Let Your Light Shine: Foreground Portrait Matting via Deep Flash Priors
Authors: Tianyi Xiang
Yangyang Xu
Qingxuan Hu
Chenyi Zi
Nanxuan Zhao
Junle Wang
Shengfeng He
Venue: Accepted by TMLR ()

Abstract: In this paper, we delve into a new perspective to solve image matting by revealing the foreground with flash priors. Previous Background Matting frameworks require a clean background as input, and although demonstrated powerfully, they are not practical to handle real-world scenarios with dynamic camera or background movement. We introduce the flash/no-flash image pair to portray the foreground object while eliminating the influence of dynamic background. The rationale behind this is that the foreground object is closer to the camera and thus received more light than the background. We propose a cascaded end-to-end network to integrate flash prior knowledge into the alpha matte estimation process. Particularly, a transformer-based Foreground Correlation Module is presented to connect foregrounds exposed in different lightings, which can effectively filter out the perturbation from the dynamic background and also robust to foreground motion. The initial prediction is concatenated with a Boundary Matting Network to polish the details of previous predictions. To supplement the training and evaluation of our flash/no-flash framework, we construct the first flash/no-flash portrait image matting dataset with 3,025 well-annotated alpha mattes. Experimental evaluations show that our proposed model significantly outperforms existing trimap-free matting methods on scenes with dynamic backgrounds. Moreover, we detailedly discuss and analyze the effects of different prior knowledge on static and dynamic backgrounds. In contrast to the restricted scenarios of Background Matting, we demonstrate a flexible and reliable solution in real-world cases with the camera or background movements.

Published in Transactions on Machine Learning Research (12/2025)
Let Your Light Shine: Foreground Portrait Matting via Deep Flash Priors
Tianyi Xiang
Deportment of Computer Science, City University of Hong Kong School of Computing and Information Systems, Singupore Management University
Yangyang Xu²
swuyengyynn@@hiil.co edu.ccm
School of Intellgencce Science and Engineering, Harbin Institute of Technology (Shenzhen)
Qingxuan Hu
quhbben com
School of Computing, National Universily of Singapore School of Computing and Information Systems, Singapore Management University
Chenyi Zi
burristeenaa0666@@mmal..cmmm
Department of Data Science and Analytics, HKUST (Guangzhou) School of Computing and Information Systems, Singapore Management University
Nanxuan Zhao Adobe Research
nanzuanzhaa@@@mmsl..om
Junle Wang
wongiunlee@gaasl.ccm com
Tencent
Shengfeng Hel
shengfenghee@smu.cdu..k
School of Computing and Information Systems, Singapore Management University
Reviewed on OpenReview: https: // openreview. net/ forum? id- uwUs Vp2eM
Abstract
In this paper, we delve into new perspective to solve image matting by revealing the foreground with flash priors. Previous Background Matting frameworks require a clean background as input, and although demonstrated powerfully, they are not practical to han- dle real-world scenarios with dynamic camera or background movement. We introduce the Hash/no-flase image pair portray the foreground object while climinating the influence of dynamic background. The rationale behind this is that the foreground object is closer to the camera and thus received more light than the background. We propose cascaded end- to-end network to integrate flash prior knowledge into the alpha matte cstimation process. Particularly, a transformer-based Foreground Correlat ion Module is presented to connect foregrounds exposed different lightings, which can effectively filter out the perturbation from the dynamic background and also robust to foreground motion. The initial prediction concatenated with Boundary Matting Network to polish the details of previous predictions. To supplement the training and evaluation of our flash/no-flast nooffashh framework, construct the first flash/no-flast floshash portrait image matting dataset with 3,025 well-annotated alpha mattes. Experimental evaluations show that our proposed model siguificantly oulperforms existing trimap-fie free matting methods on scenes with dynamic backgrounds. Moreover, we detailedly discuss and analyze the effects of different prior knowledge on static and dynamic back- grounds. contrast to the restricted scenarios of Background Matting, we dernonstrate
*Corresponding authors.

---

Published in Transactions on Machine Learning Research (12/2025)
Input image
Input image 2
(a) Inputs of BGMv2
(b) Inputs of our model
Figure 1: We consider the portrait matting problem in the dynamic scene. BGMv2 (Lin et al. 2021 requires two images (a) inputs. However, their assumption of clean and static background image does not hold in dynamic scene, in which it produces a poor alpha matte. We propose new setting to use an additional flash image to shed light on the foreground object. Our setting is robust to misalignments like background objects and camera movements.
flexible and reliable solution in real-world cases with the camera or background movements. The code is available at http:://stttu...ooocxx/s///////t
1
Introduction
Image matting has been a core and fundamental problem in computer vision that extensively applied film production, social media, video conferencing, and many other fields. Nowadays, video conferencing has become the status quo in business and educational contexts, and image matting can be used to prevent privacy leakage by replacing virtual background. Mathematically, the image matting problem can be formulated as
I= af + +
(1)
where the foreground F, background B, and the pixel-wise opacity are unknowns. Given an input image I in RGB representation, estimating seven unknown factors are obviously an ill-posed problem.
To resolve this problem, traditional matting methods Ruzon & Tomasi 2000 Chuang et al. 2001 Wang &
Cohen 2007 Gastal Oliveira 2010 He al. 2011 Feng et al.] 2016) usually rely on a trimap as a prior limit the solution space. However, generating a high-quality trimap is time-consuming and impractical real-time applications. Recently, some trimap-free methods
proposed to replace the trimap with other prior knowledge. Such of methods adopt the clean background (Sengupta et al. 2020 Lin et al. 2021
semantic mask (Chen et al. 2018 Yu et al. 2021b Park et al. 2023 Jiang et al.] 2025). or temporal features
in videos (Sun et al., 2021b Lin et al. 2022 et al.
2023a
2024a b
Lin et al. 2023 as auxiliaries to
predict the alpha matte.
Among these mothods, Background Matting series (i.e., BGMv1 (Sengupta al. 2020 and BGMv2 [Lin et al. 2021)) achieves robust performances by taking clean background as an additional input (see Fig. la). This clean and static background provides very strong prior knowledge that objects that have not appeared
2

---

Published in Transactions on Machine Learning Research (12/2025)
in the background image must belong to the foreground. With great assumption comes great restriction, background matting cannot handle any types of movements that make the source image not aligned with the background image.
In this paper, we resolve this problem from an opposite perspective. Instead of providing background hints, we shed light on the foreground regions with flash photography. 'he rationale behind that foreground objects are typically closer to the camera and thus received more lights than the background. Taking additional flash imput
not only provide strong foreground clues, it also enables more flexible and practical setting: 1) users are not required to take the additional reference image in advance (e.g., background image in BGM), and the matting process can start together with filming; 2) main objects can move around, and we can take new flash image anytime during filming to adapt to the new scene.
To fully exploit flash priors, we devise the Flash and No-flash Net (FNFNet), which uses Bash/no-oaaa no-fflsh image pair produce high-resolution portrait alpha matte and the foreground of the no-flash image. The estimation is insensitive to moving foreground, background misalignment, and camera shaking. Our FNFNet consists of two cascaded stages. The first stage aims to explore the interaction between flash and no-flash images, and provides coarse estimations of trimap and initial alpha matte. Concretely, we propose transformer- Foreground Correlation Module (FCM) to jointly identify illmination-iirelvaal foreground appearances across two inputs in a reciprocal manner. Moreover, we calculate a flash ratio map between flash/ no-flash images as a coarse indicator foreground regions to supplement the alpha matte estimation. In the second stage, the trimap and initial alpha matte are fed to the Boundary Matting Network, which has a unique emphasis on producing fine boundary details of the mattes.
In order to train and evaluate method and promote rescarch on flash photography, we construct the first flash/no-llah no-slash portrait matting dataset. It consists of more than 100 diverse videos captured using the green screen, in total containing 3,025 well-annotated alpha mattes. We also collect dynamic videos of various real- world scenes to compose dynamic background image pairs. Therefore our benchmark can be used to evaluate matting methods with static and dynamic backgrounds. Several trimap-free matting methods are compared on this benchmark. Extensive experiments show that our proposed method achieves superior performances on the scenarios with dynamic backgrounds, and our flash prior demonstrates comparable performance the background prior in static scenes.
In summary, our contribution is fourfold:
We construct the first flash/no-fin nooslassh portrait matting dataset. It consists of more than 100 diverse videos with 3,025 well-annotated alpha mattes. We collect additional dynamic background videos, logether with static backgrounds, to form benchmark for evaluating the matting performance both scenarios.
Rather than relying on clean background, we propose deep flash prior, which enables a more flexible and practical solution for real-time or live-streaming matting.
We propose FNENet to integrate flash photography into a deep neural network. It explores the interaction between two differently illuminated foregrounds extract high-quality alpha matte.
Experiment results show that our model achieves state-of-the-art performance among other trimap- free matting methods. More importantly, our method is insensitive to the dynamic background and not restricted to well-aligned foreground or background between source and reference images.
2 Related work
2.1
Trimap-based Matting
Traditional methods often require a manually annotated trimap as additional information for solving the matting problem, which can be classified into sampling-based methods and propagation- mothods. Sampling-base based methods (Ruzon Tomasi
2000
Chuang et al. 2001 Wang & Cohen 2007 Gastal &
Oliveira 2010 He et al. 2011 Feng et. al.
2016 Johnson al. 2016 sample pixels from the labelled
3

---

Published in Transactions on Machine Learning Research (12/2025)
foreground and background regions to get the color information, and then estimate the alpha matte of the unknown region. Propagation-based methods (Sun et. al. 2004 Levin et al. 2007 2008 Chen et. al. 2013 Aksoy et al. 2017) build the similarity of neighboring pixels, trying to propagate alpha value from known to unknown regions. Since the above methods only utilize low-level features, they seldom achieve good performance in complex scenes. With the rapid development of deep learning, many methods (Yao el al.
2024 Cai ct al. 2022, Park et al. 2022 Lutz et al.) 2018: (angg et al.) 2019 Liu et al., 2021c) based on neural network have been explored and have made unprecedented progress in image matting, which have been comprehensively reviewed in Li et al. (023b); Lepcha et al. (2023). Xu et al. (Xu et al.) 2017) proposes two-stage neural network structure named Deep Image Matting (DIM) to obtain the coarse alpha and refined alpha matte, respectively. They also collects the first large-scale matting dataset, which empowers the development of many following works. Hou el al. (Hou & Liu 2019) employs two encoders to extract local features and the global context information respectively, and estimates foreground and alpha simultaneously, Lu et al. (Lu et al., 2219)) presents IndexNet, which can dynamically generates indices as conditions to guide the matting context modelling and operating. GCAMatting (Li & Lu (020) designs a Guided Contextual Attention module to propagate global high-level features based on the learned low-level affinity. Cai et al. (Cai et al. 2019) disentangles image matting into two sub-tasks: trimap adaptation and alpha estimation, with disentangled framework proposed named AdaMatting to solve them. Yu et al. (Yu et al., 2021a) extend the matting problem to high-resolution images relying on a patch-based method with several modules to capture long-range Long-range matting contextual and handle information propagation. Zhang et al. (Zhang et al., 2021 suggest propagating the trimap via temporal dimension to achieve video object matting using a temporal aggregation module. In [Snn et al. (2021a) and Liu et al. (202Ia), semantic classification of matting regions is incorporated into the matting framework. Recently, some methods leverage diffusion algorithm (Ho et al., 2020 to facilitate image matting, including diffusing in pixel space by diffusing disturbed trimap (Xul 2023) or pure noise (Hu et al. 2024) until a clean alpha matte is produced, or diffusing in latent space Li et al. 2024d). However, as trimap- based methods rely on well-annotated trimap as the input for generating plausible results, they often fail to be applied in real-time scenarios.
et al.
2.2
Trimap-free Matting
Many recent works (Zhang et al. 2019 Dai et al.] 2022; Zhu et al., 2017 Shen et al. 2016 Chen et al., 2018
Liu et al., 2020; Yu et al. b021b Qiao al. 2020; Park et al.) 2023 Jiang et al. 2025) try to rclax trimap input by adding a segmentation process before acquiring the final alpha matte, while matting performance overly depends on the results of segmentation. HAtMatting (Qiao et al. 2020) introduces a blended attention mechanism to integrate features. Self-guidance provided by Progressive Refinement Network (PRN) module in Yu et al.] (2021b) can doal with versatile guidance masks. LFPNet (Liu et al., 2021b) learns long-range conlext features outside the reception field and propagates learned surrounding features to help matting prediction inside the reception field. Sun et al. (Sun et al. 2022) introduce flaw detection as sub-task to achieve automatic error-correcting in trimap-free portrait matting problems. Lin
et al.
y0 Li et al. (2023a); Sun et al. (2023); Li et al. (2024a); Gu et al. (2023); Li ct al. (2024b) Lin
al.... et
2023) adopt temporal information in the video as guidance to do the video objects or portrail matting.
However, the temporal priors relied on by these video matting methods may face challenges in various
dynamic backgrounds. Recently, some trimap-free matting methods (Ke et al.) 2022 Chen et al.] 2022b Li
et al. 2022: Ma et al. 2023) follow the idea of decomposing the matting problem into semantic prediction and
detail prediction and then considering fusion or collaboration between these two sub-objectives. However,
these methods usually perform worse than trimap-bases based methods in matting quality. Some methods take advantage of the text-image correspondence prior (c.g., CLIP (Radford et al., 2021 to achieve referring 2025 Li et al. 2023c). Some methods also try to directly solve the alpha via image matting (Xiang et al.
in-context prior [Guo et al. 2024). latent diffusion prior (Wang et al. 2024), or vision foundation model (Yo
ett al. 2024 Li el al. 2024c). Sengupta el al. (Sengupla el. al., (020) proposes Background Matting (BGM) which only requires a pre-captured background image without foreground objects as auxiliary cues to help determine the position of the foreground. This method achieves high-quality results. Based on [Sengupta et al. (2020), Lin et al. (Lin et al., (221)) further uses a two-stage network to realize real-time high-resolution matting. However, background matting cannot. handle dynamic backgrounds and large camera movements.
4

---

Published in Transactions on Machine Learning Research (12/2025)
2.3 Flash Photography Applications
The advantages of exploiting flash photography have been explored in many computer vision lasks. For adopting flash and no-flash image pairs, specially, Sun et al. (Sun et al., 2006) tries to recover the matte basod on the simple observation that the most noticcable difference between the flash and no-flash images is the foreground object if the background scene is far enough away. Additional information given by Mash/no-flasie image pairs is also explored in He & Lau (2014) for saliency detection. However, they strictly require perfect alignment for the foregrounds in flash and no-flash images, making it unsuitable for scenarios involving dynamic foregrounds. Sun et al. (Sun et al. 2007 suggests adopting Markov Random Field to combine flash, motion, and color cues and model the flash effect using the histograms of the flash/no no- flash images. However, it strongly relies on several strict low-level feature assumptions like the background consistency (i.e., near-bijective relationship for the background region) and obvious contrast due flash in foreground regions in flash/ foohh/o-ffie no-flash image pairs, making it fail when the foreground undergoes significant pose variations, when the background exhibits dynamic changes (e.g., new objects appearing), or the flash contrast for the foreground is not obvious.
In our work, we explore the previously unaddressed connection between flash cues and high-level instance- level representations. By modeling instance-level matching representations, our method effectively tackles the challenges dynamic flash matting, including variations in foreground pose and position, as well as interference from dynamic backgrounds.
Hash Flash No-lasi
No-flesh
(a) Examples of selected flash/no-fil /no-flssh portrait frames and cor- responding GT alpha matte in dataset.
(b) Examples of dynamic backgrounds in dataset.
Figure 2: Examples from our dataset: (a) flash/ Bashhno-ffash no-flash portrait frames with ground truth alpha malles, and (b) dynamic backgrounds.
3 FNF Matting Dataset
To the best our knowledge, there does not exist an available large-scale dataset containing flash and no- flash image pairs that can be used for portrait matting. Therefore, we construct the first flash and no-flash portrait matting dataset by combing foreground portraits (Fig. 2ah and dynamic background scenes (Fig. 2b together.
5

---

Published in Transactions on Machine Learning Research (12/2025)
Flss frame No-Ilahh tram
Count
10
(a) The number of flash/no-flast /no-flash frames per person, sorted in decreasing order of the total number of frames selected for each person.
108081923
2010x1262
82.2%
264Dx2262
5%
1.57
2. 2.0%
9.8%
(b) The distributions of resolution (left, weitht)width) and frame rate (right, fps) of dynamic backgrounds.
16
Count
29
27 27 26
City
forest
building intartor
school
airport intetor
kotch
bathoom
emply city
office
Lab
cum intorior
stred um merror
lecture
house dboor
Hondor
clzzrcom
thester interior
train station
night club
wortspace
woods
Scone
(c) The number of background videos under different
Figure 3: The statistics of the proposed FNF Matting datasct. We show that the dataset has a rational distribution of lash/no-flash foregrounds for each green-screen portrait video. We also illustrate some basic properties (resolution and frame rate) collected dynamic background videos and distribution various scenes.
3.1 Data Collection and Processing
Foreground Portrait. To collect the foreground portrait videos, we take the green screen as the background and use the latest smartphones for shooting. We invited 133 participants (66 males and females) for taking the videos. Each video is in resolution of 1080P and lasts around 20 seconds. Participants

---

Published in Transactions on Machine Learning Research (12/2025)
Table 1: Statistics of our proposed dataset.
Training Set
Test Set
Total
Protrait Videos
111
22
133
Flash Frames
1,102
220
1,322
No-flash Frames
1,408
295
1,703
Dynamie
625
89
714
Background Videos
Dynamic
11,806
1,712
13,518
Background Frames
were asked to act on some common actions that happened during the online meeting for simulating the real scenarios, such turning around, (alking, and playing with their hair. hand-held lamp is used for generating the flash and no-flash effects. We ensure the lighting conditions and camera settings are consistent across all videos, to minimize individual bias and improve annotation convenience. For each video, use the Super Key function in Adobe Premiere to extract the foregrounds and alpha mattes automatically. In practical, process a single video only takes less than 10 minutes, and the entire annotation process for the dataset was completed several days with skilled workers. As shown Fig. 2a our dataset covers diverse foreground portraits under common matting cases, such as long and spread hair (e.g., 4th row of Fig. 2ap and transparent area (e.g., eyeglasses in 2nd row of Fig. 2a). The portraits are also in different clothes and poses to enhance the robustness of the model. In total, obtaín 1,322 flash and 1,703 no-flash portraits with their well-annotated alpha mattes.
Dynamic Background. For background, we collect 714 videos under diverse real-world scenes from the Internet. We sample frames from each video with an interval of 15, and we only select 20 frames at most from each video. Several examples have been shown Fig. 2b
We split the dataset for training and testing in the video-level and show the statistics in Tab.
3.2 Dataset Statistics Analysis
To gain more knowledge from our dataset, conduct some statistic analysis in this subsection and show the results in Fig.
3.2.1 Flash/no-flan Frames
The number of selected flash/noffashh frames of each person (i.e., video) is shown in Fig. 3a that majority of video has no less than 20 flash and no-flash frames in total. Some video has more than 20 the edge area of the portrait
We can see
selected frames, indicating they contain more various poses or complexity (e.g., hair, eyeglasses, etc.) relatively. Videos with 20 selected frames are common portrait variety and normal quality, while vidcos with poor recording quality have less than 20 selected frames.
3.2.2 Resolution and Frame Rate of Dynamic Backgrounds
The left chart of Fig. 3b shows the resolution distribution of dynamic background videos. It can be seen that more than 80 percent of the videos have resolution of 1080 x 1920, and almost all videos high resolution. And the vast majority of videos are horizontal, and few videos vertical. The right chart of Fig. 3b shows the frame rate (fps) distribution of our datasct. In this chart, we approximate some vidcos with standard frame rates lo the nearest integer frame rates when counting statistics (e-g., video in 29.97 fps is counted as 30 fps). Nearly half of the videos have a frame rate of 30 fps, and most of the videos are recorded at common frame speed. About 10 percent of the vidcos have a higher frame rate (e.g., 50 fps, 60 fps, etc.), which means that the difference between two adjacent frames of these videos may be smaller in general.

---

Published in Transactions on Machine Learning Research (12/2025)
Low-resolution
Outputs
Coarse Matting Network (CMN)
Flash Guided Trimap Prediction
Multi-head
Solf-attention
Cars Sigerriie
Shorie
Multi-head
Skip Connection
Cross-attention
IBAM
Attention Module
Convolational Block
Foreground
Spatial
Correlation
Pyramid Fooling
Shared
Decoder
Rneeder
High-resolution Outputs
Final prediction
Ellement-arion miltippkotioon
Cacc Cab
Down/ upp sumple
Goncatamate
Opsoomple then concatenate
Comv-iN--ilea
1"
Boundary Matting Network (BMN)
Figure Overview of our FNFNet structure. Given pair of flash/no /no-/lah images [1',1" as input, we first down-sample them with a scale factor d'(c' s 0.1) to (L,,, 1nth J, and calculate the smoothed flash ratio map (r/)) of the flash image according the interior transfer of pixels affected by flash in the image histograms. Then randomly select q points from the truncated ratio map flash cues, and adopt pretrained segmentation model to extract the trimap tf of the flash image If with down-sample scale e(c < 0.5). The Coarse Malting Network (CMN) takes {16 In [1]} as inputs and predicts low-resolution matting results of the no-flash image: trimap tnf, detail map di foreground color F=f with context hidden feature F The Boundary Matting Network (BMN) is further introduced to enhance the fine dotails and up-sample del and Fag to the original resolution (d" (d"f,F") ,F/.).
3.2.3 Scenes of Background Videos
Our dataset consists of background videos across 29 categories, and the specific statistics are shown in Fig. 3c As can be seen, the category "city" is the most common one in our dataset, as most of our background videos come from urban street views. There are also many videos shot in places such as building interiors and natural landscapes. Almost all of them record dynamic changing due to camera shaking or movement, pedest rian rian or vehicle movement, water flowing, or other dynamic changes. Examples of such changes can also found in Fig. 2b
3.3 Mitigating Synthetic Data Discrepancy
In light of the disparities between composite data used for training and natural data, such differences in resolution, noise distribution, and composite artifacts between the foreground and background, have adopted techniques from Li et al. (2022) to address these discrepancies. To tackle resolution differences, we have meticulously selected background vidcos with high resolutions that align with those of the foreground videos, as elaborated in Sec. 3.2.2 In addressing differences in noise distribution, we initiate the process by applying BM3D (Dabov et al. 2009) for denoising both the foreground and background. Subsequently, we introduce Gaussian noise with the same deviation to further refine the composition. To further mitigate composite artifacts, we incorporate the re-JPEG (Hou & Liu 2019) operation into our data augmentation strategy for training samples.
8

---

Published in Transactions on Machine Learning Research (12/2025)
4 Method
Considering the person in front of the camera cannot remain completely still and the background may also undergo some dynamic changes in real-world video conference scenes, we need to take full advantage of the assumption that flash has a significant enhancement of foreground color intensity. This leads us to establish a non-local relationship between foreground person flash and no-flash images, providing foreground location cues to guide the matting of no-flash image.
To this end, we propose the Foreground Correlation Module (FCM), dual-branch and Transformer- based component to integrate implicit foreground information extracted from flash image to no-flash image features. Moreover, inspired by Sun et al. 2007); | He & Lau (2014), we introduce the flash ratio map as another flash cue, imprecisely labelling pixels in both flash and no-flash images with significant intensity changes as foreground based on the image histogram. We furthermore embed FCM to the Flash and No-flash matting network (FNFNet) to obtain robust and high-quality alpha matte of the no-flash image in the dynamic background scene.
4.1 Model Overview
Fig. demonstrates the pipeline of our proposed FNFNet. FNFNet includes Coarse Matting Network (CMN) and a Boundary Matting Network (BMN). The CMN has two inputs, one is the down-sampled no-flash image I21, the other is the concatenation of flash image If with its trimap tf (which is obtained by flash cues guided trimap prediction), two imputs are extracted to their high-level features respectively, Then these two features are fed into the Foreground Correlation Module (FCM) for feature aggregation, following three separate docoders to predict the trimap E2 detail map def, and foreground prodiction Fuy of the no-flash image. In BMN, we take the original no-flash image and reuse the first three layers of the encoder in CMN to extract low-level textual features, enabling BMN recover details and up-sample the low-resolution outputs from CMN to produce the high-resolution detail map d'd and forcground prediction paf of the no-flash image. Then the alpha malte a of the no-flash image can be obtained by compositing the up-sampled trimap (nf and detail map d
4.2
Flash Guided Flash- Trimap Prediction
The additional flash provides strong cues revealing foreground objects. Before getting into the network, we first derive an extra input of our framework, the trimap of flash image predicted using flash ratio map.
4.2.1 Flash ratio map
The flash ratio map is obtained by analyzing the differences between flash and no-flash histograms. The reason for utilizing the image histogram is that it gives the distribution of the color intensity of each pixel from the overall statistical level, making the produced flash ratio map insensitive to small background changes and foreground movements.
We denote the RGB color histogram of the flash images as H (h/)), and no-flash image histogram as Haf [h }. where hi indicates the corresponding number of pixel in the k-th bin. Here, both H and Hnf are computed in the entire RGB space, which ensures that the same index value (i.., "global" index) refers to the same color bin in both histograms, ensuring consistency across both images. We observe that the flash will modily and transfer some pixels the low-intensity bins of the no-flash image histogram to the high-intensity bins of the flash image histogram (illustrated in Fig. we have the following analysis with bi and k.f.
For hig > ny/, we can infer that some pixels in low-intensity bins of no-flash histogram have been transferred to the k-th bin of flash image histogram due to the flash effect. Thus, pixels in the k-th bin of the flash image histogram are more likely to be the foreground. While bf < hnd indicates that some pixels in the k-th bin of the no-flash image histogram have been transferred to high-intensity bins of the flash image histogram. Similarly, pixels in the k-th bin of the flash image histogram have higher probability of being the foreground.
9

---

Published in Transactions on Machine Learning Research (12/2025)
Histogram of no-flash image HT (Rg
Histogram of flash image
H {
h >
h
Before
Transferring
After
7
hj
nz
<
h
Intensity
Intensity
Initial pixel
Pixels from other bins
Pixels in other bins transferred the current bin
due to flash
Pixels
transferred
Before
Transferring
After
due to flash
Figure 5: Demonstration of our flash ratio map. Foreground pixels normally transferred from low intensity to high intensity due to the flash. Takes the i-th bin in the histograms of no-flash and flash image (AP) and bf respectively) as an example. If h2 > h/ it means that the number of pixels transferred to other bins is more than the newly added pixels. Therefore, pixels the i-th bin of the no-flash image are more likely to be foreground. Similar conclusion can be found the j-th bin of the flash image if h2 hr.
Original flash/in fash/no-flash pair with down-sample scale e'.
(b) Flash ratio maps calculated using Eqq. directly.
(c) Trruccated ratio maps with 8 0.7.
(d) Smoothed ratio maps using medium filter with
Figure 6: Examples of flash ratio maps. For pairs of down-sampled Hash/no-flest images in (a), (b) shows the flash ratio maps computed by the differences between histogram distributions. We furthermore refine (b) by increasing truncate threshold (c) and applying medium filtering to get a smoother result (d).
Based on the above analysis, given pair of flash/ ino-flash image, their corresponding flash ratio maps ri. and rnd at pixel can be quantified as:
lipp lh,
hk
1
r
(2)
where kip is the bin index which contains p-
10

---

Published in Transactions on Machine Learning Research (12/2025)
4.2.2 Flash- Flash-Trimap Trimap Processing
In order to get a flash ratio map that can provide a more accurate flash hint for further trimap prediction, we extract the confident points as flash cues by following steps. We first calculate the flash ratio map rf of the down-sampled flash image 1f. with the scale factor 2 using Eq. ( Particularly, e' is usually set to be less than 0.1 to eliminate interference from irrelevant pixels as much possible. Here do not calculate r" because ra is often inaccurate while of has a more accurate estimate of the foreground in the flash image. In order to retain more reliable regions in rf, we first perform truncation on rf using following equation:
(rl, r {0)
2 otherwise
(3)
where 6 is threshold to control the truncation level considering the flash ratio map may not be completely correct. Note that Eq. (3) can also help to climinate the undefined cases when h or Rkp since r will become -oo but can be truncated to directly given positive 8. rf is further smoothed by applying the medium filter with kernel size r for further refinement.
anf is 0 in Eq. 2
We showcase intermediate results before and after smoothing in Fig. To ensure that selected points are distributed evenly and do not accumulate in local areas, we randomly choose q points from rf as flash cues and then map these points to the resolution of I!. We employ pretrained segmentation model to extract the segmentation mask of the foreground, using !! and the flash cues as input. Subsequently, we derive the trimap !! for T from the segmentation mask using an erosion-dilation operation. During training, to enhance the training process for the subsequent end-to-end network, we directly apply the erosion-dilation operation to the ground truth alpha of of I to obtain t..
4.3 Coarse Matting Network
Inspired by DeeplabV3 (Chen et al. 2017 our CMN has the encoder-decoder archilecture, which includes two separate encoders with ResNet-50 (He et al., 2016) structures, the Atrous Spatial Pyramid Pooling (ASPP) modules, a Foroground Correlation Module (FCM), and three decoder networks for different types of output.
4.3.1 Backbone Network
Given a pair of flash and no-flash images 1n E Hx we first down-sample them with scale factor to If Inf R x W. x3 and obtain one-hot trimap t! 10, x x3 of Il. As discussed in Sec. 4.1 ins, , and the concatenation between 1! and 1g are fed into the two non-shared encoders following the ASPP module separately to extract their respective feature maps, denoted as x xf e /x respectively. Here, the ASPP module consists of three dilated convolution layers with dilation rates of 3, 6, and 9 to sunnarize the contextual information.
4.3.2 Foreground Correlation Module
As depicted in Fig. 7 we feed xf and =d into the FCM. Our objective is to establish an association between the flash image and the no-flash information using cross-attention mechanism. The leftmost branch, f comprises Lnf identical cross- attentionn layers, which are designed to facilitate the connection and fusion of the no-flash information with the foreground in the flash image. On the other hand, the rightmost branch, Tí, consists of Ly identical self-altention layers, focusing solely on extracting features from the flash image. Both of these branches take input sequences with a length of x w. To create these sequences, we apply a lincar projection followed by a flatten operation to the input features x', ,', xd' resulting in corresponding input sequences denoted as Z z h where C' represents the dimension of the embedding space. Additionally, we incorporate learnable positional embeddings into these sequences.
11

---

Published in Transactions on Machine Learning Research (12/2025)
Input features
X
Input embeddings
Zu
Output feature
X
X
Z
Convolutional Block Attention Module (CBAM)
Channel Attention
Spatial Attention Module
Module
Reshape
TT
Add & Normalize
Add & Normalize
FFN
FFN
XL
Add & Normalize
xL
Add & Normalize
MHCA
MHSA
Sinear-Flatted + Position Embedding
Figure 7: Overview of the Foreground Correlation Module (FCM). FCM consists of two branches: T and T each of which contains Ly and Laf transformer layers respectively. Each layer in T only have Multi-head Self-Attention (MHSA), while in Twd, each layer have the Multi-hoad Cross-Attention (MHCA). The Convolutional Block Attention Module (CBAM) (Woo et al. 2018 is applied for the reshaped feature outputted by 7 for Teature re-attention.
Computation of Tf; The embedded soquences Z is fed into T At each layer in TJ, we first calculate the triplet (Q/,, K[_, viL,) of Z[=; the input of current layer. Then the self-attention SAL_, is given as:
SAL_1 soft of (KL(((())))) ((LL)
(4)
Vd
We further calculate the multi-head self-attention MSA[] with the residual connection by concatenating m independent self-attention operation following a linear projection. Then, a feed-forward net (FFN) with the residual connection is used to transform MSA and obtain the output 2/ E IglwxC' of current layer:
= =F=FFN[ + MSAL
We denote zf, as the output of the last layer of T Recall that Ly represents the layer number of Ty-
Computation of Tmf; At each layer pf in Twf we utilize the multi-head cross-attention to fuse flash image features extracted from T with the input 2 For multi-head cross-atiention, denoted as MCAP we
12

---

Published in Transactions on Machine Learning Research (12/2025)
first calculate the query(Q(-1) from MS MSA. and the key (K and value from z Then MCA can be similarly carried out based on Eq. 4 An FFN with the residual connection applied for feature refinement to produce the output sequence Z of current layer:
2 = FFCEN + MCA
(6)
We denote Thn as the output of the last layer of TWS. Finally, They E RuuxO is reshaped shape of RxxxxC and passed to the Convolutional Block Attention Module (CBAM) (Woo et al. 2018 for feature re-attention both in spatial and channel-wise and obtain Tr/ for further decoding.
4.3.3 Decoders
Considering the distinct appearance of semantic and detail features, we adopt three non-shared decoders (Dirimap; Dettait; D foreground) to prodict the low-rosolution trimap, detail of alpha prediction, and foreground color. Each of these decoders includes different number of Conv-BN-ReLU groups. In each group, the input feature is bilinear-ussample with a scale factor 2, and then concatenated with the shorteut feature following 3 x 3 convolution, batch normalization (Toffe & Szegedy 2015), and ReLU activation (Nair & Hinton 2010).
The trimap decoder Dtrimap only contains 2 groups of Conv-BN-ReLU considering Dirimap only responsible for coarse trimap estimation. It takes 2nd as imput and outputs the classification probabilities with 3 classes (foreground, background, and unknown region) of all pixel of I at 1 resolution of I2 denoted [0, 1] de W. x3, Then we directly up-sample si to original resolution of Ins, denoted as sn e [0, 1] x W. x3 By taking the Argmax operation, snf can be easily converted to the trimap prediction, denoted as vnd E 1,,
n/
as $4e
The detail decoder Deetoi is responsible for predicting fine alpha matte in the unknown region. For pre- serving rich low-level contextual features, Ddetast consists of 3 groups of ConL-BN-ReLU, and takes shortcut feature of no-flash image encoder 1/8 resolution of I input. It also receives the Argmax operation applied s/d from Dtrimap and concatenate it with midden features before going into the second group, fus- ing semantic information in to better estimate detail alpha. Daetai finally outputs the detail alpha matte ded E [0 x Wx x1, which is only mcaningful in the unknown region.
The foreground decoder D foreground consists of 4 groups of Con--BN-ReLU and also takes Iz as input. Following Lin et al. (2021). Dj foreground predicts the foreground residual R and we then add the original image with clamping into [0.1] to obtain the foreground color prediction the no-flash image, denoted as FnJ E R
# x x3 Dj foreground also produces the hidden features Hat E IR x which contains the contextual information of foreground. In addition, both Dettai and Dforeground have skip connections with the no-flash image encoder (sce in Fig.
4.4 Boundary Matting Network
To obtain the matting result of the no-flash image with the original resolution, we design the boundary matting network to improve the matting result boundaries to high-resolution by incorporating low-level features. For the trimap faf, , we directly take a bi-linear interpolation operation to up-sample it to the original resolution, denoted as taf. And for the detail map d and foreground residual R we treat them differently for enhancement. We first adopt a bi-linear interpolation to up-sample the detail map feature (or foreground feature) to 1 of the original resolution. Then, we reuse the first three layers from the backbone network in CMN, including a 7 x 7 convolutional layers, batch-normalization layer, and a ReLU operation, to extract the low-level feature. We also apply several shallow convolutional layers with a small number of channels to process detailed low-level information. In detail, the low-level feature is concatenated with the detail map feature and passed into two groups of 3x3Conv-BN-ReLU to help restore the malting details. Furthermore, the output feature that has been interpolated to the original resolution, is concatenated with the original no-flash image and passed to another two groups of 3x3Conv-BN-ReIA (with the last ReLU removed) obtain the final enhanced and high-resolution detail map (or foreground residual), denoted as amd (or R.
13

---

Published in Transactions on Machine Learning Research (12/2025)
4.5
Loss Function
4.5.1
Trimap Loss
To learn the trimap of the foreground, we adopt the cross entropy loss the semantic classification proba- bilities s:
Seem (s)
Σ Gor log s
(7)
where s E [0, 1] is the classification probability of c-th class in s and &Gr E {0, 1] is the corresponding label. GT generated by the dilation and erosion operation on the ground-truth alpha.
4.5.2 Alpha Loss
Before calculating the alpha loss, we convert the trimap prediction t to the corresponding binary class masks of the foreground, background, and unknown region, denoted as {eTo, ,0, tnm]. Then, the two alpha losses, calculated on both unknown region (Ldetail) and the whole image (Laipha), are given by the following equations.
(d)) 1nm [lld aldd-acrlhi + [Vd Vacrlk Σ |Lpp
(d) (d) Lap (acr))l))
(8)
5 Σ [Tap Lap² (aGT)ll1.
Lalphala) = ||a - acrlli + I/Va Vocrlli +
(9)
where d is the predicted detail map, acr is the groudd-trut alpha, and is the alpha prediction given by d tun + tfo In addition to directly use the L1 loss, we also use the Sobel gradient operator (V) and Laplacian pyramid (L)p"(-)) loss (Hou & Liu 2019) to get better results.
4.5.3 Foreground Loss and Re-construction Loss
To learn the foreground color prediction, we adopt L1 loss the foreground prediction F where ocr 0:
(F) = [1(orT > 0) @ (F Fer)
(10)
where @ is the Hadamard product.
We also use alpha prediction to re-construct the image using Eq. the synthetic data:
1 and take the L1 loss when training with
Crecons ||a Fer (1 a) B --]],,
(11)
where B is the background for compositing to the training data, and I is the original image.
4.5.4 Overall Loss
We first train the Coarse Matting Network using following loss:
LeMN = Lsem (5 + Letetas(dyy prf(un)) + (app + (FF(FF + (a
(12)
where e is the down-sampling scale in CMN.
Once the training of CMN converges, we attach BMN to the CMN and train the entire model jointly using:
LBMN LCMN Lastas(dd (n((un)) + Calpha (Fg(F
(13)
14

---

Published in Transactions on Machine Learning Research (12/2025)
Table 2: Quantitative results on the synthetic data on our dataset (testing set). Best results under dynamic
background are marked in blue, while those in green are the best under static backgrounds. A 'f' indicates the model is retrained using our mixed training dataset. (BG-Background, PG-Foreground)
Evaluation Metrics
BG Type
Input Image Type
Method
MSE,
SAD4
Grad
Conn.
MODNetf [Ke al.
2022
1.751
4744.656
4188,432
3491.866
SHM CChen al.
2018
3,289
5931.676
5462.052
5287.022
LPF Zhang et al. 2019
3.504
508
5911.190
5445.151
HATTT f QQia et al. 20)20]
3.651
6892.134 134
6529.803
5792.352
RVMF [Lán al. 2022
1.981
4270.903
7097. 109
4133.62
Single FG image
SGHMf (Chen et al. 2022b
1.830
3859.964
4160.247
3837. 194
PPM| (Chen et a..
2022a
2.891
5726.036
306
4907. 765
GFM
LL et al.
2022
2.265
5415.296
4674.963
4321.387
MAM
Li
al.
2024c
2.374
4480.037
3959.617
2202.677
Smart Mat
Ye et al.
22124
1.034
2450.608
4019.710
1753.940
P3M-Neti
Ma et al.
2023
1.733
3034.637
5130.156
2828.504
FG
BG
BGMv2
Lin al.
2021
0.396
1112,788
1851.081
992.213
Static
Flash
No-flash
Ours
0.634
1767.632
3697.195
1589. 147
FG
BG
BGMv2 (Lin al.
2021
115.7
116675.8
32762.20 20
116327.7
FG
BG
1.212
2744.400
5314,358
2615.139
Dynamic
FG
Other FG
BGMv2 (in et al.
2021
2.547
4415.687
7394.905
4352.937
Flash
No-tlash
1.989
3768. 433
6802.648
3701.530
Flash
No-flash
Ours
0.652
1804.741
3742.230
1626.339
5 Experiment evaluation
5.1 Data Preparation
5.1.1 Data Synthesis
Foreground Acquirement. In addition to training with our own constructed dataset, we also adopt foreground samples in VidcoMatte240K (Lin et al.) 2021) dataset for training. For a foreground video in this dataset, we select 10 frames with a large and uniform time interval and adjust their channel in the YUV space to enhance the brightness. It forms flash mash/noo /no-fl image pairs with the remaining frames of the video. For implementation, we use the adjust_ee _aaama function in skimage exposure to adjust the V channel, where the gamma value is randomly selected in [0.2, 0.5). Although the pseudo flash image generated in this way is far from the real flash image, our model has no selectivity for the enhancement of foreground object color intensity, these images can effectively prevent the model from over-fitting with a small amount of data. Our model is trained on the mixed datasel of our real flash/ flossh image samples and pseudo flash/ no-sla image samples from VideoMMate240KK
Composite with Backgrounds. Following Eq. (1, we can build flash/no-flash image pairs by compositing flash/ no-slash foregrounds and their corresponding alpha matte onto dynamic background pairs under the same scene. During training, we first pair flash/no-fle nooffashh foregrounds of the same person one by one. Then we select two background images of the same scene with a random interval from all the background images as a dynamic background pair. To enhance the robustness of our model to static backgrounds, we replace a certain dynamic background pair (B,, B₁) with a static background pair (B,, B₁) with a probability of 0.4, where (B;, BB) represents two different background images but from the same scene and (B,, B:) represents two identical background images. The background pair is composited with the previous foreground pair to obtain no-shh image pairs for training.
Test Set Building. To build the test set, we composite pairs of Bash/no-flse foreground with pairs of pre-randomly scrambled dynamic background images alternatively. A flash/no-flas foreground pair is composited with five different dynamic background pairs to form the test set.
Adapt to Real-world Domain. The model trained only on the synthetic data often suffers from the domain shift problem, L.e., the model has a poor performance on real-world data. This is mainly due to
15

---

Published in Transactions on Machine Learning Research (12/2025)
Pseudo flash image
No-flahh image
alpha
Figure & Example of generated pseudo real-world flash nooh/no-iie no
flash no-flash samples from PM-10K (Li et al. 2022
No-flash images (Source image)
Flash image
Given background
BGMv2
MODNet
MAM
RVM
SGHM
P3M-Net
SmartMat
Ours
GT
Figure 9: Qualitative results on the synthetic data with static backgrounds.
the compositional artifacts and invariable texture of these samples, leading to over-fitting of the model. To eliminate such domain gap, in addition to introducing some data augmentation methods, we directly use PM-10K, real-world portrait matting dataset proposed by Ti et al. (2022) to transfer our model to the real- world domain. We use the same manner discussed above to generate the corresponding pseudo flash image for every real-world training sample in PM-10K dataset. Examples of generated training samples are shown in Fig. [8] However, we only brighten the foreground part of each sample by taking the weighted average of brightened image and the original image. The weight mask is obtained by applying erosion operation and averaging filter of the GT alpha matte.
5.1.2 Data Augmentation for Training
Morcover, we do the following augmentation operations for each pair of composited flash/no-flas /no-flal image pairs. The operations include affine transformations, horizontal flipping, brightness, hue, and saturation ad- justments on both foreground and background. We also apply blur, sharpening on the foreground randomly. The foreground resized randomly and also placed to a random position to accommodate the perturbations caused by the movement of characters in the real world. After generating the training samples, each batch of images is cropped to random height. and width between 960 and 1600 adapt the model to inputs of different resolutions.
5.2 Implementation Details
We use DeeplabV3 (Chen et al. 2017 weights pre-trained on Pascal VOC 2012 (Everingham et al. 2010 for semantic segmentation to initialize the backbone network and ASPP module, and adopt Adam as the optimizer. In the initial training of CMN, we set the down-sampling rate e and batch size to 0.25 and 8 respectively. To learn the trimap prediction, the kernel size of erosion-dilation operation in generating GT trimap and t! is set to be 25 x 25. In FCM, set L., L, 2. And the learning rates are set to 5e-5 for the backbone, and 1e-4 for ASPP, FCM, and three decoders. In the joint training of CMN and BMN, the batch size is 4 and the learning rates of the backbone, ASPP, FCM, decoders, and BMN are set to le-5, le-5, 1e-5, 2e-5, 3e-5, 3e-4 respectively. In CMN training, we use a single Nvidia GeForce RTX 3090 to accelerate training. In the joint training, two Nvidia GeForce RTX 3090 cards are used for distributed training. For inference and following evaluation experiments, we
20, r r= 5, 0.7, and q = 5 in the flash guided trimap prediction process. We also use Segment Anything Model (SAM) [Kirillov al. 2023) the pretrained segmentation model.
16

---

Published in Transactions on Machine Learning Research (12/2025)
No-flash images (Source image)
Flash image
Given background
BGMv2
MODNet
MAM
RVM
SGHM
P3M-Net
Mat
Ours
GT
No-flash images (Source image)
Flash image
Given background
BGMv2
MODNet
MAM
RVM
SGHM
P3M-Net
Smart
Qurs
GT
Figure 10: Qualitative results on the synthetic data with dynamic backgrounds.
5.3
Evaluation Metrics
We use four common metrics proposed in Rhemann et al. (2009) to measure the quality of the alpha matte prediction with ground-truth alpha matte, including mean square error (MSE), the sum of absolute difference (SAD), the gradient difference (Grad) and the connectivity difference (Conn). All MSE values are scaled by 10-3
5.4 Comparisons on Synthetic Data
We composite testing foreground videos and testing dynamic background videos of our dataset introduced in Sec. 3 to build the test set, and perform both quantitative and qualitative comparisons on it.
5.4.1 Quantitative Comparison
For quantitative comparison, we benchmark one background- matting method (BGMv2 (Lin et al. 2021)), ) and several trimap-free matting methods including MODNet (Ke al. 2022). SHM (Chen et al.
2018 LF (Zhang et al.) 2019), HATT (Qiao et al. 2020 RVM (Lin et al. 2022),
SGHM Chen et al.
2022b4, PPM (Chen et al. 2022a). GFM (Li et al. 2022 and P3M-Net (Ma et al. 2023) We also include
two interactive matting methods, including SmartMat (Ye et al. 2024) and MAM (Li et al. 2024c Since SmartMat supports automatic matting originally, we apply it in such mode directly. For MAM, since it requires user interaction as input, we utilize one of the state-of the- detection models (GDINO (Lu et al. 2025)) to obtain the bounding box of the foreground set "foreground person") as prompt for MAM for further alpha prediction. For a fair comparison, all methods are retrained the same mixed dataset. For MAM, follow its original training settings, which only train the matting components and let the SAM (Kirlllov et al. 2023 backbone frozen. In addition, we retrain the BGMv2 with three types of input and evaluate them respectively, including with/withonn subject image pairs, different image pairs of the same subject, and flash/ nl-sshh image pairs. The comparison with the BGMv2 is performed with both static and dynamic backgrounds. For static background, the input image pair has the same background. For dynamic background, the backgrounds of the input image pair are under the same scene but are different caused by dynamic changes. All the test samples for testing are resized resolution of 1280 720.
the no-flash image (with the text input for GDINO
17

---

Published in Transactions on Machine Learning Research (12/2025)
Table 3: Ablation study on the proposed FNF test set with dynamic backgrounds.
Fast:
Methods
MSEL
1.29
1:57
4020.530
5153.715
SAD.
Eeniuate
mattics
Cradl
5751 102
Conná
MSF1
2354
1.311
5/098857
SADI
Evoluste melknas
1970.035
18SUSIT
GradL
Conni
Kerne Sae
15x15
MISEL
0,619
1790.548
SA11
Eraluate
3767
musciiss
Goall
1617.709
Cami
17%
1212
1,997
2744.000
2617.628
3574
9005.000
2890.563
07
16602
0.794
2267.089
1494.74
$7442.30
4276.629
1841
1826-300
35x35
25
45x04
0.791
0.622
0008
2000.020
2071.128
1804.74
3742
4132.481
19668001
1625.80
SUDAM
0.832
0,836
2035,576
1830.741
3792
4207-151
1600.300
1855.972
088
0880
0.700
20001202
1502.00
3861 104
1302.00
1901
55x55
0.878
2457.646
4019.205
2152,018
(a) Each component. Progressively at- taching each component significant improve the performance, demonstrating importance of each module.
(b) Flash ratio map trunca-
(c) Kernel size of erosion-dilation
tion threshold 6. Using lower higher 6 will result. in inaccu- rate té, thus degrade the perfor- manoe.
for generating GT trimaps. Larger
kernel size of crosion-dilation will make the performance worse.
Medium Pllse
Foalinte metris
Fosiintt metrics
Karaal Size
MSEI
SALD
Grail
Couns
MSE4
SALD
Gracl
Cunal
Segmentatin model
Exshate saniio
0.622
810
3741. 078
1826
1,199
2641.574
2082.319
MSR.
SAD.
Grdl]
Conn
0.885
750
0.022
1900.7
8762200
Inter
39125
B6622
1904.741
5742.211
1626.389
6
0.602
1904.742
5742.200
1626.340
(f) Pre-trained segmentation model for generating flash trimap. Our method not selective for different seg-
(d) Kernel size of medium filter for generating flash trimap. The effect of adopting different medium filter kernel on the performance is negligible.
Number of confidence points for generating flash trimap. Smaller 9 results in
(c)
mentation modcls.
worse performance.
The quantitative comparison result is shown in Tab. 2 According to the results, we found our model shows superior performance on dynamic scenes while slightly falling behind BGMv2 on static scenes. This mainly because modeling static and dynamic scenes involves contradictory objectives: static scenes prioritize the detection of foreground inconsistency solcly, while dynamic scenes require modeling high-level foreground correspondences under motion and pose variations in both foreground and background. It is also worth knowing that dynamic scenes generalization of static ones, and training for broader generalization often reduces overfitting to simpler tasks, leading to slightly lower performance on static cases. This phenomenon aligns with the bias-varianee dilemma (Zou & Hastie 2005; Kohavi et al. 1996 Geman et al. 1992). showing that improving generalization may sacrifice some task-specific accuracy. Despite this, our model demonstrates strong generalization, excelling in dynamiic backgrounds and effectively handling both static and dynamic scenarios. This versatility is a key strength, even comes a small cost in static performance compared to static-specific methods like BGMv2.
Moreover, when training with flash/ sechh/oo-Htaoo no-flash image pairs using the architecture of BGMv2, it performs much worse than our model. This also justifies the rationality of our model archilecture for predicting alpha matte under dynamically changing backgrounds using fash/no-ffashl pairs. Also, since the input of trimap-fres based methods is only one single image and docs not contain any priors, they do not perform well in some diverse backgrounds. The corresponding quantitative results also show their performance weaker than our model in the test set. It is worth noting that our proposed method still outperforms MAM (Li et al. 2024c) on the dynamic scenes although it adopts SAM (Kirillov et al. 2023) as backbone. However, in addition to generating t/ our model does not directly use any external segmentation model to solve the alpha matte of the no-flash flash image.
5.4.2 Qualitative Comparison
We also select several latest state-of-theeart trimap-free methods (MODNet (Ke et al. 2022
RVM Lin et al. MAM Li et al.
p022p. SGHM (Chen et al. 2022b), P3M-Net (Ma et al.. 2023). SmartMat (Ye et al. 2024 2024cp) and BGMv2 (Lin et al. 2021) for qualitative comparison. When dealing with static background, as shown in Fig. g we can see the performance gap between BGMv2 and our model is only reflected in the details of the edges in static backgrounds. However, BGMv2 performs poorly when the background is not aligned due objects moving or camera shaking, which is shown in Fig. 10) Most of trimap-free methods also suffer from interference from background salient objects, but our model is robust dynamic backgrounds and also have well performance.
18

---

Published in Transactions on Machine Learning Research (12/2025)
5.5
Ablation Study
5.5.1
Component Analysis
To demonstrate the effect of the proposed Foreground Correlation Module and Boundary Matting Network, we first define the bascline network that only includes the encoder, ASPP module, and single decoder without down-sampling. This baseline takes Ind as input only, and we denole it Basic. We further replace the single decoder with three separate decoders, denoted as SepDecoder. Then we progressively attach BMN with down-sampling before the encoder, and each branch of FCM to build the corresponding combinations. These combinations are all retrained on the mixed dataset, and quantitative evaluation tests are carried out on the test set under dynamic background. The quantitative results are shown in Tab. 4a We can see that decomposing the matting problem into semantic and detail predictions separately can effectively improve the matting performance. The BMN can recover fine details to predict alpha matte with higher-qualite quality in the original resolution. However, attaching 7 with only self-attention blocks has minor performance improvement, since single no-flash image docs not contain any foreground cues in various dynamic scenes. We further start to utilize the guidance information from / by taking (1") 11, (!) as input, and introduce T' and add cross-attention blocks in Tm for modeling the foreground correspondence, resulting a significant improvement. On this basis, the model with CBAM module adopted before Tmf can achieve better performance, showing the effectiveness of feature re-attention operation.
5.5.2 Hyper-Parametee Analysis
We also conduct ablation studies on various hyper-parametes aspects, including the truncation threshold 5 of the flash ratio map (presented in Tab. 4bb, and the kernel size of erosion-dilation for generating the ground truth trimap (shown in Tab. 4ch.
For the truncation threshold 8, we observed that using smaller threshold value degrades performance, as it results a ratio map containing more inaccurate regions with low confidence scores. Conversely, a larger overly truncates regions with higher confidence, leading to inaccuracies in mask prediction for the flash image and a deterioration in model performance.
For the kernel size for erosion-dilation, we observed that employing kernel size smaller than 25 x 25 can lead to slight improvements in model performance. Conversely, using a larger kernel size introduces more unknown regions in the ground truth trimap, potentially covering the entire portrait area. This forces the detailed branch to learn semantic information, ultimately resulting in a decrease in model performance.
We also explore the ellect of adopting different kernel size of the medium filter and different number of confidence points g when generating the flash trimap t!. The corresponding quantative ablation results are presented in Tab. 4d and Tab. 4e respectively. We found that using smaller or larger filter kernels has minimal impact on performance. However, larger kernels increase computational cost due to higher sorting complexity, leading us to adopt a median filter kernel size of 5 in this study. Regarding the number of points q, smaller values may result in obvious performance degradation, as insufficient confident points may fail to fully represent the foreground in the flash image, leading to incomplete or inaccurate flash cues. Conversely, larger q values have almost no impact on performance.
5.5.3 Pre-trained Segmentation Model
The flexible design of our method allows the pretrained segmentation model for generating flash trimap can be replaced with any other more lightweight model to generate accurate flash cues. Such substitution would not impact the overall performance of our approach, ensuring robustness and adaptability across different model choices. To further verify this, we conducted an ablation study by replacing current SAM [Krilllv et al. 2023) with InterFormer (Huang el al. (023). a lightweight, real-time interactive image segnentation method that supports shit-prrompt inputs to generate segmentation masks, similar to SAM. The experimental results (showed in Tab. 4f demonstrate that our model's performance remains largely unaffected by this substitution. Moreover, the use of InterFormer (Huang et al. 2023) accelerates the pre-processing stage, further enhancing the practicality of approach.
19

---

Published in Transactions on Machine Learning Research (12/2025)
Flash
Flash
Flash
Alpha prodiction of No-flash image no-flash image
Alpha prediction No-flash image no-flash image
Alpha prediction of No-flash image no-flash image
(a) Strong light environment.
(b) Environment with additional light sources in the background.
(c) Environment with specular objects.
No-flash image
No-flash image
No-flash image
Alpha prediction of Flash image no-flash image
Alpha prediction of fooffash image
Alpha prediction Flash image no-flash image
Flash image
(d)
Foreground-to-camees distance.
(c) Flash intensities.
(f) Flash color temperature.
Figure 11: Evaluation in different light environment, (a-c) and different flash conditions (d-f).
5.6 Environmental Robustness Evaluation
To assess the robustness of our model under varying light conditions, conduct experiments in three specific lighting environments, which included:
1. A strong light environment (illustrated in Fig. 11ay: Despite the significantly reduced difference between the flash image and the no-flash image in this scenario, our model could still extract correct flash cues and predict accurate alpha mattes for the no-flash image. This attributed the preprocessing of the flash ratio map, which inchudes truncation and modian filtering operations.
An environment with additional light sources in the background (shown in Fig. I1b]: Given that the flash ratio map is caleulated based on the difference between the flash image and the no-flash image, remains unaffected by background light sources under the flash photography assumption. Consequently, model could successfully predict the correct alpha matte.
20

---

Published in Transactions on Machine Learning Research (12/2025)
3. An environment with specular objects (depicted in Fig. 11c When the light reflected by specular objects is not overly strong, the truncation and median filtering operations effectively filter out their impact, ensuring good model performance. However, in cases of excessively strong environmental light, such as a person facing the sun, where the flash is ineffective, our model may struggle to handle the situation. Additional discussions regarding this limitation can be found in Sec. 5.10
In addition to assessing our model's performance under various lighting conditions, we also conducted eval- uations under different flash conditions, encompassing:
ponground-to-camennn distance (illustrated in Fig. 1idh: : We ensured that the other two conditions remained unchanged while varying the foreground-to-camerr. distance. Despite the resulting reduc- tion in flash differences between the flash image and the no-flash image, our model effectively utilized these differences flash cues for prediction.
Flash intensitics (shown in Fig. 11ch: Similar to the previous scenario, we kept the other two conditions constant while adjusting flash intensities. Again, our model successfully harnessed the flash differences for accurate alpha matte prediction.
Flash color temperature (depicted in Fig. 111 We illuminated the foreground with three different color temperatures (3,200K, 4,400K, and 5, 5,60KK from top to bottom) acquire flash images. Despite the differences in color temperature, influenced by the flash, most foreground pixels exhibited an intensity increase. Since the flash ratio map is derived from pixel intensity enhancement, areas with higher confidence in the flash ratio map continued to represent the foreground. Consequently, our model accurately predicted the alpha matte of the no-flash image using flash images obtained under varying color temperatures. This demonstrates the robustness of our model to flash color temperature variations.
5.7 Evaluation on Real-world Data
We also capture some real-world vidcos using a smartphone containing flash and no-flash frames in dynamic scenes, and perform comparisons with BGMv2 and the four trimap-free methods.
Fig. 12 shows the comparison between our model and BGMv2. Since the basic assumption of BGMv2 is all pixels in the background are aligned, pixels changed in background will be considered as foreground, which causes the failure of BGMv2 in the dynamically changing regions. Moreover, the misjudgment of the semantic information also causes poor performance of boundary details prediction (e.g., hands, eyeglassos) for BGMv2. This also highlights our method's core advantage, which lies in its robust handling of dynamic scenes, offering greater flexibility and usability compared to BGMv2, which is limited to static scenes with strict pixel alignment requirements.
Qualitative comparison with trimap-free methods on real-world data is also shown in Fig. 13 Although MODNet (Ke et al., (2022) can predict alpha matte with clearer details (e.g., hair, eyeglasses), usually failed in the semantic prediction. For RVM (Lin al.] 2022),, it can predict the semantic information of the task well. However, since RVM up-samples the prediction results based on Deep Guided Filter (DGF) (Wul et a.l.
2018). resulting in unclear predictions for some regions such as glasses and fingers. SGHM (Chen 2022b and P3M-Net (Ma et al.] 2023) also suffer from semantic prediction errors in some challenge cases, while our model is more robust and can produce alpha mattes with high-quality details.
et al.
5.8 Experiemnts in Low-light Environment
To further evaluate the effectiveness of our model, we conduct following experiments in the low-light envi- ronment. In such scenario, the contrast of foregrouds in the flash image and no-flash image will be very strong, which is beneficial for extracting robust guidance for the foreground from the flash image.
To verify this, considering there no existing dynamic flash matting test set in low-light environment for evaluation, collect new benchmark by the following steps. We invited five participants take the
21

---

Published in Transactions on Machine Learning Research (12/2025)
Flash image
No-flash image
Background
BGMv2 Lin et al.
2021
Ours
image
No-flash image
Background
BGMv2
Lin al. 2021
Ours
Figure 12: Comparison with BGMv2
Lin
on real-world data captured by smartphone. All 2021 is dynamic.
2021
the background provided to BGMv2
(Lin et al.
Flash imaae
No-flash imaage
MÖDNet (Ke et al. 2022
RVM (Li al. 2022
MAM Li
2024c
SGHM [Cee
20226
P3M-Net Ma
2023
SmartMat Ye
2024
Flash image
No-flash image
MODNet Ke et al
2022
RVM Lin et al.
2022
MAM Li al.
2024e
SGHM (Chen et
2022b
P3M-Net Ma et al.
2023
Smart Mat Yo et al.
2024
Ours
image
image
(Ke
2022
(Lin al.
2022
MAM T.i
2024e
SGHM
Chen
20226
P3M-Nee Ma et al. 2023
Smart Ye al. 2024
Ours
Figure 13: Comparison with trimap-free methods on real-world data captured by smartphone.
22

---

Published in Transactions on Machine Learning Research (12/2025)
Table 4: Quantitative comparison with baselines in the dark environment on our collected benchmark. Our method outperforms all the basclines in such low-light conditions.
Evaluate metrics
Methods
MSEI
SADI
Grad]
Conn.
BGMv2 Lin et al. 2021
122.1
118376.4
19102.02
112306.5
MODNet (ee el al.
2022
21.58
23307.67
14334.61
17390.03
RVM (Lin et al. 2022)
1.658
4388.372
3992.540
3562.170
SGHM (Chen et al.
2022b)
1.014
2547.016
2652.587
1940.017
P3M-Net (Ma et. al.]
2023)
1.258
2943.804
3147.441
2241.799
MAM (Li et al.] 2024c)
2.157
5329.854
3131.071
4503.777
Mat (Ye et al.
2024)
1.251
3048. 106
5870.643
2322.956
Ours
0.863
1908.671
2174.382
1763.055
totally ten video clips in low-light environment using a mobile phone as camera. All the clips was captured in different outdoor scenes in evening. We also used hand-held lamp to light the person intermittently during video recording process for further acquiring flash frame. We also ensured the camera parameters remain the same (e.g., ISO, shutter speed) during taking the video in single scene. For cach video, we selected five representative frames from both the flash and no-flash sequences, and manually annotated GT alpha matte for them using Adobe Photoshop. Finally, we build a benchmark with 50 flash/no-flash image pairs for evaluation in low-light environment.
We then tested both the baselines and our method on this benchmark. The corresponding quantitative results are shown in Tab. 4 We found that even without including low-light data during training, our method still exhibited significant advantages over the baselines. This is because the stronger foreground contrast in low-light conditions allows the flash image to provide more effective guidance for matting the no-flash image. We also showcase the qualitative comparison in Fig. 14 We can see that BGMv2 {Lin) ctt al.) 2021) and MODNet (Ke et al. 2022) failed to generalize to the low-light environment. Although other trimap-Iree baselines show good performance in such scenarios, they tend to predict more blurred alpha in the boundary regions, especially for SGHM (Chen et al., 2022b) and P3M-Net (Ma et al.) 2023). SmartMat (Ye et al., 2024) can produce more accurate alpha but with more artifacts and noiso-like patterns in some cases (e.g., the first sample in Fig. 14p. In contrast, our method can generate more accurate alpha mattes even the no-flash image is slightly degradated due to the low-light condition, which also demonstrates the robustness of our model in real-world scenarios. In future work, we plan to collect more data in low-light environments to further advance rescarch on the flash matting problem.
5.9 Model Complexity Comparison
We conduct a model complexity comparison, which encompasses the number of parameters (M), compu- tational complexity (GMac), and inference time (ms), detailed in Tab. 5 For the backbones, use MobileNet (Sndder et al. 2018) for MODNet (Ke et al. 2022), VITAF-S (Xu et al., 2021 for P3M- Net Ma et al. )222), DINOv2-VITSI4 (Oquab et al.] 2024 Dosovitskiy et al. 2021 for SmartMat (Ye ct al. 2024 SAM-B Kirillov et al. 2023) for MAM (Li et al. 2024c), and ResNet-50 (He et al., 2016) for the remaining models. To assess inference time, we resized the input image to 1280 720, and the reported inference times were obtained using an NVIDIA RTX A5000. We also report the complexity of the preprocessing stage of our model, which includes generating the flash ratio map and segmentation process (using SAM-B (Krillov et al. )023)) to obtain t!.
We report the computational complexity and inference time for our model in two distinct cases. The first case, denoted as 1st frame, involves the model taking 1a ,16, laf as input and working end-to-end. In the second case, denoted as other frames, the computation result of the flash image until I is given, and only new no-flash frames are forwarded, significantly reducing computational costs. Comparatively, BGMv2 (Lin et al. (021)) and RVM (Lin et al. 2022) exhibit lower computational complexity and inference time. This can be attributed to several factors, such as BGMv2 using single decoder for direct alpha
23

---

Published in Transactions on Machine Learning Research (12/2025)
No-flash images (Source itaage)
Flash image
Given background
BGMv2
MODNet
RVM
P3M-Net
MAM
SmartMat
Ours
GT
No-Hlash images (Source image)
Flash image
Given background
BGMv2
MODNet
SGHM
P3M-Net
MAM
SmartMat
Ours
GT
No-flash images (Source image)
Flash image
Given background
BGMv2
MODNet
RVM
SGHM
P3M-Net
SmartMat
Ours
Figure 14: Qualitative comparison with baselines in the low-light environment. Zoom in for better viewing.
24

---

Published in Transactions on Machine Learning Research (12/2025)
Table 5: Comparison of number of model parameters (M), computational complexity (GMac), and inference
time (ms). For ours, (1tt frame) and (other frames) represent forwarding the first no-flash frame and the flash image initially and forwarding other no-flash frames with given flash image features, respectively.
#Params
Comp.
Method
Infer. (ms)
(M)
(GMac)
BGMv2 Lin et al.
2021
40.25
17.47
13.27
MODNet (e et al.
2022
6.49
64.95
27.90
RVM LLi et al. 2022)
26.89
11.49
8.89
SGHM (Chen et al. 2022bb
40.25
25.81
25.96
P3M-Net (Ma et al.
2023
39.48
300.39
36.79
MAM (Li et al. 2024c)
96.45
383.02
533.2
SmartMat (Ye et al. 2024)
26.89
228.73
38.96
Preprocessing
93.74
370.47
122.32
Ours (1 frame)
60.29
34.06
70.09
Ours (other frames)
44.85
28.69
matte and foreground color prediction and RVM operating lower resolutions and performing up-sampling through DGF (Wu et al.) 2018) directly at the end of processing. Furthermore, SGHM (Chen et al. 2022b) only predicts alpha matte without foreground color, resulting in a lower computational complexity compared to our model. For MAM (Li et al.) 2024c) and SmartMat. (Ye et al., 2024), they have relatively higher G.Mcc since they adopt VIT (Dosovitskiy et al. 2021) as backbone. Even if we choose the lightest version of SAM (SAM-B), MAM still suffers from high computational complexity and inference time.
Regarding our model, despite having a preprocessing stage with relatively higher complexity (since SAM is used) at the beginning, we observed significant reduction in computational complexity and inference time when forwarding only new no-flash frames, while still maintaining competitive performance. This optimization is especially noticeable in the "only-no-flash forwarding" scenario, where model's inference time closely aligns with that of MODNet (Ke et al. 2022) and SGHM (Chen et al. 2022b), despite our model's larger size. In practical application like video conference, since the preprocessing and "1st frame forwarding" " stages typically only need to be performed once at the beginning, the overall efficiency of our model comparable to other methods.
While it is true that our model boasts larger number of parameters compared to other models, this is primarily attributed to our use of two separate ResNet-50 encoders that do not share weights, as well as the inclusion of the FCM module. Our ablation experiments, detailed in Sec. 5.5 demonstrated the essential roles played by these two modules in establishing the connection between the flash image and the no-flash image foreground. These modules contributed significantly to the overall performance improvement of our model. In future work, we will explore the development of more lightweight modules for flash image encoding and foreground correction.
5.10 Extreme Cases Analysis
Our model performs reliably across a wide range of typical scenarios; however, failures may occur under specific, extreme conditions, which we systematically categorize into two aspects as following.
First, the flash photography assumption may violate in some rare scenarios where the flash/no-flash con- trast is severely disrupted. These conditions lead to inaccurate flash ratio map estimations, resulting in unreliable matting outputs. For instance, when the foreground is outdoors under extremely strong sunlight (see Fig. 15a). the flash/no- no-sls photography assumption will be violated, since the flash difference between the flash image and the no-flash image becomes so minimal that no flash cues can be reliably detected. A potential solution to address this issue the use of infrared light to illuminate the foreground, which will be explored in our future research. However, important to note that such light-related challenging scenarios are infrequent in typical environments, particularly indoors. These limitations do not compromise the per- formance of our model in applications such video conferencing, live boardeasting, and auggmened
25

---

Published in Transactions on Machine Learning Research (12/2025)
Flash Imaae
No-Flash Image
Alpha Prediction
Flash Image
No-Flash Image
Alpha Prediction
(a) Very Strong Sunlight
(b) Large Motion Variation
Flash Image
No-Plash Frame
Prediction
No-Flash Frame B
Prediction
No-Flash Frame C
Prediction
(c) Prediction Artifacts due Fast Motion Blur
Figure 15: Extreme cases. Our method is susceptible to the effects of flash photography assumptions that are not met such scenes with extreme light conditions, e.g.no enough flash contrast. on the foreground in the environment with very strong sunlight (a). Other challenge cases such large motion variation (b) blurred image caused by extreme temporal conditions (e.g., fast movement) can also cause our model to produce suboptinal results.
reality. Since modern imaging devices, including smartphones and webcams, are commonly equipped with built-in flash modules or external lighting aids, which are routinely used in professional and semi-professional setups to enhance visual quality. Our approach does not impose restrictive requirements such continuous flash triggering user calibration. Capturing flash image is one-time, automatic process that does not interfere with user experience. Compared to BGMv2 (Lin et al. 2021 which requires user intervention prior to each capture, our method offers improved usability and flexibility, making it more suitable for practical deployment.
Second, some extreme temporal conditions (e.g., fast movement), such as large motion variation or motion- blurred images, can also make our model to produce suboptimal results (see Fig. 15b and 15ch. This limitation arises because our method processes frames independently, without explicit temporal modeling, making it sensitive to significant motion image degradation in such rare scenarios. To address them for future research, we plan to integrate motion cues from flash/no-flash pairs and explore techniques such memory bank to explicitly model temporal information, further enhancing robustness for extreme pose variations while maintaining the model's strong performance in typical scenarios.
6 Conclusion
To solve the malting problem with dynamic backgrounds, this paper proposes a port matting model based on flash and no-flash images, which provides additional foreground priors reveal the foreground objects. Our model is insensitive to dynamically changing backgrounds, and does not require that the movements of the foreground characters map out multiple no-flash images, which is very practical. This paper also presents the first high-quality matting dataset of flash and no-flash single-person images, which provides an effective resource for su subsequent research work on flash and no-flash matting work. Experiments show that our proposed method outperforms existing matting methods that do not require a tripartite map as a prior in dynamic backgrounds, showing the offectiveness of the model.
exactly the same in the two images. Therefore, taking flash image
Future Works. Although method shows strong generalization both static and dynamic backgrounds, it still has slightly weaker performance in static scenes comparing with BGMv2 (Lin et al. 2021). Apart from the future work discussion in Sec. 5.10 we also provide two possible future directions to improve this. First, we can further integrale some pixel-flow estimation Lechniques, such as optical flow, inlo our foreground correlation modeling based on current flash cues, which can better capture subtle spatial and temporal relationships in static scenes. Optical flow can provide precise motion cues, enabling the model distinguish static background elements from foreground objects more effectively. Another direction is to explicitly model static background information by introducing background restoration subtask that
26

---

Published in Transactions on Machine Learning Research (12/2025)
uses generative impainting prior, taking the flash cues as guidance. By training the model to reconstruct background regions, it can learn robust representation of static scene features, thereby reducing artifacts and improving consistency in static matting tasks. Furthermore, considering the privacy-sensitive scenarios, we can also adapt the method to operate with infrared illumination, which is invisible to the human eye. We leave this promising direction for future research.
Acknowledgmments. This research is supported by the Guangdong Natural Science Funds for Distin- guished Young Scholars (Grant 2023B1515020097) Natural Science Foundation of Guangdong Province (2023A1515012894), Key R&D Project of Guangzhou Science and Technology Plan (2023B01J0002), Na- tional Natural Science Foundation of China (Grant No.: 62502117). And the National Rescarch Founda- tion Singapore under its AI Singapore Programme (AISG Award No: -0023-VV-022--111), the National Research Foundation Singapore under the AI Singapore Programme (AISG Award No: AISG4-TC-2025- 018-SGKR), the Ministry of Education, Singapore, under its Academic Research Fund Tier (Award No. 100--22P20125-00010 the Ministry of Education, Singapore, under its Academic Research Fund Tier (Award No. MSS25C004), the National Natural Science Foundation of China (Grant No.: 62502117), the Shenzhen Natural Science Foundation (No. JCY120230004155304))) and the Lee Kong Chian Fellowships.
References
Yagiz Aksoy, Tunc Ozan Aydin, and Marc Pollefeys. Designing effective inter-pixel information flow for natural image malting. In CVPR, 2017.
Huanqia Cai, Fanglei Xue, Tele Xu, and Lili Guo. Transmatting: Enhancing transparent objects matting with transformers. In ECCV, 2022.
Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan, Haibin Huang, Jiangyu Liu, Jiaming Liu, Jiaying Liu, Jue Wang, and Jian Sun. Disentangled image matting. In ICCV, 2019.
Guowei Chen, Yi Liu, Jian Wang, Juncai Peng, Yuying Hao, Lutao Chu, Shiyu Tang, Zewu Wu, Zeyu Chen, Zhiliang Yu, et al. Pp-matting: High-accuracy natural image matting, arXiv preprint arXiv:2204.09433, 2022a.
Liang-Chich Chich Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXio preprint or 10Xi::170.0000 770605587, 2017.
Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE TPAMI, 2013.
Quan Chen, Ticzheng Gc, Yanyu Xu, Zhiqiang Zhang, Xinxin Yang, and Kun Gai. Semantic human matting. ACM Multimedia, 2018.
Xiangguang Chen, Ye Zhu, Yu Li, Binglao Fu, Lei Sun, Ying Shan, and Shan Liu. Robust huraa matting via semantic guidance. In ACCV, 2022b.
Yung- Yu Chuang, Brian Curless, David Salesin, and Richard Szeliski. A bayesian approach to digital matting. In CVPR, 2001.
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Bm3d image denoising with shape-adaptive principal component analysis. In Signal Processing with Adaptive Sparse Structured Rep- resentations, 2009.
Linhui Dai, Xiang Song, Xiaohong Liu, Chengqi Li, Zhihao Shi, Jun Chen, and Martin Brooks. Enabling trimap-free image matting with a frequency-guie saliency- aware network via joint learning. IEEE TMM, 25:4868 4879, 2022.
Alexcy Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Un- terthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 16 words: Transformers for image recognition at scale. In ICLR, 2021.
27

---

Published in Transactions on Machine Learning Research (12/2025)
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Wimn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. 2010.
Xiacxue Feng, Xiaohui Liang, and Zili Zhang. A cluster sampling method for image matting via sparse coding. In ECCV, 2016.
Eduardo ST. Gastal and Manuel M Oliveira. Shared sampling for real-time alpha matting. In Computer Graphics Forum, 2010.
Stuart Geman, Elie Bienenstock, and René Doursat. Neural networks and the bias/variann dilemma. Neural computation, 1992.
Zeqq Gu, Wenqi Xian, Noah Snavely, and Abe Davis. Factormatte: Redefining video matting for re- composition tasks. ACM TOG, 2023.
He Guo, Zixuan Ye, Zhiguo Cao, and Hao Lu. In-context matting. In CVPR, 2024.
Kaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun. A global sampling method for alpha matting. In CVPR, 2011.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual Icarning for image recognition. In CVPR, 2016.
Shengfeng He and Rynson WH Lau. Saliency detection with flash and no-flash image pairs. In ECCV, 2014.
Jonathan Ho, Ajay Jain, and Picter Abbeel. Denoising diffusion probabilistic models. NewrIPS, 2020.
Qiqi Hou and Feng Liu. Context-aware image matting for simultaneous foreground and alpha estimation.
In ICCV, 2019.
Yihan Hu, Yiheng Lin, Wei Wang, Yao Zhao, Yunchao Wei, and Humphrey Shi. Diffusion for natural image matting. In ECCV, 2024.
You Huang, Hao Yang, Ke Sun, Shengehuan Zhang, Liujuan Cao, Guannan Jiang, and Rongrong Ji. Inter- 2023.
former: Real-time interactive image segmentation. In ICCV, pp.
Sergey Tofle and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
Weihao Jiang, Zhaozhi Xie, Yuxiang Lu, Longjie Qi, Jingyong Cai, Hiroyuki Uchiyama, Bin Chen, Yue Ding, and Hongtao Lu. Learning auxiliary representations with inconsistency-guider detail regularization for mask-guuded matting. IEEE TMM, 2025.
Jubin Johnson, Ehsan Shahrian Varnousfaderani, Hisham Cholakkal, and Deepu Rajan. Sparse coding for alpha matting. IEEE TIP, 2016.
Zhanghan Ke, Jiayu Sun, Kaican Li, Qiong Yan, and Rynson WH Lau. Modnet: real-time trimap-free portrait matting via objective decomposition. In AAAI, 2022.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitchead, Alexander C Berg, Wan-Yen Lo, al. Segment anything. In ICCV, 2023.
Ron Kohavi, David H Wolpert, et al. Bias plus variance decomposition for zero-one loss functions. In ICML, 1996.
Dawa Chyophel Lepcha, Bhawna Goyal, and Ayush Dogra Image matting: a comprehensive survey techniques, comparative analysis, applications and future scope. International Journal of Image and Graphics, 2023.
Anat Levin, Dani Lischinski, and Yair Weiss. A form solution natural image matting. JEEE TPAMI, 2007.
28

---

Published in Transactions on Machine Learning Research (12/2025)
Anat Levin, Alex Rav-Acha, and Dani Lischinski. Spectral matting. IEEE TPAMI, 2008
Jiachen Li, Mariannaa Ohanyan, Vidit Goel, Shant Navasardyan, Yunchao Wei, and Humphrey Shi. Video- matt: A simple baseline for accessible real-time video matting. In CVPR, 2023a.
Jiachen Li, Vidit Goel, Marianna Ohanyan, Shant Navasardyan, Yunchao Wei, and Humphrey Shi. Vi- former: End-to-end video matting with transformer. In WACV, 2024a.
Jiachen Li, Roberto Henschel, Vidit Goel, Marianna Ohanyan, Shant Navasardyan, and Humphrey Shi.
Video instance matting. In WACV, 2024b.
Jiachen Li, Jitesh Jain, and Humphrey Shi. Matting anything. In CVPR Workshop, 2024c.
Jizhizi Li, Jing Zhang, Stephen J Maybank, and Dacheng Tao. Bridging composite and real: towards end-to-end deep image matting. 2022.
Jizhizi Li, Jing Zhang, and Dacheng Tao. Deep image matting. comprehensive survey. ar preprinl arXiv:2304.0400 2023b.
Jizhizi Li, Jing Zhang, and Dacheng Tao. Referring image matting. In CVPR, 2023c.
Xiaodi Li, Zongxin Yang. Ruijic Quan, and Yi Yang. DRIP: Unleashing diffusion priors for joint foreground and alpha prediction in image matting. In NeurIPS, 2024d.
Yaoyi Li and Hongtao Lu. Natural image matting via guided contextual attention. In AAAI, 2020.
Chung-Ching Lin, Jiang Wang, Kun Luo, Kevin Lin, Linjic Li, Lijuan Wang, and Zicheng Liu. Adaptive human matting for dynamic videos. In CVPR, 2023.
Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian Curless, Steven M Seitz, and Ira Kemelmaccerr-Shiie Shhizerrrn-S.iieennn.. Real-time high-resolution background maiting. In CVPR, 2021.
Shanchuan Lin, Linjie Yang, Imran Saleemi, and Soumyadip Sengupta. Robust high-resolution video matting with temporal guidance. In WACV, 2022.
Chang Liu, Henghui Ding, and Xudong Jiang. Towards enhancing fine-grained details for image matting, In WACV, 2021a.
Jinlin Liu, Yuan Yao, Wendi Hou, Miaomiao Cui, Xuansong Xie, Changshui Zhang, and Xian-sheng Hua. Boosting semantic human matting with coarse annotations. In CVPR, 2020.
Qinglin Liu, Haozhe Xie, Shengping Zhang, Bineng Zhong, and Rongrong Ji. Long-range feature propagating for natural image matting. In ACM Multimedia, 2021b.
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In ECCV, 2025.
Yuhao Liu, Jiake Xie, Yu Qiao, Yong Tang, and Xin Yang. Prior-induced information alignment for image matting. IEEE TMM, 24:2727 2738, 2021c.
Hao Lu, Yutong Dai, Chunhua Shen, and Songcen Xu. Indices matter: Learning to index for deep image matting. In ICCV, 2019.
Sebastian Lutz, Konstantinos Amplianitis, and Aljosa Smolic. Alphagan: Generative adversarial networks for natural image matting. arXiv preprint arXiv:1807.10088, 2018.
Sihan Ma, Jizhizi Li, Jing Zhang, He Zhang, and Dacheng Tao. Rethinking portrait matting with privacy preserving. IJCV, 2023.
Vinod Nair and Geoffrey Hinton. Rectified lincar units improve restricted boltzmann machines. In ICML, 2010.
29

---

Published in Transactions on Machine Learning Research (12/2025)
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Woj- ciech Galuba, Russell Howes, Yao Huang, Shang- Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnacve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bo- janowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learn- ing Research, 2024.
GyuTae Park, JongJoon Son, JaeYoung Yoo, SeHo Kim, and Nojun Kwak. Matteformer: Transformer- based image matting via prior-tokens. In CVPR, 2022.
Kwanyong Park, Sanghyun Woo, Seoung Wug Oh, In So Kweon, and Joon-Young Lee. Mask-guided matting in the wild. In CVPR, 2023.
Yu Qiao, Yuhao Liu, Xin Yang, Dongsheng Zhou, Mingliang Xu, Qiang Zhang, and Xiaopeng Wei, Attention- guided hicrarchical structure aggregation for image matting. In CVPR, 2020.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021.
Christoph Rhemann, Carsten Rother, Jue Wang, Margrit Gelautz, Pushmeet Kohli, and Pamela Rott. A perceptually motivated online benchmark for image matting. In CVPR, 2009.
Mark A Ruzon and Carlo Tomasi. Alpha estimation in natural images. In CVPR, 2000.
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Blang-Chiel Chen. Mobilenetv2: Inverted residuals and linear bottlenccks. In CVPR, 2018.
Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steven M Scitz, and Ira Kemelmacher-Shiizerme Background matting: The world is your green screen. In CVPR, 2020.
Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. Deep automatic portrait matting. In ECCV, 2016.
Jian Sun, Jiaya Jia, Chi-Keung Tang, and Heung-Yeun Shum. Poisson matting. In SIGGRAPH. 2004.
Jian Sun, Yin Li, Sing Bing Kang, and Heung- Young Shum. Flash matting. In SIGGRAPH. 2006.
Jian Sun, Sing Bing Kang, Zong-Ben Ben Xu, Xiaoou Tang, and Heung- Yeung Shum. Flash cut: Foreground extraction with flash and no-flash image pairs. In CVPR, 2007.
Jiayu Sun, Zhanghan Ke, Ke Xu, Fan Shao, Lihe Zhang, Huchuan Lu, and Rynson WH Lau. Semantics- adding flaw-erasing network for semantic human matting. In BMVC, 2022.
Yanan Sun, Chi-Keung Tang, and Yu- Wing Tai. Semantic image matting. In CVPR, 2021a.
Yanan Sun, Guanzhi Wang, Qiao Gu, Chi-Keung Tang, and Yu- Vinging Tai. Deep video matting via spatio- temporal aligument and aggregation. In CVPR, 2021b.
Yanan Sun, Chi-Keung Tang, and Yu-Wing Tai. Ultrahigh resolution image/video matting with spatio- temporal sparsity. In CVPR, 2023.
Jingwei Tang, Yagiz Aksoy, Cengiz Oztireli, Markus Gross, and Tune Ozan Aydin. Learning based sampling for natural image matting. In CVPR, 2019.
Jue Wang and Michael F Cohen. Optimized color sampling for robust matting. In CVPR, 2007.
Zhixiang Wang, Baiang Li, Jian Wang, Yu-Lun Liu, Jinwei Gu, Yung- Yu Chuang, and Shin Satoh. Matting by generation. In SIGGRAPH, 2024.
Sanghyun Woo, Jongchan Park, Joon- Joon-Young Lee, and In So Kweon. Cham: Convolutional block attention module. In ECCV, 2018.
30

---

Published in Transactions on Machine Learning Research (12/2025)
Huikai Wu, Shuai Zheng, Junge Zhang, and Kaiqi Huang. Fast end-to-end trainable guided filter. In CVPR, 2018.
Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, and Shengfeng He. Teaching diffusion models to ground alpha matte. Transactions Machine Learning Research, 2025.
Ning Xu, Brian Price, Scott Cohen, and Thomas Huang. Deep image matting. In CVPR, 2017.
Yangyang Xu, Shengfeng He, Wenqi Shao, Kwan- K Wong, Yu Qiao, and Ping Luo. Diffusionmat: Alpha matting as sequential refinement learning. arXiv preprint arXim:231 1. 13535, 2023.
Yufei Xu, Qiming Zhang, Jing Zhang, and Dacheng Tao. Vitae: Vision transformer advanced by exploring intrinsic inductive bias. NewrIPS, 2021.
Jingfeng Yao, Xinggang Wang, Shusheng Yang, and Baoyuan Wang. Vitmatte: Boosting image matting with pre-trained plain vision transformers. Information Fusion, 2024.
Zixuan Ye, Wenze Liu, He Guo, Yujia Liang, Chaoyi Hong, Hao Lu, and Zhiguo Cao. Unifying automatic and interactive matting with pretrained vits. In CVPR, 2024.
Haichao Yu, Ning Xu, Zilong Huang, Yugian Zhou, and Humphrey Shi. High-resolution deep image matting, In AAAI, 2021a.
Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, and Alan Yuille. Mask guided matting via progressive refinement. network. In CVPR, 2021b.
Yunke Zhang, Lixue Gong, Lubin Fan, Peiran Ren, Qixing Huang, Hujun Bao, and Weiwei Xu, A late fusion cmn for digital matting. In CVPR, 2019.
Yunke Zhang, Chi Wang, Miaomiao Cui, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Hujun Bao, Qixing Huang, and Weiwei Xu. Attention-guided temporally coherent video object matting. In ACM Multimedia, 2021.
Bingke Zhu, Yingying Chen, Jinqiao Wang, Si Liu, Bo Zhang, and Ming Tang. Fast deep matting for portrait animation on mobile phone. In ACM Mullimedia, 2017.
Hui Zou and Trevor Hastic. Regularization and variable selection via the clastic net Journal of the Royal Statislical Sociely Series B: Statisticul Melhodology, 2005.
31


Reviews:
[{"review_id": "Ouv4dIpN9b", "reviewer": "Reviewer_dJWH", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n- This paper addresses the real limitation of BGMv2's static background requirement, enabling matting with camera/background movement.\n- This paper conducts comprehensive ablations on components, hyperparameters, and multiple baselines with clear metrics.\n- The authors introduce the first flash/no-flash matting dataset that fills an important gap for this research direction.\n- The FCM's cross-attention mechanism is well-motivated for handling pose variations between flash/no-flash pairs.\n\nWeaknesses\n- The proposed method underperforms BGMv2 on static backgrounds without compelling justification for this trade-off.\n- Most of the testings are conducted on synthetic composites rather than real flash/no-flash captures. The preprocessing overheads (e.g., SAM) are excluded from the complexity analysis.\n- The paper only shows one Single failure case.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes using flash/no-flash image pairs for portrait matting instead of clean backgrounds. The authors introduce FNFNet with a transformer-based Foreground Correlation Module using cross-attention and flash ratio maps. The authors create the first flash/no-flash matting dataset (3,025 samples, 133 subjects) and demonstrate superior performance over trimap-free methods on dynamic backgrounds, though underperforming BGMv2 on static scenes.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Please provide a compelling analysis of when/why users should choose this method over BGMv2, given the performance degradation on static scenes. Include a decision framework or specific use cases where flash-based matting is superior.\n- Please include evaluation on real captured flash/no-flash pairs (not synthetic composites) to validate that the method works in practice. Compare pseudo-flash gamma adjustment against actual flash photography to verify this approximation.\n- Please fix the undefined behavior in Eq. 2 when histogram bins approach zero. Add explicit handling of edge cases and empty bins in the flash ratio calculation.\n- Please provide more comprehensive failure case analysis beyond the single outdoor example. Characterize limitations systematically (e.g., minimum flash intensity required, maximum pose variation tolerated, temporal constraints).", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "cmYJg3xq82", "reviewer": "Reviewer_WP5u", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "There are several strengths here:\n1. The core idea of using a flash/no-flash image pair as a prior is a interesting for image matting in dynamic scenes, addressing a key limitation of background-matting methods.\n2. The introduction of a novel, large-scale dataset is highly valuable for promoting future research in community.\n3. The experimental results seem to be good and convincing.\n\n\nThere are several weakness here:\n1. The overall pipeline is quite complex, involving a multi-step pre-processing stage for trimap prediction before the main two-stage matting network.\n2. The method's performance is potentially dependent on an external large pre-trained model (SAM), which might lead to extra params and inference time. I am not sure whether the proposed method could use other pretrained model to have similar performance.\n3. The quantitative comparison in Table 2 lacks a discussion of model complexity, such as parameter counts or FLOPs, making it difficult to evaluate the method's efficiency.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this paper, authors aim to propose a novel image matting method by revealing the foreground with flash priors, which could do not rely on the background image as reference. To this end, authors use a flash/no-flash image pair as a prior to highlight the foreground object. The proposed framework, FNFNet, is a two-stage cascaded network that uses a Transformer-based Foreground Correlation Module (FCM) to explore the relationship between the two differently illuminated images. To facilitate this research, authors also introduce the first large-scale flash/no-flash portrait matting dataset. In the experimental part, authors claim that their proposed method significantly outperforms other trimap-free matting methods in scenarios with dynamic backgrounds.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "There are several concerns expected to further demonstrate here:\n1. The trimap prediction relies on a pre-trained SAM to generate the segmentation mask. How dependent is the final performance of proposed method on the quality of this external model? It would be better to provide an ablation study using a different and simpler segmentation model to investigate this dependency.\n2. It would be better to include a comparison of model parameters and computational complexity (FLOPs, or inference time) in Table 2. This information is important for assessing the efficiency of FNFNet relative to the other methods, especially for potential real-time applications.\n3. The pre-processing pipeline for generating the flash-trimap involves several hyperparameters (e.g., filter r, number of points q). Could the authors investigate the sensitivity of the final performance to these hyperparameters?", "broader_impact_concerns": "There are not any further broader impact concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "uyqcubutyy", "reviewer": "Reviewer_9Rpe", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths:**\n\n(1) The authors propose a new \"Flash/No-flash\" image pair as a priori for foreground clipping, which cleverly avoids the limitations of the traditional \"background image\" method in dynamic backgrounds and camera movement scenes. It has practical application value and is especially suitable for actual scenarios such as video conferencing. \n\n(2) The authors design a two-stage network (FNFNet), including a foreground correlation module and a boundary matting module, which effectively combines Flash priors with semantic information to predict high-quality alpha mattes. In particular, the Transformer-based structure is used to model the foreground correspondence under different lighting conditions, effectively enhancing the adaptability to dynamic scenes. \n\n(3) The authors build a large-scale dataset containing 3025 high-quality annotated alpha mattes, covering a variety of backgrounds and character poses, filling the gap in public data, and with good statistical analysis and detailed description. \n\n**Weaknesses:**\n\n(1) The authors use Adobe Premiere to manually extract the foregrounds and alpha mattes, which seems to be a massive workload given the large volume of data. In addition, how the foreground portraits are blended with background videos of varying resolutions and frame rates to ensure that the resulting composite videos look visually coherent is also a concern. Improper settings may introduce a gap between the synthetic dataset and real-world data.\n\n(2) The algorithm proposed by authors relies heavily on the quality of Flash/No-flash image pairs, but in reality most devices do not support frequent use of flash during video shooting, which may be inconvenient to use in privacy-sensitive scenarios (such as meetings and live broadcasts). Therefore, the reliance on Flash devices may limit the actual deployment of the algorithm.\n\n(3) There appears to be a formatting error in the last citation on the second line of Section 2.2.\n\n(4) The proposed method performs slightly worse than BGM-V2 in static scenes. Are there any potential improvements (e.g., improving the current model structure) that could address this issue in the future? The authors are encouraged to add relevant discussion on this.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "To solve the problem of image matting with dynamic backgrounds, the authors explored a new approach to solve the image matting problem by leveraging flash prior knowledge to reveal the foreground. Specifically, the authors proposed a portrait matting model based on flash and non-flash images, which provides additional foreground prior knowledge to reveal foreground objects. The model is insensitive to dynamically changing backgrounds and does not require the foreground person to move exactly the same in both images. In addition, the authors constructed the first flash/no-flash portrait image matting dataset, which contains thousands of well-annotated alpha mattings. Finally, the authors verified the significant performance improvement of the proposed model in dynamic background scenes through experimental evaluation.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "**(1) Data synthesis and consistency issues.**\n\nAlthough the author manually annotates the foreground and alpha matte through Adobe Premiere, which is a method to ensure high-quality annotation, considering the large amount of data, this process is very cumbersome. It is recommended that the author add more descriptions on the annotation efficiency (such as whether semi-automatic auxiliary tools are used). In addition, for the process of synthesizing the foreground portrait with background videos of different resolutions and frame rates, the author should explain in detail its alignment strategy and visual consistency processing method to reduce the domain gap between the synthetic data and the real scene.\n\n**(2) Insufficient discussion on the practical feasibility of Flash/No-flash dependence.**\n\nThe model is highly dependent on Flash/No-flash image pairs, but in the process of real video shooting, especially in privacy-sensitive scenarios such as meetings and live broadcasts, it is not realistic to frequently use flash. It is recommended that the author add analysis and discussion of deployment scenarios in the paper, such as whether there are alternatives or how to make adaptive adjustments when Flash images are missing, so as to improve the practicality of the method.\n\n**(3) Paper formatting issues.**\n\nThe last reference in the second line of Section 2.2 seems to have a format error (\"?\"). Please check and correct the BibTeX or LaTeX reference format.\n\n**(4) Analysis of the problem of slightly weaker performance in static scenes and discussion of optimization possibilities.**\n\nAlthough the proposed method performs well in dynamic backgrounds, it is slightly inferior to BGM-V2 in static backgrounds. It is recommended that the author explore the reasons for this phenomenon and propose possible improvement directions, such as enhancing the segmentation accuracy of static scenes by fusing Flash and Background dual priors or designing a more fine-grained attention mechanism, thereby enhancing overall adaptability.", "broader_impact_concerns": "The authors propose a novel solution for dynamic scene portrait matting, leveraging flash/no-flash image pairs. The proposed FNFNet demonstrates strong technical contributions, including an effective Transformer-based correlation module and a carefully designed two-stage architecture. The authors also construct the first large-scale flash/no-flash matting dataset, which will benefit future research. Overall, the method shows good practical application prospects and achieves state-of-the-art performance, especially in challenging dynamic backgrounds.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)