [|u| can be derived through the singular value decomposition (SVD) of the Jacobian matrix Jc UAYT, as illustrated in Figure 2 Here, V = (Ui...., 2 represents the right singular vectors of Jc. U = {uI..... un } E Tx represents the left singular vectors, and A is diagonal matrix of singular values, it has Jeu; A4u,, The extracted latent basis vectors V = (ui,...,,h, Vn) correspond to directions in the latent space that are highly responsive the conditions encoded in C, offering key insights into how the video editing process responds to specific attributes. Henceforth, we obtain the latent basis responses corresponding to the conditions C and C' denoted as V = {or ..., e and V {uf,-.. ..., ) respectively. Having extracted these basis vectors, the next critical step is to use them to guide the denoising process by our Implicit Basis Guidance, detailed in the following section.
3.4 IMPLICIT BASIS GUIDANCE
To quantify the degree of alignment between the original and manipulated conditions, we measure the similarity between the latent basis vectors V" and V This similarity provides a means to assess the extent of changes introduced during the editing process. Among various similarity metrics (Pear- son correlation, Spearman correlation, etc.), we adopt cosine similarity to measure the relationship between yr and De:
Pe(V", cos (0)/TT, cos(o)
(5)
The choice of cosine similarity motivated by its ability to capture directional relationships between latent basis vectors while remaining robust to magnitude variations, which is particularly important for measuring semantic changes in the latent space. We empirically validate this choice by com- paring cosine similarity with Pearson correlation and Spearman correlation in Table I with detailed comparative analysis provided in Section 4.1.2.
The latent basis associated with different conditions is extracted described in Section and the similarity between V" and D can be utilized to provide more precise guidance for video manipula- tion. We denote the computed similarity as E o VO), refer to Equation 5 This similarity
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serves key factor in adjusting the manipulation process, ensuring that only the target attributes are modified while preserving other important characteristics like identity and background. Given that the similarity ç &c(V", V*) measures the impact of the conditions on the model, propose using this similarity as guidance to regulate the denoising process.
To ensure that the magnitude of deviation between c and c" is proportional to the similarity a, use the similarity value to determine which regions should be edited. Specifically, when the latent bases very similar (high a), only small regions should differ between the two paths; when the bases are dissimilar (low a), larger regions can be modified. To achieve this, we employ a dynamic threshold rather than fixed one. We select the the cutoff value as X. This allows us to construct binary mask and compute the final guided noise:
quantiles from the matrix Je" <"| and denote
(6) where è is the final noise prediction used in the denoising step, blending the editing noise ef with the reconstruction noise e" according to the mask MM. This method enables us to focus edits on regions with significant latent basis diff differences, effectively filtering out less relevant information to ensure the target attributes are modified while maintaining the integrity of non-target features.
M je- - c¹I < X, غ é= + M (c"-
EXPERIMENT
4.1 FACE IMAGE EDITING
4.1.1 EXPERIMENT SETTING
Dataset. To evaluate the performance of face image editing, we select 500 images from the CelebA dataset Liu et al. (013).. We employ GPT-4o to generate comprehensive editing prompts encom- passing five distinct editing tasks: "Add Sunglasses", "Add Makeup". "Age Progression", "Hair Color Modification", and "Add Smile". The detailed construction methodology for editing prompts is provided in the Appendix EE.3 Furthermore, to assess the generalizability of approach beyond facial editing, we conduct additional evaluations on the PIE-Bench dataset Ju et al. (2023) to mea- sure general-purpose editing capabilities, the results on PIE Benchmark can refer to Appendix E.5
We compare our proposed method against state-of-the-art image editing approaches, in- cluding h-Edit[Nguyen al. (2025), Miyake al. (2025). NMG al. (2024), EF Hubermann iet al. (2024), and PnP Inv Ju et al. (2023). To ensure fair and consistent evaluation across all meth- ods, we employ p2p control to enhance reconstruction performance for each baseline.
Baseline.
For evaluation, we follow the evaluation setting in Nguyen et al. (2025), three main aspects are considered: 1) edited image quality, 2) editing effectiveness, 3) consistency between the original image and the edited image. To evaluate the edited image quality, we compute PSNR, LPIPS, and SSIM on non-edited regions. To measure the editing effectiveness, both standard CLIP similarity between the edited image and text and directional CL.IP similarity between the edited image and text are used. To evaluate the consistency between the original image and the edited image, we compute DINO feature distance and the MSE distance between the original image and the edited image.
Metric.
4.1.2 QUANTITATIVE RESULTS
Quantitative results are presented in Table [ comparing our method against five state-of-the-art baselines. We evaluate three variants of our model: one using cosine similarity (our primary pro- posal), and two others using Spearman and Pearson correlation for guidance. Both the cosine and Spearman variants demonstrate superior trade-off between editing effectiveness (CLIP similarity) and identity preservation, significantly outperforming the Pearson variant, which produces overly aggressive edits that degrade identity. This outcome confirms our theoretical analysis (Section 3) angular and rank-based similarity metrics (Cosine, Spearman) better capture the geometrie relation- ships in the latent space, providing more precise guidance than Pearson correlation, which is limited to linear relationships.
Across all methods, an inherent trade-off exists between editing strength and consistency. As il- lustrated in Figure 3 our cosine and opearman-based FlowGuide variants achieve more favorable balance than strong baselines like h-Edit, attaining higher quality and identity scores while remain- ing competitive edit alignment. While the Spearman variant achieves the highest scores in quality

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and consistency, the cosine variant provides slightly better balance with edit strength, making our recommended approach. Both demonstrate that our geometrically-grae grouunded guidance mechanism enables more controlled and robust editing.
Table I: The text-guided face image editing performance of different editing methods.
Edited Image Quality
Edited Performance
Consistency
Method
PSNR (f)
LPIPS (1)
SSIM (t)
CLIP Sim (t)
Local CLIP (f)
DINO Dist (L)
MSE Dist (4)
EF Huberman et 2024
20.012
0.2028
0.7184
20.714
0.1225
0.0349
0.0109
PhP Inv li et al 2025
20.370
0.1343
0.7967
20.530
0.1296
0.0271
0.0106
NMG
14.679
0.3437
0.5673
21.666
0.1348
0,0831
0.0360
NP Miyake el al 422
11.999
0.4747
0.4031
20.918
0.1409
0.1257
0.0665
b-Edh Nggyenn
22.078
0.1034
0.8341
19.707
0.1546
0.0193
0.0078
FlowGuide (Pearson)
16.988
0.2223
0.6988
22.157
0,1451
0.0539
0.0224
FlowGuide (Spearman)
24.129
0.0882
0.8642
17.831
0.1437
0,0161
0.0055
Flow (Cosine)
23.160
0.0965
0.8448
19.391
0.1479
0.0166
0.0060
4.1.3 QUALITATIVE RESULTS
We visualize the face image editing results comparing our proposed method with bascline methods in Figure 3 Our method achieves superior editing quality and maintains better consistency between the original and edited images, though with slightly lower CLIP similarity between the edited image and text prompt. These visualization results align with the quantitative findings in Table [ con- firming that our method achieves more precise and consistent editing, thereby demonstrating the superiority of our proposed approach in face image editing. We provide additional visualizations of our method's face image editing capabilities Appendix E.4 (Figure
"A Person without with sunolassoss
"a serions smiling person"
"A porsoN Withon makoopp
Original
Ours
h-Edit
PnP
NP
NMG
EF
Figure 3: The comparison of the edited face image between our method and the baseline methods.
4.2
FACE VIDEO EDITING
4.2.1
EXPERIMENT SETTING
Dataset. We evaluate the performance of our proposed FlowGuide real-world videos sampled from the HDTF dataset (Zhang et al., (02T) and the VoxCeleb dataset (Nagrani et al., 2017). Specif- ically, we randomly select 20 videos from each dataset, ensuring diversity across gender, age, and skin tones. Each video consists of hundreds of frames, from which we randomly sample 32 consecu- tive frames for each evaluation. The selected frames aligned and cropped following the approach in (Tzaban et al. 2022 Kim et al. 2023). and subsequently resized to resolution of 256 x 256.
We compare method extensively with several previous state-of-the-art baselines. We choose diffusion- editing method DVA (Kim et al.] 2023) and transformer. based method Latent-trans (Yao al. 2021). For GAN-based methods, choose STIT (Tzaban et al.) 2022)
Baseline.

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TCSVE Xu al. (2022). PTI (Roich al. 2022) and StyleCLIP (Patashnik et al., 2021). Some of the baseline methods are designed for image editing, we adapt them into the video editing paradigm (the details refer to Appendix FF2.. It is important note that, for fair comparison of the reconstruction abilities of different editing methods, the original videos are used solely as input.
Metric. For comprehensive evaluation of proposed FlowGuide and the baseline methods, utilize range of evaluation metrics. For the evaluation reconstruction performance, we use SSIM (Wang al. 2004), LPIPS (Zhang al., 2018b), MSE and FID. For time consistency eval- uation of manipulated videos, we apply TL-ID and TG-ID (Tzaban et al. 2022). For evaluating video editing performance, we use the Identity Preservation Rate (IPR), Target Attribute Change
Rate (TACR) Yao et al. [021), and CLIP score.
Original
AE
A
STIT
Latent
DVA
RAVE
Ours
Figure 4:
Comparison of editing performance of our FlowGuide to the previous video editing methods for editing direction 'Libstick'.
4.2.2 QUANTITATIVE RESULTS
To thoroughly evaluate the editing capabilities of our proposed FlowGuide compared baseline methods, we choose two general editing directions ("Smiling",, "Mustache"). We compute and re- port the average values of key evaluation metrics, such as Identity Preservation Rate (IPR), Target Attribute Change Rate (TACR), and CLIP score, for both our method and the baseline approaches. The results, summarized in Table 2 illustrate how effectively each method handles these editing tasks, offering insights into their relative performance across different editing scenarios. The recon- struction ability of different methods are presented Appendix FF
As shown in Table 2 our proposed FlowGuide achieves the highest Identity Preservation Rate (IPR), highlighting its effectiveness in maintaining identity information during editing process. Notably, RAVE shows significantly lower performance compared to face-specifi methods. This performance gap highlights a fundamental challenge: general video editing methods are designed for semantic scene editing and large-scale motion manipulation, where spatial consistency requirements are rel- atively relaxed. In contrast, face video editing demands extremely precise pixel-level consistency to the input video and fine-grained control over subtle facial attributes while maintaining identity. The human visual system is highly sensitive to facial inconsistencies, making it particularly chal- lenging apply general video editing approaches face manipulation tasks. Additionally,
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The editing ability of our FlowGuide and baselines on HDTF and VoxCeleb datasets. The reported values are the mean of two editing directions ("Smile", " "Mustache").
Table 2:
HDTY
VoxCeleb
(1)
())
CLIP-Score: (*)
TI-IDD (t)
TG-ID (t)
(1)
(J)
CI.I--Soore (f)
TI.ID (t)
TG-ID (t)
StyleCLIP IP
0.8013
0.0329
0.7676
0.9997
0.9995
0.7051
0.0337
0.7670
0.9998
0.9995
STIT
0.8214
0.0341
0.7501
0.9866
0.9490
0.8131
0.0339
0,7383
0.9997
0.9994
PTI
0.7540
0.7646
0.8238
0.8122
0.7140
0.7627
0.7586
TCSVE
0.9413
0.0342
0.7566
0.9864
0.9770
0.8723
0.0029
0.7218
0:9813
0.9077
Latenr-ranss
0,7515
0,0348
0.7450
0.9978
1.0000
0.2070
0.0335
0,7395
0.9999
1,0000
0.9244
0.9977
RAVE
0,7005
0.0338
0.7295
0.8621
0.7731
0,6812
0.034]
0.7301
0.8598
D. 0,7684
FlowGuide
0.9667
0,0338
0.7777
1,0001
1,0000
0.9033
0.0335
0,7607
1,0000
1,0000
method demonstrates comparable temporal consistency the baseline methods, further validating its robustness in preserving video quality.
4.2.3 QUALITATIVE RESULTS
Qualitative comparisons are presented in Figure 4 with additional results in Appendix [.9 The visualizations demonstrate our method's ability perform precise, localized edits. As shown, Glowe effectively modifies the target attribute while preserving the subject's identity, non-target facial features, and the background. This high degree of control ensures the character's identity mains intact and the scene's 's original context is undisturbed.
To further showcase the robustness and gener- alizability of our method, we provide results for multiple, distinct edits on a single video in Appendices F.7 These examples highlight our model's s ability to handle challenging, dynamic scenarios with intricate backgrounds, substan- tial head movements where many state-of-the- art methods falter. Our approach consistently retains the stylistic elements of the original video, producing exceptionally natural edits that blend seamlessly with the original content. This ability to maintain coherence across diverse and challenging edits underscores the effectiveness of our guidance mechanism. We further report the computation efficiency of our method and the base- line methods in Appendix [T66
Table 3: The ablation results of our FlowGuide on HDTF dataset with two editing directions ("Smile" and "Mustache").
Methed
IPR
TACR(.)
CLIP SioP- (?)
TL:ID (f)
TG-ID
w/oLBE
0.9311
0.0311
0.7437
0.9925
0U975
w/o I6G
0.370
0,0337
0.7773
0.9770
0.8854
w/o Soth
0.870
0,0009
0.7540
0.9550
0.8557
FlowGuide
0J5100
0,0299
0,929
4.3 ABLATION STUDY
We conduct an ablation study of the video edit- ing tasks to analyze the contributions of our two core components: Latent Basis Extraction (LBE) and Implicit Basis Guidance (IBG), with results presented in Table and Figure 5 First, evaluate the importance of LBE by remov- ing the module and computing similarity di- rectly on the raw latent variables. This pre-
GT
All
Aip
liff
w/o IBG
vents the model from isolating attribute-specffic features; the resulting guidance is too diffuse to apply the desired edit (low Target Attribute Change Rate), demonstrating that LBE is cru- cial for identifying the correct semantic direc- tions for modification. A detailed analysis of the impact of the latent basis on the editing pro- cess is provided in Appendix
w/o LBE
w/o Both
Ours
Next, we remove the IBG module while re- taining LBE to assess its distinct role. With- out IBG, the model correctly identifies what to change but lacks spatial control, applying edits indiscriminately across the entire frame. This
AUM
Figure 5: The ablation results of Glow when apply editing ineection""miles

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Published as a conference paper at ICLR 2026
leads to significant identity degradation (low IPR) and uncontrolled attribute changes, highlighting IBG's critical role in providing the spatial guidance necessary for localized edits. When both compo- nents are removed, the model's performance collapses entirely, producing distorted and ineffective results. These findings confirm that LBE and IBG are integral and complementary: LBE provides the semantic what to change, while IBG provides the spatial where apply it.
EVALUATION FOR LATENT BASIS
Figure shows the similarity values SV Vo) between latent bases different denoising timesteps for two editing directions ("Beard" and "Big Lip"). We observe that similarity is higher at larger timesteps and decreases denoising progresses. This behavior validates several key proper- ties of our method:
Linearity across timesteps. The smooth, continuous decrease in similarity suggests that the local linearity assumption holds consistently throughout the denoising process. Sharp discontinuities would indicate breakdown of linearity, but the gradual transition demonstrates stable geometric structure in the latent space. At early timesteps (high noise), latent bases V" and Ve are more similar because noise dominates the latent space, making the lin- ear approximation particularly valid. As denoising pro- gresses, the bases diverge smoothly, indicating that the linear region accommodates the growing semantic differ- ences between reconstruction and editing paths.
Trand Board neet of 112"
Thardd
big Up
Similarity
0.7
501
0.27
0:41
0.1
Edlt
Time Step
Adaptive guidance mechanism. The varying similarity timesteps demonstrates why our adaptive thresh- old mechanism (using 1 - a quantiles) is crucial. At early stages where similarity is high (a z 0.8-0.9), our method applies minimal editing, preserving the coarse structure. At later stages where similarity drops (a 0.4-0.5),
Figure 6: The similarity between the la- tent basis of the original video and the manipulated video evolves as the de- noising progresses.
larger editing regions are permitted, allowing fine-grained attribute manipulation. This adaptive behavior provides robustness even the linearity assumption weakens at certain timesteps.
CONCLUSION
In this work, we introduced FlowGuide, a unified framework for high-fidelity face editing in both images and videos. We addressed the key challenge of disentangling identity from editable at- tributes by leveraging the geometric properties of the diffusion model's latent space. Our approach treats semantic attributes linear subspaces and uses novel guidance mechanism, consisting of Latent Basis Extraction (LBE) and Implicit Basis Guidance (IBG), to steer the generation process. By operating on the geometric alignment of these subspaces, our method confines edits target attributes while preserving identity and temporal coherence. Extensive experiments demonstrate that FlowGuide achieves state-of-the-arr performance, striking superior balance between edit fi- delity and attribute modification. Our work opens a promising direction for more controllable and geometrically-gren grounded manipulation in generative models.
LIMITATION DISCUSSION
While our method achieves state-of-the performance, several limitations remain. First, operating in the diffusion model's 's latent space can lead to over-smoothing in high-motion scenarios and unre- alistic blending when adding hard-edge accessories like sunglasses. This represents a fundamental trade-off, we achieve superior identity preservation but at the cost of some visual artifacts. Second, perfect attribute disentanglement remains elusive; correlated features in training data lead to minor unintended changes of edits. Finally, method inherits the limitations of the underlying diffusion model, restricting edits to what the latent space can represent and requiring finettuning for optimal performance new domains.
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8 ACKNOWLEDGMENT
The research was supported in part by Theme-based Research Scheme under Grant T45-205/21-N from Hong Kong RGC, and Generative AI Research and Development Centre from InnoHK.
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The source code and more showcases of paper be
found at:
A USE OF LLMS
We used LLMs for tasks such as improving grammar, refining phrasing, and ensuring consistency in language. The core ideas, experimental design, and interpretation of results solely the work of the authors. All final content was reviewed and edited by the authors to ensure its accuracy and originality.
B RELATED WORK
B. LATENT SPACE ANALYSIS
The study of latent spaces has gained significant attention in recent years. the field of Generative Adversarial Networks (GANs), researchers have proposed various methods to manipulate the latent space to achieve the desired effect the generated images (Ramesh et al. 2018 Patashnik et al.) 2021 Abdal et al.] 2021 Shen & Zhou 2021 Härkönen al. 2020). More recently, several studies have examined the geometrical properties of latent space GANS and utilized these findings for image manipulations (Choi et al., 2021 Zhu al. 2021). Some studies have applied Riemannian
geometry to analyze the latent spaces of deep generative models (Arvanitidis et al.) 2017 2020
Chen al.) 2018 Lee & Park 2023 Lee al.) 2022 Shao et al. 2018)) (Shao al. 2018)
proposed a pullback metric on the latent space from image space Euclidean metric to analyze the latent space's geometry. This method has been widely used VAEs and GANs because it only requires a differentiable map from latent space to image space. And (Park et al. 2023) extend it into diffuison models (DMs) to investigate the geometry of latent space of DMs to facilitate the image editing. However, it is challenging for the pullback metric to accurately capture the geometry of the latent space from the image space, the image space contains excessive information, making difficult to identify the correct directions for editing.
C EVALUATION FOR LATENT BASIS
By calculating the similarity between the latent basis of the original video and the manipulated video under specific editing direction, we can quantify the degree of change introduced during editing. This similarity metric serves guide for the editing process, enabling more precise adjustments and ultimately improving the overall quality the edits. Figure 7 we present the change in similarity values different denoising time steps for two editing directions: "Beard" and "Big Lip."
As observed, the similarity is higher at larger time steps and lower smaller time steps. At larger time steps, in- creased noise in the latent space causes the original and edited videos share similar latent bases. Conversely, at
lend Start oeae
smaller time steps, reduced noise allows the latent basis better capture encoding features, creating greater dis- tinction between original and edited content.
Similarity
Furthermore, this observation aligns with the understand- ing that the model initially focuses on cow-frequency sig- nals during the early stages of the generative process, where the similarities between the original and edited videos are more pronounced. Over time, the model pro- gressively shifts its attention to high-frequency signals, which highlight the introduced target attribute and the dif- ferences between the two videos. This result reinforces the common view of the coarse-to-fin behavior exhibited by diffusion models throughout the generative process (Kim et al. 2023
622
031
Time Step
Figure 7: The similarity between the la- tent basis of the original video and manipulated video evolves as the de- noising progresses.
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person withont with sunglasses"
"A porson with darte lolonde hair"
"A porson withost with makeoup"
"A vonng elderly porsow"
"A serions smile person"
Figure 8: The editing sample visualization of methods with different editing instructions.
D DEEPFAKE DETECTION ANALYSIS
To assess the detectability of videos edited by our method and baseline approaches, we conduct ex- periments using the state-of-the-arr deepfake detection model from FaceForensicst+ Rossler et al.) (211). Understanding the detectability of edited content is important for responsible Al develop- ment and helps identify potential misuse scenarios.
Table Deepfake detection rates using FaceForensics** detector both image and video editing methods. Higher detection rates indicate easier identification of manipulated content. Lower rates suggest more natural-looking edits. Image Editing Methods
Method
Type
Detection Rate (%)
Naturalness
Ours
Diffusion
78.0
Most Natural
h-edit NeeditNguyee Nguyen et al. 22025
Diffusion
79.5
High
Ju et al. (2023)
Diffusion
81.0
Moderate
EF Huberman al. 2024
Diffusion
82.1
Low
Video Editing Methods
Method
Type
Detection Rate (%)
Naturalness
Ours
Diffusion
72.5
Most Natural
STIT Tzaban al.
2022
GAN
85.5
High
DVA Kim et al. 2023)
Diffusion
91.5
Moderate
Latent-trans
al. 2021
Transformer
99.5
Low
As shown in Table 4 our method consistently achieves the lowest detection rates both image and video editing tasks. For image editing. method achieves detection rate of 78.0%, outperforming h-edit (79.5%), PnP (81.0%). and EF (82.1%). For video editing, our method achieves 72.5%, significantly lower than baseline methods (85.5%-99.55)
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These results suggest that our edited content produces more natural-looking results that are harder to detect as manipulated media. We hypothesize that this due to: (1) our method's superior preservation of temporal consistency in videos and natural facial dynamics throughout the sequence, and (2) better semantic coherence in edited regions that maintains the statistical distribution of real content. The lower detection rates in video editing (72.5%) compared image editing (78.0%) demonstrate that our temporal modeling provides additional naturalness that is harder for detectors identify.
E
FACE IMAGE EDITING EXPERIMENT
Original
Ours
h-Edit
PnP
NP
NMG
EF
"Chamoge the mankcey
Remove mint leaves"
"Make this red sawirre! reading loook"
Figure 9: The editing results of different method on the task "change object" (the first row and the second row) and "add object" (the third row).
E.1 EXPERIMENT SETTING
Implementation Details:
For all face image editing experiments, we use the pre-trained Stable Diffusion v1.4 as our base model. We employ DDIM inversion for the encoding process, with both the inversion and denoising sampling steps set to 50.
key aspect our method the use of distinct conditions for the inversion and denoising stages. For the initial DDIM inversion, both the reconstruction and editing paths use the same condition, Corg = Cedit. which corresponds to the source prompt (e.g., "a person with long hair") as defined in Appendix E.3 This ensures that both processes start from identical noisy latent, Xy.
During the denoising phase, the conditions diverge to enable guided editing. The reconstruction path uses the original condition C (derived from the source prompt), while the editing path the target condition C (derived from the target prompt, e.g., "a person with short hair"). This setup allows our guidance mechanism measure and control the semantic changes between the two paths. All experiments were conducted on single NVIDIA RTX 4090 GPU.
Implementation of Baseline Methods: We the following baseline methods for face image editing tasks, and all the baseline methods are implemented using the same base model and inversion process, the hyper-panne parametrs are to the same the original paper:
h-Edit (Nguyen et al. 2025): A hierarchical editing framework that decomposes the edit- ing process into multiple semantic levels for more granular control over different attributes.
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Original
Ours
A-Edit
PnP
NP
NMG
"Change the shape of the cake from round to star"
"chango the woodon crate shape to roand'
"Tura the image oil paintting
Figure 10: The editing results of different method on the task "change attribute content" (the first row), "change attribute pose" (the second row) and "change style" (the third row).
Original
Ours
A-Fdit
PnP
NP
NMG
EF
"Chaage the raven from pink green"
"change the horse to bronee horse"
"Twwn the maud lntto grass"
Figure 11: The editing results of different method on the task "change attribute color" (the first row), "change attribute material" (the second row) and "change background" (the third row).
PnP Inversion (Ju et al. 2023): A plug-and-play method that avoids costly optimization by injecting features from the original input directly into the denoising process to guide the generation.
Noise Map Guidance (NMG) (Cho et al., 2024): Leverages the structure of noise maps from the inversion process guide the denoising steps, aiming better preserve fine de- tails and image structure.
Negative Prompt Inversion (NPI) (Myyaee al. 2023): An efficient optimization-fra method that uses the original text prompt embedding to approximate the null-text embed- ding, speeding up the inversion process.
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Sow'co
Edit
Soorco
Exitt
Sovereo
Edit
"a "aleon retriever (holdnogy flower)
"a gless red [dkmnee-[wkke [win)) the bocach"
sitfing on the groend in Front Fonce"
"wirite plate with (frettj [piaze] it*
"slcckerries (and strawborrios]
"S.frodf: [blke]] the dark""
"two [origamm sittikug brsandin"
a slanted [rassy] moontan: bicuyle on the rosid froet of bwilding"
"a (mcoricott) [loo]] pappy Wi preppediin blue towel"
"fron) houss"
Figure 12: The editing results of method PIE benchmark.
Edit Friendly (EF) (Huberman et al. 2024): A technique that employs random inversion rather than deterministic inversion, achieving high-quality reconstruction without needing attention map modifications.
E.2 INVERSION PROCESS FOR IMAGE EDITING
We use the DDIM inversion process to invert the original image into the latent space for face image editing tasks. DDIM inversion is deterministic process designed to find noise latent Xr that, when used as the starting point for the standard DDIM denoising process, reconstructs the original input image Xo with high fidelity. This allows for the manipulation of real images by first inverting them into the latent space and then denoising them with modified text prompt condition.
The inversion process is iterative, progressively adding noise the input image X₂ over timesteps. Starting with X, (the clean image), for each step t from 1 to T, we calculate the next latent X, based on previous latent X,-1. 1. The core the process relies on using the pre-trained noise prediction network to estimate the noise that would have been present step 1, and then using this estimate to project forward to step
The update rule for each step of the DDIM inversion is as follows:
αe X1-1
(VI- -g
ā āx-1)
X1
V ar-1
+
ce((X-11t -,, corg)
ār-1
where:
X is the latent from the previous step (with Xo being the initial image).
ā₂ = I a; is the cumulative product of the noise schedule coefficients 1 - Bi. (0(X1-1, Corg) is the noise predicted by the UNet model for the latent X 1 at timestep 1, under the original condition Corg-
By iteratively applying this equation from = 1 to T, we obtain a trajectory of latents X2....... ). The final latent, Xý. serves as the encoded representation of the original image. For editing, this latent is then used as the starting point for the denoising process, but guided by a new target condition Codit generate the manipulated image.
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E.3 CONSTRUCTING FACE IMAGE EDITING PROMPTS
To systematically evaluate the performance of our face image editing framework, we constructed standardized set of editing prompts. Each prompt is designed test specific, common facial attribute modification. The construction process for each data point follows a consistent structure, including source prompt that describes the original image and target prompt that describes the desired edited outcome.
For each editing task, we define the following components:
Source Prompt: A brief textual description of the initial state of the attribute in the source image (e.g., "a person without sunglasses," "a person with long hair"). This prompt is used generate the original condition, Corg-
Target Prompt: A corresponding textual description of the desired state of the attribute after editing (e.g., "a person with sunglasses," "a person with short hair"). This prompt is used to generate the target condition, Cedit-
Editing Instruction: A clear, human-readable instruction that specifies the transformation to be performed (e.g., "Add sunglasses to the person's 's face," "Change the hair length from long to short").
We curated diverse set of common face editing tasks to ensure comprehensive evaluation. The primary editing axes we considered include:
Accessories: Adding or removing items like sunglasses.
Hairstyle: Modifying hair length or color (e.g., long to short, dark to blonde).
Age: Changing the perceived age of the person (e.g., young to elderly).
Makeup: Applying or removing makeup.
Expression: Altering facial expressions (e.g., serious to smiling).
This structured approach to prompt construction allows for consistent and reproducible experiments, ensuring that all baseline methods are evaluated under the same conditions and that the performance of our model can be fairly assessed across range of common and important face editing scenarios.
E.4 ADDITIONAL VISCALIZATION OF FACE IMAGE EDITING
We provide additional visualization of face image editing results in Figure It can be seen that our method can successfully edit the face image, and the editing results are natural and realistic.
E.5 PIE BENCUMARK RESULTS
E.5.1 MAIN RESULTS
The quantitative results on the PIE benchmark, summarized in Table 5) highlight the efficacy of our proposed method. A key observation across the baselines is the inherent trade-off between edit conformance and fidelity to the original image. For instance, methods such as PnP Inversion demonstrate strong performance in consistency metrics (DINO Dist, MSE Dist), indicating minimal deviation from the source image, but this comes at the cost of lower alignment with the target prompt (CLIP Sim). Conversely, methods like EF and NMG achieve high CLIP similarity by making more aggressive edits, which compromises image quality (PSNR, LPIPS) and consistency.
In contrast, our FlowGuide strikes more effective balance across these competing objectives. achieves the second-best performance on average across all quality and consistency metrics, sur- passed only by the highly conservative PnP Inversion, while simultaneously maintaining a compet- itive CLIP Similarity score. This suggests that our gcometrically-grounde not merely preserving the original image, but enabling precise, targeted edits. By confining mod- ifications the intended semantie regions, method preserves the overall fidelity and structure of the source image without sacrificing the desired semantic change, thereby achieving a superior position on the fidelity-con cenity-ccoformance trade-off curve.
guidance mechanism is
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Table 5: The text-guided image editing performance different editing methods.
Edited Image Quality
Edited Performance
Consistency
Method
PSNR (f)
LPIPS (1)
SSIM
CLIP Sim (1)
Lucal CLIP (1)
DINO Dist (4)
MSE Dist (1)
EF Huberman al.
2024
17.624
0.1771
0.7306
27.127
0.1520
0,0661
0.0229
NMGC
A744
14.075
0.3189
27.053
0.1563
0.0492
NP Miyake et at
0725
14,510
0.3262
0.6028
24.182
0.1968
0.1266
0.0446
Edi
Nguyen
19.657
0.1397
0.1878
0.0156
Glide
22.021
0.1006
0.8047
26.422
0.1754
0.0349
0,0091
E.5.2 VISUALIZATION
Comparison Results Visualization: We present the comparison of editing results of different method on the all eight tasks in PIE benchmark. As shown in Figure 9 Figure I0 and Figure I1 our method achieves the best performance on all the tasks. In Figure 9 "change object" and "add object" are included. In Figure [O. "change attribute content", "change attribute pose" and "change style" are included. In Figure I "chngee attribute color", "change attribute material" and "change background" are included. We can see that our method can successfully edit the object in the image, and the editing results are more natural and realistic than the baseline methods.
Visualization of Our Method: We provide additional visualization results of our method on PIE benchmark. As shown in Figure T2 our method can successfully edit the object in the image, and the editing results are more natural and realistic than the baseline methods.
F FACE VIDEO EDITING EXPERIMENT
F.1 INVERSION FOR VIDEO EDITING
To encode the conditions related to the target attribute into the video, first obtain the embedding for the original frames using a pre-trained condition generator, denoted as &gi Corg = Ex(X). Next, utilize pre-trained encoder & jointly encode the video frames and the associated embedding into conditions (the process of obtaining Cedit can refer Ap- pendix F.4. which are then used as conditions during the de- noising process:
3
+4.
c
E C
X
E
C" EX, C = (x(X, Corg + Cedit)
(8)
where C and C are utilized as conditions for the denoising of the original and manipulated frames, respectively. And the input representations at time step t = 0 are derived using frozen input encoder & X5 (x, Cr) and Xú (X, X5 represents the original input representation and Xf serves as the conditional imput representation for manipulation.
Figure 13:
The architecture of en- coder E, consists of & & and &
After obtaining the encoded input representations XJ. X6. the forward diffusion can be applied:
x(xilx)) = N(T: val (1 ax)+), & = Fe (x5,
(9)
where Fo denotes pre-trained noise estimator, and X;¹ represents the noisy representation dif- fusion step t. The parameter a, controls the noise scale at step t. Through this process, X is generated by the forward diffusion process. Similarly, the forward diffusion process is applied to X to obtain Xç.
F.2 EXPERIMENT SETTINGS
Implementation details. FlowGuide uses a diffusion autoencoder with a UNet as the noise esti- mator. To enhance the model's ability to reconstruct the background in face videos, we fine-tune the pre-trained diffusion autoencoder from (Kim et al. 2023) on the HDTF dataset (the details of fine- tuning the diffusion autoencoder can refer Appendix F.5p. Note that during the editing process, the pre-trained diffusion autoencoder model remains frozen. We use the DDIM sampler, setting the the reverse time step and the inference time step 50. The batch size for inference set to 1, and all
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inference is performed on 4 RTX4090 GPUs. For face video editing, didn't use the edit friendly guidance, the consistency is realized solely by our method solely. We report the inference time of method and the baseline methods in Appendix F.6
Implementation of Baselines. We select several state-of-the-ane methods for comparison: the diffusion- based editing method DVA Kim al., (2023) and the mansformer-based method Latent- trans Yao et al. (202T). For GAN-based methods, we include
et al. (2022). TCSVE|Xu
al. 2022). PTI Roich et al. (2022). and StyleCLIP Patashnik al. (2021).
is important to emphasize that, for a fair evaluation of reconstruction capabilities, all methods only use the original videos as input. None of the methods have access the original videos during the output generation phase, ensuring that the reconstruction quality reflects the true performance of each editing approach without reliance on the input data.
DVA Kim et al. (2023): For the implementation of DVA, use their CLIP-based editing method, and the editing scale is set to 0.25 as recommended in their paper, and the input texts of the CL.IP-baee editing method are "Face" and "Face with ** for original video and the target manipulated video, other experiment settings are used the default settings.
TSCVE Xu et al. 2022) We use the default settings recommended, and the frames of the videos are resized to 512. We also use the output frames directly, without blending them into the original video frames.
Latent-trans Yao et al. (202T): For the implementation of Latent-trans, we set the scaling factor as 1.5 and the other settings are kept as recommended. And we use the output frames directly, the output frames are not blended with the original input frames.
STIT et al.] (0022): We run edits with stitching tuning, and the edit ranges set to 10101, the parameter 33 is 0.2 and the outer mask dilation mask_dilation is 50. Other settings are kept as recommended. The output frames are used directly as well.
Roich et al. (2022): We use the default settings recommended, and the frames of the videos are resized to 1024. We also use the output frames directly, without blending them into the original video frames.
StyleCLIP Patashnik al. 2021): We train the mappers of input videos with the default settinfss and use the attributes as the descriptions. Then we use the default settings to edit the videos and the output frames are used directly.
F.3 RECONSTRUCTION EVALUATION
For video editing tasks, it is essential that the model can accurately reconstruct the original video from its encoded representation. To achieve this, fine-tune the pre-trained diffusion autoencoder to enhance its ability to accurately reconstruct both the background and human face. We evaluate the reconstruction performance of Flow against all baseline methods on the HDTF and VoxCeleb datasets, with the results reported in Table 6
Table 6:
The reconstruction performance of our FlowGuide and baselines on HDTF and Voxceleb
datasets. The reported values are the mean of the averaged per-frame measurements for each video.
HDTF
VoxCeleb
Method
SSIM (t)
LPIPS (Q)
MSE (L)
FID (J)
SSIM (t)
LPIPS (4)
MSE (J)
FID (L)
StyleCIIP
0.6653
0.1984
0.0125
136.52
0.4830
0.3028
0.0183
233.60
STIT
0.5202
0.3978
0.0617
244.60
0.6669
0,2769
0.0513
179.27
PTI
0.6347
0.2476
0.0256
168.12
0.4737
0.3434
0.0337
227.43
Latent-trans
0.7035
0.1571
0.0068
137.70
0.6017
0.2208
0.0076
217.96
DVA
0.9448
0.0584
0.0003
33.531
0.9696
0.0130
0.0006
44.458
FlowGuide 0.9715
0.0108
0.0001
23.432
0.9779
0.0095
0.0004
24.840
Table clearly demonstrates that our method achieves significantly better reconstruction perfor- mance compared to baseline methods on both the HDTF and VoxCeleb datasets. This highlights the superior ability of model to faithfully reconstruct fine details in both the background and
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(a) Origin.
(b) Ours.
(c) DVA.
(d) Latent.
(c) PTL
(1) STIT.
(g) StyleCLIP.
Figure 14: The comparison of the images reconstructed by our FlowGuide and five baseline methods with the original input image.
human face, underscoring its robustness and generalizability. We further provide visual compar- ison of the reconstruction performance across different methods in Figure [4] It can be seen from Figure 14 that baseline methods struggle to either preserve the identity of the characters retain the background features. contrast, our FlowGuide shows clear superiority in reconstructing the face videos, delivering more accurate restoration of both facial identity and background details. This enhanced reconstruction ability makes Guide particularly effective for tasks where maintaining consistency between the original content and the edited results is crucial, highlighting its robustness in vidco manipulation.
Original.
EE
STIT:
Latent.
FlowGuide. DVA.
Figure 15:
Comparison of editing performance of our FlowGuide to the previous video editing methods for editing direction 'Libstick'.
F.4 OBTAIN CONDITION
To edit videos using diffusion-basee models, the editing directions must first be mapped into condi- tions. We achieve this by leveraging the pre-trained CLIP model Radford et al. (2211 encode the editing directions. We denote original condition as C" (see Equation 8) and represent the input with this original condition Xç. The forward diffusion process then applied to Xū over the diffusion steps T.
Next, the target conditions are initialized as E = C". These target conditions are iteratively updated until the final conditions are obtained. each diffusion step E î. we compute the input X; using the equation X; = (XO, CO), ensuring that the editing directions are accurately incorporated into the denoising process.
The source text for X "face," and the target text is "face with 8," where represents the target attribute. We use / to denote the source text and Ta denote the target text. To quantify the differ-
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ence between the source and target conditions, utilize the CLIP loss function Cetip from Radford et al. (2021) to compute the loss. The loss function is formulated as:
L1
Calip I,, X;)
(10)
This loss helps guide the model toward generating video frames that align with the target attributes defined by 5.
Then keep the consistency the background information of the reconstructed frames under the larget conditions with the original video frames, another loss function is used:
C2
(x] (x)x;;)
(11)
t=0
and to control the updated conditions don't vary too much:
CrC
L3 =
(12)
||C+|||[c||
then the optimization object be obtained as:
L = wh Li + W2 + wzLa
(13)
where Wi, W2, are constants, And through minimizing C until convergence, we could get the trained conditions Cedit = -CO
Settings for Obtaining Conditions
In this paper, we use the pre-trained CLIP model, specifically the ViT-B/32 version. The weights Wi, W2, Wy are set to 5, 1, and 3. respectively, and the forward time step 1 is set to 5. The learning rate is set to 0.002, with batch size of 1 during training. The number of updating steps is fixed at 1000.
F.5 FINETUNE DIFFUSION AUTOENCODER
We finetune the pre-trained diffusion autoencoder from Kim et al. (003) on the HDTF dataset. The loss function used for finetuning consists of two components. The first component the standard DDIM (Denoising Diffusion Implicit Models) loss function, represented as:
Ladim E2 Ee, - -eel1
(14)
where ef can refer Equation 9 and E is the true noise, e T. This loss is minimized ensure accurate denoising and reconstruction during the finetuning process.
To enhance the robustness of the model to noise, we sample images given the time step with two different noise realizations, denoted as and E2, where 61, ~N(0, 1). The sampled images are represented X and X?.
The loss function for this sampling process can formulated as follows:
Lreg = W(o X
(15)
This loss encourages the model to accurately predict the noise for both sampled images, thereby improving its robustness against variations in noise during the denoising process.
The final optimization objective for finetuning the diffusion autoencoder = Ladii Cadim
Settings for Finetuning the Diffusion Autoencoder
We finetume the diffusion model on HDTF dataset. The learning rate is set to le-4 and the dropout is
set to 0.1, and we sample from each videos 16 frames during each training step. The batchsize is set to 16, the total training steps is set to 120000. And we set the seed to O, the diffusion step = 1000.
The experiment performed 4 RTX4090 GPUs.
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F.6 COMPUTATIONAL EFFICIENCY
o demonstrate the efficiency of our proposed method, we compare the inference time of editing one frame with the baseline methods. The results are shown Table 7 Since that the GANs based methods only need one forward pass to generate the video, we only compare the inference time of our method and STIT and DVA. can be seen that our method is more efficient than the baseline methods.
Table 7: The inference time of our method and the baseline methods.
STIT
DVA
FlowGuide
Infer Time
12.0 sec
62.4 sec
4.94 sec
F.7 ADDITIONAL RESULTS
Multiple Editing Direction We provide more manipulation results of a single video across multiple editing directions Figure[Iojnd Figure Our approach excels handling highly intricate back- ground details and dynamic scenes that include substantial head movements and that typically challenge existing state-of-the-art methods. Furthermore, our method adeptly retains the stylistic elements of the original video, ensuring that the edited output blends seamlessly with the untouched portions. This results in an exceptionally natural appearance, with virtually no visible traces of editing. The ability to maintain such coherence across different editing tasks underscores the robustness and adaptability of our approach.
scenarios
F.8
EDITING
We claim that the obtained condition Cedit can be used to edit the video of different subjects. To verify this claim, we further evaluate the cross-subject editing capabilities of our proposed method. As shown in Figure 18 we can edit the video of different subjects with the same condition Cedit-
F.9 MORE COMPARISON RESULTS
We provide more visualization results of our method and the baseline methods with editing direction "Mustache" in Figure 19 It can be seen that our method can handle the complex background and dynamic scene, and the edited output can blend seamlessly with the untouched portions.
F.10 NON PASTE-BACK VISUALIZATION
To evaluate the performance of our method and baselines more thoroughly, we provide the visual- ization results of our method and the baseline methods Figure 2T with editing direction "Smile" without paste-back the editing results to the original video. It can be seen that our method can handle complex background and dynamic scene, and the edited output can blend seamlessly with the untouched portions.
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Original
Young
Big lip
Eyeglasses
Beard
Smile
Makeup
Lipstick
Figure 16: Manipulation results of our Flow on single video with different editing directions: "Beard" and "Big Lip". "Eyeglasses", "smile", "Young", "makeup" and "wearing Lipstick".
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Young Original
mit
Mim
Ail
iif
dil
Hip
mm
Big
fift
fift
iftt
Fift
dis
Beef
Beard
Fi!
Aip
Fii.
BESF
Smile
fift
Ffit
Alb
Al
A&M
Makeup
ABB
ABF
AEST
Hair Color
AEUT
Lipstick
AEE
AED
SEUT
Figure 17: Manipulation results of our Flow Guide on single video with two different editing direc- tions: "Beard" and "Big Lip", "Hair Color", "smile", "Young", "makeup" and "wearing Lipstick"..
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Original
Subject
Subject2
Figure 18: The crose- editing results of our method.
Original.
STIT.
Latent titenttrrnss trans.
DVA.
FlowGuide.
Figure 19; Comparison of editing performance our FlowGuide the previous video editing
methods for editing direction 'Mustache'.
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Original.
PTL
StyleClip.
STIT.
Latent-tranm
DVA.
FloGGuide.
Figure 20: Comparison of editing performance of our FlowGuide to the previous video editing methods for editing direction 'Libstick'.
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Original.
Bil
lill
StyleCLIP.
STIT.
Bitt
iit!
PTL.
Latent-trans.
DVA.
FlowGuide.
Figure 21: Comparison of editing performance our FlowGuide to the previous video editing methods for editing direction 'Smile'.
31


Reviews:
[{"review_id": "0XtlJzjgIn", "reviewer": "Reviewer_AHnz", "summary": "This paper presents a unified framework called FlowGuide for face image and video editing based on a novel guidance mechanism to achieve precise attribute control in diffusion models. FlowGuide consists of two main key contributions: a Latent Basis Extraction (LBE) module and an Implicit Basis Guidance (IBG) mechanism. As compared to previous approaches, this algorithm can avoid introducing distortions to other facial features, identity, or background elements. Linear subspaces within the UNet bottleneck’s latent space for semantic attributes were obtained using SVD, and the LBE module identify orthogonal basis vectors in this subspace to isolate the identity from the attributes in the latent space. The Implicit Basis Guidance (IBG) mechanism computes the geometric alignment between these bases using cosine similarity to preserve the original identity. Two kinds of experiments were conducted: face image editing and face video editing. Those experiments showed the validity of the proposed algorithm well, supported by enhanced performance and some ablation studies.", "questions": "Face editing while preserving the identity would have many possible applications. I am curious how deepfake detectors would respond to images/videos with your approach and other previous approaches.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "This paper is well structured and well organized. It was quite easy to follow.\nThe proposed methods are well motivated and mathematically sound.\nTwo experiments show the validity of the proposed algorithm and its generality. The experiments are comprehensive using multiple datasets and the proposed algorithm is compared to recent methods.\nThe proposed method is more time-efficient than the previous SOTA.", "weaknesses": "I understand the page is quite limited, but I would suggest adding some discussions to make this paper more attractive.\nLimitations, failure cases, and discussions on the reasons are not well presented.\n\nI am afraid that the videos are rather simple. How long and how much can the proposed method keep valid for longer videos and for videos with larger movements and facial expressions/occlusions?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OZjSmi8FwP", "reviewer": "Reviewer_89iV", "summary": "This paper tackles challenge in face editing: achieving precise attribute manipulation while preserving identity and temporal consistency. The method, **FlowGuide**, proposed the ​**​geometrically-grounded guidance mechanism​**​, which is built upon a key observation: the latent space in the UNet bottleneck exhibits a locally linear structure. By modeling attributes as linear subspaces in the UNet's latent space and using the geometric alignment between original and edited subspaces to guide the denoising process, it achieves precise, localized edits while preserving identity and temporal coherence.", "questions": "1. How do the authors quantify the degree of local linearity? What happens when this assumption breaks down for large attribute changes?\n2. How many basis vectors are used? What is the impact of this choice of $n$ on editing quality?\n3. How crucial is the dataset-specific fine-tuning of the diffusion autoencoder to FlowGuide's video editing performance? Would the method work effectively on video in a true zero-shot setting with a generic pre-trained model?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.  Leveraging the geometric structure (linear subspaces) of the latent space for control is elegant for disentanglement, moving beyond heuristic guidance methods. \n2. The method can be applied to both image and video editing tasks.\n3. Comprehensive quantitative and qualitative results are provided.", "weaknesses": "1. This method rests on the assumption of ​**​local linearity​**​ in the UNet bottleneck's latent space  (Park et al., 2023; Kwon et al., 2022). However, the nature and bounds of this \"locality\" are not defined. How large is the linear region? Is it consistent across different inputs, and different denoising timesteps? The method's robustness depends on this assumption holding true universally.\n2. The claim of disentanglement is challenging to prove. The qualitative results, while good, still show that edits can affect non-target regions. For example, in Figure 3, when adding a smile, changes in skin texture and face attributes are visible, which are not strictly part of the \"smile\" attribute but are correlated with it. \n3. Equation 1 uses $θ$ subscript for $q_θ$, but $q$ is supposed to be the fixed forward process, not parameterized.\n\n\n\n**References**\n\n[1] *Mingi Kwon,et al. Diffusion models already have a semantic latent space. ICLR, 2023.*\n\n[2] *Yong-Hyun Park,et al. Understanding the latent space of diffusion models through the lens of riemannian geometry. NeurIPS 2023*", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c4vLTMHfnI", "reviewer": "Reviewer_17Mb", "summary": "This paper presents a method to edit face images and videos using the latent space of diffusion models. The authors find a basis in the latent space of the U-Net of the diffusion model to carry out editing with and then apply a binary mask to choose between a reconstruction noise direction $\\epsilon^r$ and an editing noise direction $\\epsilon^c$.", "questions": "The section 3.4 is a bit confusing, especially around lines 263-269. You say that the directions of $\\epsilon^c\" and $\\epsilon^r$ should remain consistent with similarity. To me, this means that, the more similar the bases are, the more similar the directions should be, and vice versa. But, if I understand correctly, what equation 6 does is the following:\n- if the difference between $\\epsilon^c$ and $\\epsilon^r$ is smaller than $\\lambda$, then the final direction is simply the reconstruction direction\n- if the difference between them is high, then you take the editing direction\nThis is different from just saying \"they should remain consistent with the similarity\", which is extremely vague and could mean many things. As it is, the choice makes sense, but I feel it is quite poorly explained. It is true at the end of the paragraph you say \"editing process only targets the desired attributes while maintaining ...\", but this should come earlier and be clearer. In particular, I would put a clear interpretation of equation 6. Also, \"dynamic threshold\" is not really clear I find, since in equation 3, you never mentioned a \"threshold\", indeed this is something you propose.\n\nFinally, there is a method which works extremely well at the moment for prompt-based image editing, which is not cited here:\n- FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space, 2025\nWhile I agree that this is recent (however, you have included other methods from 2025), it does represent the state-of-the-art, and should at least be mentioned. It produces extremely impressive results, and so cannot be ignored.\n\nAlso, I did not understand why you present this as a video editing problem: everything is done image by image, so that there is no video element here (no enforcement of temporal consistency etc). I think you should present it just as an image editing method.\n\nSmaller details\n\n- Page 3, line 124: you use $X_0=\\{x_1,\\dots, x_n\\}$ to represent initial input frames. I find that this is a bit confusing, since you then apply a neural network to this whole list. I understand that it represents a batch, and that everything can be done independently over the elements of the batch, but I would just say let $X_0$ represent a frame, and then that the frames will be processed independently. Furthermore, in section 3.3, you use $n$ to represent the number of vectors in the bases you find, and this cannot be the same $n$. So just drop the initial reference to all of the frames (unless somewhere you need to reference different frames, but I could not find such a point).\n- Page 3, line 133: $\\alpha_t$ not introduced. It is standard in diffusion models, but you still need to introduce it.\n- Page 3, line 133: what is $\\epsilon^r$, not introduced. We deduce that it is the output of the neural network, but still, not introduced. You specify that $\\epsilon^c$ is the predicted noise with the condition, but this comes after $\\epsilon^r$.\n- Page 4, line 198: \"Since the extraction of the latent basis is identical for both $X^r_T$ and $X^c_T$...\". This is a bit misleading, it implies that the same basis is found, which is not the case. Rephrase to say that the process of finding the two bases is identical.\n- Page 5, line 263: \"To ensure that the directions of $\\epsilon^c$ and $\\epsilon^r$ remain consistent with the similarity $\\varepsilon$. I find this a little confusion, it is not obvious how vectors can be consistent with a scalar (see above comment). \n- Page 5, equation 6 : you do not state what $\\hat{\\epsilon}$ is used for. We understand that it is the final editing direction (I guess), but this is not stated.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The results of the paper look good, and the method makes sense. The presentation is well-structured and the writing is good. The adherence to the original identity of the person is good, which is an important point.", "weaknesses": "One of my main criticisms is that the whole idea of section 3.3, finding the basis vectors using an SVD of the Jacobian, is directly the same as in the work of Park et al 2023. The main difference as far as I can tell, is that finding the bases is done in twoo different situations (reconstruction and editing), but apart from that, the technique is the same. I do not think this is necessarily something problematic, but the presentation of it is. While the authors indeed cite Park et al, they do not clarify that the technique is basically the same. So they should either say why it is different, or present this section more succinctly and not as a novel innovation.\n\nFurthermore, the bases found for editing are only used to establish the binary mask to choose between reconstruction and editing! This feels like a bit of a let down, because usually when we look for a basis, it is to use these basis vectors to carry out the editing (because they encode useful semantic directions), not just to choose between editing and reconstruction. So it seems like a lot of work for a quite small gain and difference with respect to the work of Huberman et al. The difference boils down to using a binary choice between reconstruction and editing, rather than a linear combination. I feel that this is quite hidden, and the basis vectors are presented as having a much greater and direct importance than they actually have. I wonder if the authors tried using the basis vectors to edit but maybe they did not work as well as hoped.\n\nConsequently, I found it difficult to isolate what the actual contribution is of the paper. As far as I can tell, there are two aspects:\n- separating the editing direction into reconstruction and editing, which is a good idea, but is inspired by Huberman et al 2024. (as the authors acknowledge)\n- Applying a binary make to choose either reconstruction or editing, and establishing this mask with a dynamic criterion, based on the basis vectors\nPresented like this, the novelty is much smaller.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "L9VSQDPcuK", "reviewer": "Reviewer_3wKp", "summary": "The paper introduces a diffusion-based framework designed for identity-preserving face attribute edits in both images and videos. \nBy leveraging the local linearity of the U-Net bottleneck, the authors identify semantic subspaces (for identity vs. target attributes) as linear directions. During denoising, a novel guidance mechanism dynamically steers the generation along the target attribute axis, while preserving orthogonal identity components.", "questions": "1. Did the authors evaluate or compare their method against other recent diffusion-based video editing techniques?\n2. A discussion of limitations (e.g., visual quality issues and attribute leakage, see 2. above) would strengthen the paper.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The paper addresses an interesting and relatively under-explored problem with diffusion models, which is local attribute editing of videos.\n2. The proposed solution is both novel and interesting.\n3. The paper is well-written and experiments are reasonably solid, showing fair quantitative metrics and adequate comparisons with prior work.", "weaknesses": "1. There are almost no citations and comparisons with prior diffusion-based video editing methods, even ones that support or are designed for face attribute editing, e.g. [1-4].\n\n2. The visual results are not entirely convincing, e.g. videos tend to be blurry, sunglasses (in both image and video examples) appear blended into the face rather than realistic, and sometimes the edits change additional attributes in addition to the intended ones.\n\n[1] RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models\n\n[2] V-LASIK: Consistent Glasses-Removal from Videos Using Synthetic Data \n\n[3] IP-FaceDiff: Identity-Preserving Facial Video Editing with Diffusion\n\n[4] Qffusion: Controllable Portrait Video Editing via Quadrant-Grid Attention Learning", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)