LumosX: Relate Any Identities with Their Attributes for Personalized Video Generation
Authors: Jiazheng Xing
Fei Du
Hangjie Yuan
Pengwei Liu
Hongbin Xu
Hai Ci
Ruigang Niu
Weihua Chen
Fan Wang
Yong Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Video Generation
Video Customization
Diffusion Models
Multi-Subject Generation
Face-Attribute Alignment
Primary area: generative models
TL;DR: a framework that explicitly models face-attribute dependencies for multi-subject customization

Abstract: Recent advances in diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained control over both foreground and background elements. However, precise face–attribute alignment across subjects remains challenging, as existing methods lack explicit mechanisms to ensure intra-group consistency. Addressing this gap requires both explicit modeling strategies and face-attribute-aware data resources.  We therefore propose LumosX, a framework that advances both data and model design. On the data side, a tailored collection pipeline orchestrates captions and visual cues from independent videos, while multimodal large language models (MLLMs) infer and assign subject-specific dependencies. These extracted relational priors impose a finer-grained structure that amplifies the expressive control of personalized video generation and enables the construction of a comprehensive benchmark. On the modeling side, Relational Self-Attention and Relational Cross-Attention intertwine position-aware embeddings with refined attention dynamics to inscribe explicit subject–attribute dependencies, enforcing disciplined intra-group cohesion and amplifying the separation between distinct subject clusters. Comprehensive evaluations on our benchmark demonstrate that LumosX achieves state-of-the-art performance in fine-grained, identity-consistent, and semantically aligned personalized multi-subject video generation.

Published as a conference paper at ICLR 2026
LUMOSX: RELATE ANY IDENTITIES WITH THEIR AT- TRIBUTES FOR PERSONALIZED VIDEO GENERATION
Jiazheng Xing*l.44 1,422 Fei 2 Hangjie Yuan"2.3. Pengwei Liu Hongbin Xu²,
Hai Ci Ruigang 2iu Weihua Chen f2.3, Fan Wang?, Yong Liu
Zhejiang University, 9DMO Academy, Alibaba Group, Hupan Lab, "National University Singapore
Equal contributioo,, Corresponding authors.
j aaa.znngiingggju..ee. cn,
wwwg yorgliueiipe. 2j,. edu.
Project Page: http:://jieeheegggngggggggttttt
ABSTRACT
Recent advances diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained control over both foreground and background elements. However, precise aitt-btt align- ment across subjects remains challenging, existing methods lack explicit mech- anisms to ensure intra-group consistency. Addressing this gap requires both ex- plicit modeling strategies and face-attribute-awaan data resources. We therefore propose LumosX' a framework that advances both data and model design. On the data side, a tailored collection pipeline orchestrates captions and visual cues from independent videos, while multimodal large language models (MLLMs) in- fer and assign subject-specific dependencies. These extracted relational priors im- pose finer- grained structure that amplifies the expressive control of personalized video generation and enables the construction of comprehensive benchmark. On the modeling side, Relational Self-Attentin and Relational Cross-Attention inter- twine position-aware embeddings with refined attention dynamics inscribe ex- plicit subject-attribute dependencies, enforcing disciplined intra-group cohesion and amplifying the separation between distinct subject clusters. Comprchensive evaluations benchmark demonstrate that Lumos,' achieves tfate-of-the-ar performance in fine- treeggraidd, ddentity-conssteen and semantically aligned person alized multi-subject video generation.
INTRODUCTION
In recent years, diffusion models (Ho et al. 2020 Ho Salimans 2022
Rombach et al. 2022)
have driven remarkable progress, establishing
performance standards in text-to-video genera-
tion (Hong et al. 2022 Blattmann et al., 2023 Menapace et al. 2024; Zheng et al.] (024), par-
ticularly through the adoption of Diffusion Transformer (DiTT) architectures (Peebles & Xie (023)
These advances have laid a solid groundwork for customized video generation (Ma et al. 2024| Yuan
al. 2024 Zhong al.) 20225] Chen et al. 2025 Huang al. 2025| Fei et al. 2025 Liu et al.j personalization unlocks transformative applications ranging Trom virtual theatrical production to ecommeerce-enaliine fine-grained control over both back- grounds and foregrounds, including multiple interacting subjects. Yet, realizing open-set personal- ized multi-subject video generation under such flexible and complex conditions remains profoundly challenging. The task requires not only the precise integration of diverse and interrelated condition- ing signals but also the preservation of temporal coherence and identity fidelity across all subjects.
2025 where gegh-ddgrre-of-freee
In the realm of open-set personalized video generation, prior studies have pushed the field forward from distinct angles. Certain approaches (Ma et al. 2024 He et al. 20224] Yuan et al. 2024 Zhang et al. 2025b Zhong et al. 2025) concentrate narrowly on foreground facial customization, preserv- ing identity fidelity from reference images yet affording only limited flexibility in input specification. In contrast, more recent methods (Chen et al., 2025 Huang al. 2025 Fei et al. 20225) Liu et al. 2025) enable highly versatile multi-subject video personalization with controllable foregrounds and backgrounds, but they largely neglect the intrinsic dependency structures that govern multi-subject

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man the left
the right
Tae men are seated next to each ather in a cosus indeor setting, captured from on eye level comero angle. man on left has long, enrry hor and beand, woriing grean button-up strrt man on right wecring hahirt black bosebal oxp, Between them are two green glass bottles, placed on table Bahind them fromed painting with contract design hanger lightcoboeed wall
LamosX
green tent
block shirt
shirt
black with graying
groy shirt anr
close each other under
conversot engaged
Lumos
Figure I: Lumos supports flexible personalized multi-subject video generation.
conditions. Crucially, during fine-grained multi-condition injection, conditioning signals for each subject are typically decomposed into facial exemplars and attribute descriptors (e.g., man: blond hair, white T-shirt, sunglasses). Absent an explicit mechanism to bind identity with its associated attributes, such formulations are inherently fragile and frequently yield attribute entanglement or attribatti misalignment across subjects.
Although implicit modeling via textual captions can capture simple multi-subject dependencies dur- ing video generation, ambiguity often arises when captions contain similar subject nouns, such as "A man on the left with... ... and man on the right with ..., leading to confusion in subject-attribute as- sociations. To overcome this limitation, under fine- fine-grained multi-subject inputs, explicit constraints must be imposed at both the data and model levels. (1) Data level: When visual references are pro- vided, the correspondence between each face and its associated attributes should be clearly specified. (2) Model level: During generation, each face- attribute pair is explicitly bound into an independent subject group, with group correlation enhanced and inter-group interference suppressed.
To address the challenge of modeling face-attribute dependencies in multi-subject video genera- tion, present Lumos,X, a novel framework for personalized multi-subject synthesis. On the data side, the absence of public datasets with annotated dependency structures motivates us to construct a collection pipeline that supports open-set entities. This pipeline extracts captions and breeground-backgrousg visual conditions from independent videos, while multimodal large lan- guage models (MLLMs) infer and assign subject-specific dependencies. In partieular, it produces customized single- and multi-subject data with explicit face- attribute correspondences. which not only enhance personalization during modeling but also enable the construction of comprehensive benchmark. On this basis, the benchmark further defines two evaluation tasks, identity-consistent and subject-consistent generation, which allow a systematic assessment of a model's ability to preserve identity and align multi-subject relationships. On the modeling side, Lumosk' explic- itly encodes face-attribute bindings into coherent subject groups through two dedicated modules: Relational Self-Attention and Relational Cross-Attention. The Relational Self-Attention mod- ule incorporates Relational Rotary Position Embedding (R2PE) and Causal Self-Attention Mask (CSAM) to model dependencies at the positional encoding and apatio-tempont self-attention stages. In addition, the Relational Cross-Attention module introduces a Multilevel Cross-Attention Mask (MCAM), which reinforces intra-group coherence, suppresses cross-group interference, and refines the semantic representation of visual condition tokens. Lumos is built upon the Wan2.1 (Wang) al. 2025) text-to-video backbone, with our modules seamlessly integrated and high-fidelity personalized multi-subject generation, as shown in Fig. D Extensive experiments demonstrate Lumos-X XX's strong capability in producing fine- -nniiiidddd, dentity-connistery and seman- tically aligned personalized videos, achieving state-of-ther -th--art results across diverse benchmarks.
support flexible
The contributions of our Lumos?' can be summarized as follows:
Data Side. We build a collection pipeline for open-set multi-subject generation that extracts
captions and mneground-backgrouun condition images with explicit aitribut depen-
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dencies from independent videos. This yields finer- finer-graine grained relational priors that enhance personalized video customization and enable the construction of reliable benchmarks.
Model Side. We introduce Relational Self-Attentio and Relational Cross-Attention, which integrate relational positional encodings with structured attention masks to explic- itly encode face-attribute bindings. This reinforces intra-group coherence, mitigates cross- group interference, and ensures semantically consistent multi-subject video generation.
Overall Performance. Through extensive experiments and comparative evaluations, Lumos,' achieves state-of- thr- results in generating fine-grained, identity-consisten and semantically aligned personalized multi-subject videos, decisively outperforming ad- vanced open-source approaches including Phantom and SkyReels-A2.
RELATED WORKS
Video Generation. Video generation has advanced rapidly in recent years, becoming one of the most dynamic research areas. Early works based on generative adversarial networks (GANs) (Von- drick et al., 2016 Tulyakov et al. 2018) demonstrated initial video synthesis but struggled with temporal coherence and fidelity. Latent diffusion models (LDMs) (Rombach et al., 2022). powered by UNet (Ronneherger et al. 2015), marked significant milestone by enabling high-quality video generation through denoising in compressed latent spaces. These works typically add a temporal module to an image generation model, such as Make-A- Video (Singer et al. (202)) and Animated- iff (Guo et al.) 2023). However, these models often face scalability challenges when scaling to larger parameter sizes or higher resolutions. Diffusion Transformers (DiTs) (Peebles & Xie 2023). replac- ing the Unet backbone with Transformer blocks, have shown superior performance visual gener- ation. By incorporating spatio-temporal attention mechanisms, video DiTs achieved unprecedented performance in modeling long-range dependencies across both spatial and temporal dimensions, sig- nificantly enhancing video realism and consistency, Models like Hunyuan Video (Kong et al., 2024)) 1 (Wang al. 2025), and MAGI-1 (Sand-Al 2025) have scaled the parameters of video DiTs to more than 10 billion. achieving significant ad adnacement. Despite these advances, controllability remains critical bottleneck: text-driven generation often fails to precisely align with user inten- tions due to ambiguities in natural language descriptions. This work focuses on multi-subject video customization address this limitation, enabling precise content-control video generation.
Multi-Subject Video Customization. In recent years, subject-driven video generation has attracted growing interest. Several works focus on ID-consistent video generation, such as Magic-Me (Mal et al. 2024). ID-Animator (He et al. 2024), ConsislI) (Yuan et al. 2024), Magic Mirror (Zhang ctt al., 2025a), FantasyID (Zhang et al. (025b), and Concat-ID (Zhong et al. 2025). These works generate videos that show consistent identity with the reference images, mainly focusing facial identity. For arbitrary subject customization, VideoBooth (Jiang et al. 2024) incorporates high-level and fine-level visual cues from an image prompt to the video generation model via cross attention and cross-frame attention. Dream Video (Wei et al. 2024) customizes both subject and motion, with motion extracted from a reference video. Although they have demonstrated capabilities in generating single-subject consistent videos, neither the data processing nor the model can be easily transferred the more challenging multi-subject customization. Custom (Wang al., (202c) generates multi-subject adentity-preserving videos by composing multiple subjects in single image and de- signs an attention control strategy to disentangle them. However, it requires test-time finetuning for different subjects. Recently, several works (Chen et al., [025 Huang et al. 2025 Fei et al.. 2022) Liu et al. 2025) propose to customize multiple subjects in the video DiTs. Different subjects are usually concatenated and fed into the video DiT network without distinguishing between them. This lack of differentiation can lead to semantic ambiguity, especially when there are numerous targets and hierarchical relationships among them. In this work, we design several strategies to differenti- ate between various subjects and their hierarchical relationships. achieving consistent customization while ensuring harmony among different objectives and the text's adherence capabilities.
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Step
Step
Step
Subjeeti
Original Video
Background
Video
Objects
garden
Jace
Caption Generation
Detection
Rausdom Sampplnng
Cropping
Subject
-SAM
-AM
Mashinng +ngaainting
A mon and woman are sested of table lush gandes, sharing a mesl The mon, waaniing o black purt and a black watch on his left wist, gestures with his hands while speaking. while the woman, in white ttp. listers attentively and occosionally inspondss Utenails one noatry on the tobies as they enjoy their meal together...
luse 6d6
Subjects
black
Objects
Background
Fatity Words Retrieval & Subject-AAteilee Matching
garden
Word Tage
Figure 2: Dataset construction pipeline for personalized multi-subject video generation. We build the training dataset from raw videos three steps: (1) generate cantion and detect human subjects in extracted frames; (2) retrieve entity words from the caption and match subjects their attributes; (3) use these entity tags to localize and segment target subjects and objects. producing a clean background image.
METHODS
3.1 PRELIMINARY
In this work, we bulid upon the latest text-to-video generative model, Wan2./ (Wang et al. 2025), which comprises the 3D variational autoencoder (VAE) E, the text encoder T, and the denoising DiT (Peebles & Xie 2023) backbone 6g combined with Flow Matching (Lipman et al.) 2022) Within the DiT architecture, full spatio-temporal Self-Attention is used capture complex dynam- ies, while Cross-Attention is employed to incorporate text conditions. Specifically, given video TT' along the
X ) with N frames, E compresses into latent representation spatiotemporal dimensions, where T', HW, and C' denote the temporal, spatial, and channel dimen- sions, respectively. The text encoder takes the text prompt and encodes into textual embedding Sext The denoising DIT co then processes the latent representation and the textual representation
Cext to predict the distribution of video content. Each DiT Block incorporates 3D Rotary Position Embedding (3D-RoPE (Su et al. 2024h) within the full spatio-temporal attention module to better capture both temporal and spatial dependencies.
3.2 DATASET CONSTRUCTION
As illustrated in Fig- D our training dataset and inference benchmark for personalized multi-subject video generation are constructed from raw videos through the following three steps.
Caption Generation and Human Detection. To obtain richer textual descriptions for down- stream tasks, replace the original video captions with captions generated by the large vi- sion-language model VILA (Lin et al. 2024). We sample three frames from the beginning, middle, and end of each video (5%, 50%, and 95% positions) and apply human detection (Wang et al. 2024a) to extract human subjects for subsequent face-attribute matching.
Entity Words Retrieval and Face-Attribute Matching. In this step, our goal is to retrieve entity words from the caption, which can be classified into three categories: human subjects with attributes (e.g., man: black shirt, black watch), objects (e.g., utensils), and background (e.g., lush garden). During this process, if multiple human subjects are present, we need to assign different attributes to the corresponding subjects. In particular, when the caption contains multiple instances of the same subject noun (e.g.. woman), we rely on visual information to assist distinguishing between them. Therefore, employ the multimodal large language model Qwen2.5-VI. (Bai et al. 2025)
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Trainable
Caption
In cozy kitchen setting, woman yellow scort mixing ingredients a lorge blue bowl. while her attentively. black hoodie watches visible counter...
Waits
measuring cup
3D VAR Decuder
Video Tokeus
euvv evio
0,0)
(T-4
0.0
DIT Block
(M/)) (1,5))
(T-L
1,89
(T-L
1.9
(TA.))
Test Encoder
Background Tokens
Feed Forward
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(T+s,
Subject A Subject
Objects
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0,83
9,9
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Background
Relational Rotary Position Embedding (RaPE))
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2,23
Tolkens
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(1+33
C++3.
(143.
1/0
3,29
3,3)
3D VAE Encoder
*
(T+4,
(T+44
(T+4,
(T14.
Subject B
2.2
Video
Tokens
(T+4. 1,00
(F-4)
(1+44
1.9
3.2
3.3)
Overview of Lumos Built on the T2V model Wan2.1 (Wang et al. 2025), our framework en- codes all condition images into image tokens via a VAE encoder, concatenates them with denoising video tokens, and feeds the result into DiT (Peebles & Xie 2023) blocks. Within each block, the proposed Relational Self-Attention and Relational Cross-Attention enable causal conditional modeling, enhance visual token repre- sentations. ensure precise face-attribute alignment.
Figure 3:
to retrieve multiple entity words from the caption, while leveraging prior visual information from human detection results to achieve precise face-attribute matching.
Obtaining Condition Images. For subjects, apply face detection (Wang al. 2024a) within human detection boxes to extract face crops and use SAM (Kirillov et al.) 2023) to segment at- tribute masks. For objects, GroundingDINO (Liu al., (2024) combined with SAM segments each entity within the global image. For backgrounds, we remove subjects and objects using the crops and masks, then apply the diffusion inpainting model FLUX (Labs) 2023) generate a clean back- ground. Finally, from the valid results of the three key frames, we randomly select one per entity as its condition soage-matching the inference process, where each condition uses single reference while ensuring data diversity by preventing all selections from single frame.
Through these three steps, we obtain the visual condition images for the subjects, objects, and back- ground, along with their paired word tags derived from the input text caption. Note that subject is defined as single human face paired with its corresponding attributes. The face expected to present clear facial features without significant occlusion, and the associated attributes can include clothing (top or bottom), accessories (e.g., glasses, earrings, or necklaces), or hairstyle.
3.3 LUMOSA
As shown in Fig. 3 our framework builds on the T2V model Wan2./ (Wang et al.) 2025). To enable personalized multi-subject video generation, all condition images are encoded
image tokens via a VAE encoder, concatenated with denoising video tokens, and fed into DiT (Peebles & Xie 2023] blocks. Within each block, we introduce Relational Self-Attention with Relational Rotary Position Embedding (R2PE) and a Causal Self-Attention Mask to support spatio-temporal and causal conditional modeling. Additionally, Relational Ccross-Attention with Multilevel Cross-Attention Mask (MCAM) incorporates textual conditions, strengthens visual token representations, and aligns attribbt relationships.
3.3.1 RELATIONAL SELF-ATTENTION
Relational Rotary Position Embedding (R2PE). In T2V models like Wan2.1 (Wang et al.) 2025). utilizing 3D) Rotary Position Embedding (3D-RoPE) to assign position indices (f,j, k) to video
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tokens is necessary, which affect the interaction among these tokens. In T2V tasks, the original
3D-RoPE assigns position indices (2. j. k)) sequentially to the video tokens z E RT** XC, where i E [), E [0, W), and & E [0. H). In personalized multi--ubbject video generation tasks, it is essential to not only extend 3I>-RoPE to the reference condition images but also to preserve the face-attribute dependency throughout this process.
Given the concatenated VAE tokens z' [ Ze] E -.. where Ze E RRN.XHWxx represents the condition tokens, we introduce the Relational Rotary Position Embedding (R2PE), as illustrated in Fig. 3 The condition tokens Ze are composed of subject tokens zeub. object tokens Zobj. and background tokens Abg. i.e., 2e [2sus; Zubj; 2bg]. ) In R2PF, for the video tokens 2, we adopt the standard 3D-RoPE (i, k) position assignment method, while for the background 2by and object Body tokens, we sequentially extend each entity along the i-index. For the subject tokens 2zab- which are composed of human face tokens face and human attribute tokens Zuttr, we strictly adhere to the face-attribute dependency when assigning position indices to the subject tokens. Therefore, for the human face tokens and their corresponding attribute tokens within the same group, they share the same i-index and are extended along the j j-index and k--index Specifically, the position index for the condition tokens Ze. is defined as:
{ (ibg/ob) obj + kT),k) (L, { (Toub + T + Nbg/obj. + W * No lous" k
when Bbg and Zobj N when Baub
where obg/ E. [0, (0.Nooo)))) with N Nog/obj denoting the total number of background and object entity. igub E 10, Nsub) where Nous represents the total number of face-attribute subject groups. And, N [0, No..)... where Ni... denotes the total number of face and attribute entity within the ioub subject group. The proposed R2PE effectively inherits and extends the implicit positional correspondence of the original Wan2. model, while preserving the ance-bttribute dependency within each group of the subject condition.
obj
Mask (CSAM). The Causal Self-Attention Mask is a boolean matrix, as illustrated in Fig. (a), with the following two rules governing its mechanism: (I) Calculations are performed within each conditional branch, where the human face and its corresponding attributes are treated unified subject condition branch; (II) Video denoising tokens apply unidirectional attention to the condition tokens only. Given the concatenated tokens z' E R(T+N.xxHXx. the mask can be formulated as:
Causal
True if q z or q =- k or q, k E sub
M q.k
(2)
False, otherwise
where q and k denote the categories of the tokens corresponding to the query-key matrix in Self- Attention, both of which belong to the visual concatenated tokens z'. z and z represent the denoising video tokens and the face/attribute tokens within the same subject group. This causal mask enforces constraints on the range of interactions during the Self-Attention process. This design efficiently prevents unidirectional attention from the conditional branch to the denoising branch, while enabling the denoising branch to independently aggregate conditional signals and efficiently bind the face-attribute dependencies within the conditional branch. To enable efficient computation, we employ the MagiAttention mechanism proposed in (Sand-Al 2025).
3.3.2 RELATIONAL. CROSS- ATTENTION
Multilevel Cross-Attention Mask (MCAM). In the Crtss-Attention process of the T2V task, all visual tokens interact with all textual tokens. However, the requirements may differ for customized video generation tasks. Intuitively, all textual tokens are of equal importance for video denoising tokens. However, for visual condition tokens customized tasks, each has a corresponding tex- tual token, such as: face image + "man". Therefore, we aim enhance the interaction of visual condition tokens with the corresponding textual tokens in the cross-attention process to improve the semantic representation of visual tokens. Furthermore, for subject condition tokens, we seek to strengthen the ftce-attribute dependency within the same subject group in the Cross-Attention process, while reducing the mutual influence between different subject groups.
Based on the aforementioned motivation, we propose the Multilevel Cross-Attention Mask (MCAM), as shown in Fig. (b). MCAM is numerical mask in which we have defined three

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True
Palse
Textual Tolkrn
Face Human Face
Toketto
yclon soart
young semon
wotsl
mucos ong cup
Subjear A
(nemaa) Aitr Atrriuun (ysllow scorf)
setting
Video
Part
Face
Ale 31
Video Naioe Quar
Other
Tokens
Face B: Hemmn Pace (young women) Air It: Arreihun is (block hoodle)
Subject
Vi Tokeea
Video
Obj A: Objjctt A (small metal measuring
Para
Objects
Ohl Ohject Is
Fare
Plae 11
Carrelation
Strong
Curretation
Bg: Background (ceay kitchan setting)
Olij B
Ong
Week: Correlation
(a) Self-Atteation Mask (CSAM)
(b) Multilevel Croes Croes-Attention Mask (MCAM)
Figure 4: Illustration of Attention Mask Design: Causal Sell-Attention Mask (CSAM) and Multilevel Cross- Attention Mask (MCAM). We present a specific customized task case as an example.
levels of correlation: Strong Correlation (I), Correlation (0), and Weak Correlation (-1). Specif- ically, Strong Correlation applies to the interaction between the visual condition tokens and their corresponding textual tokens, as well as between visual subject (face & attribute) condition tokens and all textual tokens within the same subject group. Weak Correlation applies to the interaction between visual subject tokens and the textual tokens from different subject groups. And all other cases remain as Correlation. Therefore, this mask M can be formulated as:
1 (Strong Correlation), if q, k belong the same semantic entity subject group (Weak Correlation), if q. belong to the different subject group otherwise
MCA- q.k
lo (Correlation),
(3)
where q and k denote the categories of the tokens corresponding the query-key matrix in Cross- Attention, with the query and key representing the visual and textual tokens, respectively, in this context. Subsequently, inject this constraint mask MCA into the Cross-Attentin as follows:
QK
MCA q.k
Cross-Attention( (Qr Q. K. V) Softmax
V
(4)
vdk
where Q denotes concatenated visual features, and K and V are textual features. The hyperparame- ter controls the strength the MCA constraint. Because similarity scores between query and key tokens vary across positions, uniform mask template cannot be applied directly. To address this, we introduce a dynamic scaling factor to adjust M at each position. The most straightforward strategy to use the absolute value of the similarity matrix itself as s. However, existing acceler- ated Attention computation modules based on Pytorch do not support customized numerical masks like this, and recomputing the similarity scores between Q and K outside the Attention module would incur significant computational overhead. To balance accuracy and efficiency, we propose an approximate method to compute the similarity matrix and derive s outside the Attention module as:
Repeat QQ..k shape (QKT))
(5)
where Qds denotes Q downsampled by factor of via local average pooling on its spatial dimensions. The Repeat( shape(-)) operation restores the downsampled similarity matrix to its original size. Overall, the proposed MACM effectively strengthens relational dependency consis- tency and enhances the semantic-level representation of visual condition tokens.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. Our personalized multi-subject video generation training dataset is built Panda70M (Chnn et al. 2024). After the cleaning and processing steps described in Sec. 3.2 we obtain 1.57M samples: 1. 1.31M single-subject, 0.23M two-subject, and 0.03M three-subject videos.

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Benchmark. For the testing benchmark, 500 videos crawled from YouTube processed us- ing the method described in Sec. 3.2 including 220 single-subject, 230 two-subject, and 50 three- subject videos. To rigorously assess personalized multi-subjeet video generation, establish two tasks in this benchmark: identity-consister consistent video generation, to 3 facial reference images are provided, with the facial similarity between the reference images and generated videos assessed using FaceSim-Are (AreSim), based on ArcFace (Deng et al. 2019), and FaceSim-Cur (CurSim), based on CurricularFace (Huang al.) 2020), following (Zhong et al. 2025) and (Yuan et al., 2024). We also employ VideoCLIPXI (ViCLIP-T) (Wang et al., 2024b) to measure semantic similarity between generated videos and text prompts. For subject-consistens video generation, the model takes multiple reference images, in- cluding faces, attributes, objects, and the background, as input. To comprehensively evaluate this task, we consider two aspects: 1) Evaluation of the entire video, where we use ViCI.IP-T (Wang) ict al., 2024b) and ViCLIP-V (Wang et al.) 2024b) to assess semantic similarity between the gener- ated videos and text prompts, well as between the generated videos and the ground-truth videos, respectively. Additionally, to prevent the generated videos from exhibiting copy-paste artifacts, we assess their dynamic degree, following (Huang et al., 2024). 2) Evaluation on subjects, where first use Florence-2 (Xiao et al. 2024) to detect the person subjects based on the text prompts, and then apply OWLv2 (Minderer et al., 2023) detect the bounding boxes of the person's 's attributes and other objects. Once all subjects are located, we compute CLIP-T (Radford et al. 2021) between the cropped image regions and the corresponding text prompts, as well as DINO-I (Oquab et al.) 2023) and CLIP-I (Radford et al. 202I) between these regions and the corresponding reference im- ages. AreSim (Deng et al. 2019) is also used here to assess the identity similarities. If subject not detected in the generated video, the score is set to zero
and subject-consistent video generation. For identity-
Implementation Details. Lumos is fine-tuned from Wan2. T2V(1.3B) (Wang et al. 2025) model, built on the DiT (Peebles & Xie (023) architecture. Video generation is performed at 480p resolution, with each training clip containing 81 frames (5 seconds at 16 FPS). In MACM, the downsampling factor d and hyperparameter are set to 8 and 0.5, respectively. During training, each subject's face is associated with up to three attributes. Therefore, we recommend providing no more than three attributes per subject group during inference to maintain consistency with the training setup. Lumos,Y is trained in two phases: 15k iterations on single-subject data, followed by 16k iterations on mixed multi-subject data. Training uses the Adam (Kingmal 2014) optimizer with a learning rate of 1e-5, EMA decay of 0.99, weight decay of le-4, gradient clipping at 1.0, a batch size of 64, and random text-conditioning dropout 10%. Our full training process required approximately 883 GPU-days on H20 GPUs. During inference, we use 50 steps and set the CFG scale to 6.
4.2 MAIN RESULTS
In the following experiments, all bascline configurations are fixed when compared against our Lumos (Wan2. I-1.3)), including ConsisID (CogVideoX-5B) (Yuan et al. 2024). Concat-II (Wan2.1-1.33) (Zhong al. 2025). ) SkyReels-A2 (Wan2.1-143) (Fei et al. 2025), , and Phantom (-an2.1-1.3B) (Liu et al. 2025). For the Identity-Consistet Video Generation setting, all methods use the same inputs: each subject's face image and shared global text prompt. For the Subject- Consistent Video Generation setting, we also enforce strict input parity. All methods receive exactly the same inputs as LumosX' including each subject's face image, all associated attribute images, object reference images, the background image, and the shared global prompt.
Identity-consister video generation. In this experiment, we use only face reference images as input to evaluate identity preservation with AreSim and CurSim. We first compare our Lumos,X with face-specific customization methods, ConsislI (Yuan et al. 2024) and Concat-ID (Zhong et al., 2025). on a single-face test set of 220 videos, as ConsislD supports only single-face customiza- tion and Concat-ID has only released weights for the single-face setting, with results presented in Tab. [ Additionally, we compare Lumos.L' with SkyReels-A2 (Fei et al., 2025) and Phantom (Liu et al., 2025), two general multi-subject video customization methods, on the full test set of 500 videos, as shown in Tab. 2 Across both settings, Y achieves SOTA identity similarity scores, demonstrating its strong ability to preserve identity consistency in video generation. The qualitative comparison shown in Fig. (a) further highlights the advanced performance of our Lumos.
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Table I: Comparison of different methods for single-
Table 2: Comparison of different methods for identity- consistent video generation.
face identity-coneee consistent video generation.
Methods
Ideecit Comistency
Prompt Following
Methuds
Ideecity Consistreyy
Prompt Following
ArcSim
CunSim
VICLIP-T
ArcSim
CurSim 1
VICLIP-1
ConsistI
Yoar
2025 22ga
0.458
0,474
0.363
SkyReele A2 Pei
2025
0.382
0.401
0.261
Concur-I1
2015
0.467
0.485
0:261
Phantom (La
0. 0.508
0.536
0.264
Lumos'
0.542
0,575
0.262
Lumos C
0.510
0,540
0.262
Table 3: Comparison of different methods for subject-consisent video generation, including evaluation of the entire video and evaluation on the extracted subjects.
Eatire Video
Extracted Subjects
Methods
Dynamic
VICLIP-T 1
VICLI-VVT
CLIP-T 1
CLIP-1
DINO-1
AreSim
CurSim
SkyReels-A22 (Fei et
2025
0.671
0,251
0.839
0.178
0.606
0.192
0.271
0.290
Phantom (Liu et al 2025 Lumos.X
0.661
0.254
0.865
0.185
0.647
0,216
0.444
0.477
0.723
0.260
0,932
0.201
0.692
0.261
0.454
0.483
Subject-consistent video generation. This experiment focuses on comprehensive multi-subject video customization, with reference inputs including faces, attributes, objects, and background im- ages. For the evaluation of the entire video, we adopt ViCLIP-T and ViCLIP-V to assess the seman- tic similarity, while Dynamics detects potential copy-paste artifacts in the motion consistency. For evaluation on subjects, CLIP-T, CLIP-I, and DINO-I measure the semantic similarity, while AreSim and CurSim evaluate facial similarity within the extracted subject regions, collectively capturing the accuracy of face-attribute associations in multi-subject video generation. As shown in Tab. our method achieves SOTA performance in both the entire video generation quality and the accu- racy of face-attribute associations within the subject regions, outperforming the advanced models SkyReels-A2 (Fei et al., 2025) and Phantom (Liu et al. (025). thus demonstrating the robustness of our LumosX'. For qualitative comparison, Fig. (()) shows that method supports flexible multi-subject foreground and background customization while maintaining accurate face-au atttribbee matching across multiple subjects. In contrast, competing methods exhibit incorrect aattibbte pairings at a relatively high frequency, such as the mismatches visible in the lower-left SkyReels-A2 and Phantom results and the lower-right SkyReels-A2 result in Fig. (b, which further underscores the robustness of our approach.
4.3 ABLATION STUDY
In this paper, we focus on enabling flexible Eneerooudd-acckgroung customization while addressing the dependency of face-attribute across multiple subjects. Therefore, we conduct the ablation study of our LumosX on subject-consistent video generation. We conduct experiments under a relatively lightweight setting, where the training dataset consists of 300K video samples while maintaining the same subject distribution as the full dataset, and the video generation resolution is set to 240p.
We ablate each component in Lumos, with the results presented in Tab. 4 The results show that R2PE (Row 2) signif- icantly improves AreSim, thanks to the binding of faces and attributes during the positional encoding, which helps the model avoid face confusion in genera- tion. But CLIP-T shows a slight decrease, which believe may be due the shared T-idx within the subject group, affecting the semantic representation of individual entities. Furthermore, our CSAM (Row 3) shows an im- provement over CLIP-T, as it effectively blocks the interaction between different conditional signals and allows the denoising branch to independently aggregate these signals. For MCAM (Rows 4-6), we evaluated performance under different values of the hyperparameter r. The results indicate that MCAM yields substantial improvements in both CLIP-T and AreSim, as it not only enhances the semantic representation of each visual condition but also effectively optimizes intra-group and inter- group correlations within subject groups AreSim achieves its best performance at r = 0.5 (Row 5), while CLIP-T peaks at r 1.0 (Row 6). Since the improvement in AreSim is more signifi- cant at r = 0.5, and considering that AreSim better reflects the accuracy of face-attribute affiliation matching, we ultimately choose r 0.5 in Lumos.X'
Table 4: Ablation study for the suhject-consistent video gen-
eration. The value in the MACM parentheses is r.
Methods
CLIP-T
ArcSim
None
0.184
0.316
+R2PE
0.178
0.363
+R2PH CSAM
0.182
0.363
+R2PE+ CSAM MCAM (0.1)
0.182
0.364
#R2PE CSAM MCAM (0.5)
0.186
0.429
+R2PE CSAM MCAM (1.0)
0.187
0.384

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Published as a conference paper at ICLR 2026
Reference Image
Caption
Reference Images
Captiin
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Cincal-In
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Images
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Figure 5: Qualitative comparison tdentity-consistent and subject-comsistent video generation.
5 CONCLUSION
This paper proposes LumosæX' a novel framework designed for personalized multi-subject video generation that explicitly models face-atrribute dependencies. To solve the lack of annotated data tailored for multi-subject generation, develop data collection pipeline that supports open-set entities with subject-specific dependencies. Built upon Wan2.1's T2V model, Lumos& introduces Relational Self-Attention and Relational Cross-Attention, which incorporate position embedding and attention mechanisms to explicitly bind face-attribute pairs into coherent subject groups and optimize both intra-group and inter-group correlations. Extensive experiments validate the effec- tiveness of Lumos in generating fine-grained and personalized multi-subject videos, achieving Matt-off-the-ar performance across diverse benchmarks.
ACKNOWLEDGMENTS
This work was supported by the State Key Laboratory of Industrial Control Technology, China (Grant No. ICT2024A09).
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ETHICS STATEMENT
Our study and dataset construction conform to established ethical standards, with no direct involve- ment of human subjects and foreseeable risk of harm. Data usage complies with privacy and legal requirements, and we have aimed to mitigate potential biases in annotations and model evaluation. We disclose no conflicts of interest or sponsorship that could influence the results.
REPRODUCIBILIIT STATEMENT
We have thoroughly described all proposed models and algorithms, experimental configurations, and the benchmarks used in our experiments in the main text and the appendix. Furthermore, we confirm that the code for this work will be released upon acceptance.
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Appendix of LumosX
In this Appendix, provide additional content organized follows:
Sec. A details the data collection pipeline and training dataset, including:
Sec.
A.1
Entity words retrieval and subject-attribute matching.
Sec.
A.2
Obtaining condition images.
Sec.
A.3
Data cleaning and filtering for training dataset.
Sec.
A.4
Robustness analysis of external modules the data collection pipeline.
Sec.
B
provides additional details LumosX', including:
Sec.
B.1
Training objective.
Sec. B.2
Data augmentation details during training.
Sec. 833
Generalization of Lumos, to diverse T2V architectures.
Sec. presents extended ablation studies and evaluations, including:
Sec. C.I
Fine-grained quantitative comparisons of identity-consistent video genera-
tion.
Sec.
Fine-grained quantitative comparison of subject-consistett video generation.
Sec. C.3
Additional quantitative results from various online sources.
Sec.
C.4
Discussion Lumos ' capability for customized control for 4+ subjects.
Sec.
C.5
Quantitative assessment of temporal coherence among different methods.
Sec.
C.6
Evaluation on public personalization benchmark.
Sec.
C.7
Visual effectiveness of individual components Lumos.X.
Sec.
C.8
Qualitative comparison under varying hyperparameters in MCAM.
Sec.
C.9
Quantitative analysis of text-based attribute control.
Sec.
C.10
Quantitative comparison with sange-personalization based initialization.
Sec.
C.11
Discussion of the importance of the inpainting model in data collection
pipeline.
Sec.
C.12
Human study for multi-subject video customization.
Sec.
Analysis of computational overhead and latency in Lumosæ'
Sec. D provides more visualization results, including:
-Scc.
D.I
Additional results identity-consistentt video generation.
Sec.
D.2
Additional results of subject-consistent video generation.
Sec.
E
discusses the limitations and future work.
Sec.
provides the LLM usage statement.
Sec.
addresses the broader impact of our approach.
A DETAILS OF DATA COLLECTION PIPELINE AND TRAINING DATASET
A. ENTITY WORDS RETRIEVAL AND SUBJECT-ATTRIUUTES MATCHING
In Sec. 3.2 of the manuscript, we propose utilizing Dwen2.5-VLL-32. (Bai et al. 2025 to retrieve multiple entity words from the caption, while leveraging prior visual information from human detec- tion results achieve precise face-attribute matching. The process of processing human-detecting frame with the corresponding caption can divided into four steps, as shown in Fig. The result of Data Organization (step 2) serves the input to Qwen2.5-VV VL and includes: captions (text), bound- ing boxes (text), and frames with colored bounding boxes (visual). During the Data Processing (third step) phase, design tailored prompt for Qwen2.5-VL. shown below. The results of Data Processing, as shown in the fourth column ("Data Annotation") of Fig. involve extracting key entity words from the captions and categorizing them into Subjects, Objects, and Background. For Subjects, we obtain human-attribute dependency relations, which strictly follow the matching structure presented in the duman-det frame.
better address the task requirements, as
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Collection
Organization
Processing
Caption
are seafed fable sharing medi The black shirt and black watch while spesking, while the white listers ottentively and occasionally responds. Utensils nently the tobles they their meal together...
Caption: "A zan lush garden,
his left
with hands
Owen2. 2-VL
Subjects
You information
mon: shirt, block watch
11"bboxx*
Analyze captioon. frame (with bozen), and bhozes data.
extraction
Objects
womon: white top
utenalls
"parssttncee"
bouneding
Background
corange
Figure 6: Process entity words retrieval and subject-attributes matching with four steps: Data Collection, Data Organization, Data Processing, and Data Annotation.
Prompt design for entity words retrieval and subject-atrribute matching Qwen2.5-VL
PROMPT
You are an information extraction assistant. Analyza the given vidao captior, frame
colored bounding boxes), bounding
Rules:
For PERSONS:
by the caption and bboxes data, 1ist ALL human related Identify how many httpa///aa/////////aa/// http://wwm//////////o// girl/boy etc.). Merge the same person to avoid
terms houmen/person/ant redundancy.
Extract DNTY concrete actributes for each person:
[ок]
THREE nost frequent conerete attribuses.
[X] The attibutes can be clothing descriptions (a.g., "black suis", *red dreas", glasses", *ast"] or physical. features (e.g., "blonde hair", "beard") [X: REJECT abstract at:ribures (e.g., "skin tone", emo-ions/actions like "smiling" or "running")
[X. REJECT additional descriptions of the attributes (e.g., red hoodie with black design on the front -> red hoodle)
[x REJECT long phrases: keep under words per
Crucially, use the Frame and BRoxes input: Vistally ly match each person described in the Caption to the person inside the oczresponding colored bbox Ired, qreen, blue) depicted in the Frame. The BBoxes variable maps color (e.g., *zed"! to bbox identifiers (e.g., "bboxl"). Assign the correct bbox identifier to the matched prevent miamstches.
For SUBJECTS:
[OR] Pick maximum THREE most trequent concrete objects
(OK] Use exact objes. names (e.g.r "dug", Yosk tree", "wodden chair")
:x] REJECT objects that cannet be directly described with specific nouns le.g., small rectangular object]
REJECT worda may vefer
tiple objects (e.g., "leaves")
XX] REJECT long phrases: keep under words pe: attribute
3. For BACKGROUND:
Nouns indicating the overall environment or backdrep of the video
[ok] Extract the environmental phrase (e.g., "park", *coffee shop")
XX] REJECT infarred locations
4. HOTES:
The extracted phrases must ssrictly come
caption, without adding, removing,
or rewriting words and panctuation marks.
Ensure bbox references correspond to the correct colored bbox from the input. Bbox coordinate format: (xmin, ymin, ymax].
A.2 OBTAINING CONDITION IMAGES
The detailed process for obtaining condition images, described in Sec. 3.2
illustrated in Fig. For
subjects, first apply face detection (Wang al. 2024a) within the bounding boxes obtained from human detection to extract facial crops. Then, guided by the extracted word tags from Qwen2.5- SAM (Kirillov et al. 2023) segment the corresponding attribute
VI. (Bai al. 2025),
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Subjects
Background
Objects
lush gardien
Random Sampling
Subject Masking Inpsinling
Grosnding(NNO SAM
Cropping
Subjects
Objects
Background
men: black shirt, black watch
utonsle
gordinn
white
Figure 7: Process of acquiring condition images of subjects, objects, and the background.
regions within these human bounding boxes. For objects, we first employ GroundingDINO (Luu et al. 2024), guided by object tags, to detect bounding boxes from the global image. We then apply SAM within these bounding boxes to segment the mask regions corresponding to each object first remove the subjects and objects based on the previously
entity. As for the background, obtained crops and masks in face cropping and SAM. Since SAM occasionally produces imprecise boundaries, we dilate the foreground masks before inpainting. We then utilize the diffusion-basee inpainting model FLUX (Labs) 2023), guided by the prompt: " "(background), empty, nothing, there nothing," where (background) corresponds to the extracted background tag. Finally, based on the valid outputs from the three key frames, we randomly select one result for each entity to serve as its corresponding condition image. This design aligns with the inference process, where each condition typically corresponds to single reference image. Additionally, the random selection prevents all condition images from originating from the same frame, thereby enhancing data diversity.
A.3 DATA CLEANING AND FILTERING FOR TRAINING DATASET
Our personalized multi-subject video generation training dataset is constructed based on Panda70M (Chen et al. (024).. However, not all data in the Panda70M dataset meets the quality standards required for video generation training. To ensure the suitability of the dataset, we perform a filtering and cleaning process based on the following criteria and operations:
Removing subtitles using text detection pipeline based on PaddleOCR (Baidu 2025
Cropping black-and-white borders using a simple Hough transform-bant based approach.
Excluding grayscale or black-and-whin videos to ensure sufficient visual quality and aes- thetic appeal.
Retaining only samples with QAlign quality > 3.5 (Wu al. 2023) and aesthetic score 2.0 (Wu et al. 2023), enforcing constraints on semantic alignment and visual aesthetics.
Retaining only samples with flow strength within the range of 0.05 to 2.0, constraining motion intensity to an appropriate level for video generation.
Retaining only videos in which I to 3 people are detected by Yolov9 (Wang et al.) 2024a), as videos with more than 3 individuals typically exhibit lower visual and semantic quality.
Removing duplicate videos by clustering based on VideoCLIP (Wang et al. 2024b) em- beddings.
After data cleaning and filtering, we collect a total of 1.57M video samples, comprising 1.31M single-subject, 0.23M two-subject, and 0.03M three-subject videos.
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A.4 ROBUSTNESS ANALYSIS OF EXTERNAL MODULES THE DATA COLLECTION PIPELINE
To ensure the quality and robustness of the data collection pipeline, we adopt deliberate component choices and strict filtering strategies to reduce upstream prediction errors:
Careful Selection of High-Reliahile Components: 1) For aite-att tag extraction, we used Qwen-VL-32B, VL-32B, which incorporates visual priors, instead of the pure language model Qwen-2.5- 2.5-22,,, improving accuracy (see [c; 2) For background inpainting, we selected FLUX over Stable Diffusion 2.0 due to its superior realism.
Strict Data Filtering Policies: 1) Qwen-VL predictions were accepted only if the pre- dicted entities appeared in the captions; 2) Conservative thresholds were used in detection modules (box threshold=O... 5, text threshold=O. 45 in Grounding DINO and SAM) to eliminate unreliable region-text matches; 3) For background inpainting, we re- tained only samples where the foreground occupied less than 50% of the image to maintain consistency and plausibility. Additional filters are detailed in Sec. A.3
While isolating the robustness of each external module is computationally expensive, we conduct simple test of Qwen's 's robustness in word tag extraction. A case was marked as incorrect if either the face or attribute word was missing from the caption, or if the pairing was semantically invalid. With visual priors, Qwen-VL-32B VL-32B achieved 95.2% accuracy, compared to 78.4% for Swenn22.532BB without visual input. Overall, our stringent quality-driver filtering is evident in the data selection ratio: from 70 million Panda70M samples, only 1.57 million videos were retained (2.2%). This highlights strong emphasis quality control and minimizing error propagation.
B DETAILS AND DISCUSSIONS OF LUMOSA'
B. TRAINING OBJECTIVE
Due to our LumosA' built upon Wan2. I's (Wang el al. 2025) T2V model, we adopt Flow Match- Lipman al. (2022) to formulate the training objective. Flow Matching is a generative paradigm that learns continuous- time dynamics using ordinary differential equations (ODEs). It directly es- timates optimal transport paths between distributions, enabling stable training without iterative de- noising steps. Given video latent representation
random noise 2o. and timestep & E [0, 1] drawn from a logit-normal distribution, an intermediate latent z; is obtained as the training input. Following Rectified Flows (RF) (Esser et al.) 2024), the intermediate state 2, is defined via linear interpolation between z and Zy. formulated as:
2, (1 t) Zo + t.
(6)
The corresponding ground-truth velocity is given by the time derivative of
dz:
Zo.
dt
The model trained to estimate this velocity field. The training objective is defined as the Mean Squared Error (MSE) between the predicted velocity and the ground-truth velocity:
L = Ezo, 2, Re.r [ Ctext, Ze, t; 0) v
(8)
where Ctezt and z, denote the umT5 text embedding and reference visual embedding, respectively. the model weights, and u(z,, Ctext, Ze, t; f) denotes the output velocity predicted by the generation model.
B.2 DATA AUGMENTATION DETAILS DURING TRAINING
During training, all conditional reference images are resized to match the video aspect ratio. Subject and object reference images augmented with both numerical transformations (e.g.. brightness, blur) and geometric transformations (e.g., rotation, horizontal flip), while background images are augmented only with numerical augmentations.
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B.3 GENERALIZATION OF LUMOSX TO DIVERSE T2V ARCHITECTURES
While our current implementation is based on Wam2. 1-n2 with a single-tower DiT architecture, the proposed modules in Lumos.L are architecture-agnostic and compatible with other DiT-style backbones, such as the dual-tower MM-DiT in Hunyuan Video (Kong et al. (2024) and the Parallel Attention in MAGI-1 (Sand-Al 2025). Specifically, R2PE simply reorders relative position indices and can be seamlessly integrated into any DiT-based model, as it is independent of tower design.
CSAM and MCAM function as attention masks for SeffAAtteniion (among visual tokens) and Cross- Attention (between visual and textual tokens), respectively. In MM-DiT, these operations occur after dual-stream fusion and can adopt the same masking logic. Similarly, Parallel Attention includes equivalent attention interfaces that support our mask-based modules directly. In essence, regardless of whether the base model Wan2.1, Hunyuan Video, or MAGI-1, all DiT-based T2V architectures share two key components: intra-modal interactions among visual tokens and cross-modal attention between visual and textual tokens. Our modules are explicitly designed to operate at these points, enabling straightforward extension to diverse architectures.
C MORE ABLATION DISCUSSION
C.I FINE-GRAINED QUANTITATIVE COMPARISONS OF IDENTITY-CONSISTENT VIDEO GENERATION
We have added comparisons with Phantom and SkyReels- A2 in the single-face and multi-faces video generation setting, as shown in Tab In the single-face setting (rows 1-3), our method underperforms Phantom in terms of identity preservation metrics (AreSim and Cur- Sim). However, our Lumos demonstrates a clear performance advantage in multi-face scenarios for identity preservation (rows 4-6).
C.2 GRAING QUANTITATIVE COMPARISON OF SUBJECT- CONSISTENT VIDEO GENERATION
further demonstrate the superiority of our model in handling multi-subject settings, conduct more fine-grained quantitative evaluation of subject-consistent video gener- ation across varying numbers of subjects (1-3 subjects), as shown in Tab. 6 It can be observed that as the number of subjects increases, Lumos demonstrates
Table 5: Fine-graings Fine-grained quantitative comparison of video generation across different numbers of faces.
Identity Consistency
Prompt Following
Methods
Numbers
ArcSim
CurSim
VICLIP-T
SkyReols-2 Fei et al. 2025
face
0.509
0.540
0.258
Phantom
Lio
0.602
0,637
0.261
Lumos.
face
0.542
0.575
0.262
SkyReels A2 Fei el
2025
2 faces
0,282
0,292
0,263
Phantom
Liu 01 al. 2025
2 faces
0.434
0.457
0.266
Lumos..
2 faces
0.485
0,513
0,263
increasingly superior performance across the majority of metrics, with the performance gap over other meth- ods becoming more pronounced. These experimental results further validate the effectiveness of the module we designed in Lumos,Y for modeling multi-subject face-attribute dependency relationships.
C.3 ADDITIONAL QUANTITATIVE RESULTS FROM VARIOUS ONLINE SOURCES
We also provide additional qualitative results using condition images collected from various online sources. The test includes 50 sample cases (20 single-subject, 20 two-subject, and 10 three-subject cases). Since ground truth videos are not available for these samples, ViCLIP-V cannot be mea- sured. As shown in Tab. 7 our method continues to outperform other approaches most metries, particularly those related to subject-specific customization (CLIP-T, CLIP-I, DINO-I, AreSim, Cur- Sim).
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Table 6: Fine-grained quantitative comparison of subject-consisted video generation across different numbers of subjects. (+) denotes that the respective method yields lower value than our Lumos, on this metric. whereas ( indicates better performance. Values in parentheses are recalibrated differences computed as (Method Lumos for the same setting and metric.
Methods
Numbers
Eatire Video
Extracted Subjects
Dynamie
VICLIP-T
VICLIPAT
CLIPTT
CLIP-T
DINO- ?
AreSim f
CurSim f
SkyRedls-A2
subject
0,772 (0,080)
0.252 (0.0177
0.700 (0.064)
0.177 (0,022)
0.684
(0.082)
0.199 (D:000)
0,386 (8.133)
0,380 (0.136)
Phantom
sabject
0.848 (0.004)
0 0.254 (0.005)
0,880 (0,054)
0.186 (0.013)
0.646 (0.0 (0.040)
0.215 (0.055)
0,539 (0.050)
0.581 (0.065)
Lumos,t
subject
0.852
0.259
0.34
0.199
0.686
0.268
0.489
0,516
SkyReels
subjects
(0.009)
(0:011)
0,820 (0:110)
(0.079)
(0:064)
(0.241)
Phantom
0.526 (0:138)
(0:007)
0.856
0,188 (0.015)
0.658 (0:041)
(B:014)
0.996 (B:047
0,424 (0,050)
Lamos.X
subjects
0.664
0.262
0.930
0.203
0.699
0.249
0.443
0.474
SkyReebs-A3 A2
3 subjects
0.268 (0. 156)
0.243 (0.017)
0,795 (0. 141)
0.161
(0.039)
0,546 (0. 164)
0.189
(0.097)
0.150 (0.202)
0.161
(0.219)
Phantom
subjjcts
0411 (0.013)
0. 250 (0:010)
0.840 (0. (0.096)
0.169 (0.051)
0.601 (0.089)
0,220 (0:066)
0,247 (0. 105)
0.269 (0.111)
Lumos.t'
subjects
0.424
0.260
0.936
0.200
0.690
0.286
0.352
0,380
SkyReels A2 subjects
0.257 (0.0:7)
0.241 (0.022)
0,761 (0. 165)
0.111 (0.095)
0.375 (0. (0.329)
0.116 (0. (0.1699
0.122 (0.
0.129 (0.19))
Phantum
subjects
0 28286 (0.018)
0.240 (0.0 (0:025)
0,79 (0, 136)
0.165 (0.041)
0.615 (0, (0.089)
0. 0,200 (0:065)
0.191 (0.098)
0,200 (0.111)
Lumos,
subjects
0.304
0.263
0.926
0.206
0.704
0.265
0.289
0.319
Table 7: Comparison of different methods for subject-consistent video generation, including evaluation of the entire video and evaluation on subjects.
Entire Video
Extracted Subjects
Methods
Dynamic f
VICLIP-T
CLIP-T
CLIP-I
DINO-I 1
ArcSim
CurSim
SkyReels-A2 Fei et al. 2025
0.839
0.235
0.207
0.614
0.173
0.160
0.166
Phantom Liu ct al.
0.929
0,255
0.226
0.623
0.195
0.271
0,305
Lumos.
0.247
0.232
0.668
0.336
0.317
0.346
Caption
'10 people looking directly comero fully faces. They toss catch glowing objects between other in high-Tech arena. Fost coordinated motion, faces always unobstructed well-itt.
Wanz.1- --av---38
Figure 8: Visualization of 10-person T2V results of our baseline model 1-12-1--TV
C.4 DISCUSSION ON LUMOSX'S CAPABILITY FOR CUSTOMIZED CONTROL FOR 4+ SUBJECTS
Lumos, is designed with scalability in mind. Although the training dataset contains videos with up to three subjects, the model architecture, including R2PE, CSAM,, and MCAM, inherently scalable and capable of handling more subjects during inference without retraining. To validate this capabil- ity, we conduct quantitative comparisons under a four-subject setting using 50 videos, without any retraining. The results in Tab. [ show that even in the four-subject setting, our method consistently outperforms the baselines. Moreover, performance remains stable compared to the three-subject setting, showing no significant drop. It is worth noting that although R2PE in Lumos,Y inherits the extrapolation capability of ROPE, increasing the number of subjects slightly beyond what was seen during training (e.g., from 3 4) does not lead to noticeable drop in performance during infer- ence without re-training. However, when the subject count increases substantially (e.g. from 3 to 10+), the risk of extrapolation instability rises significantly: higher RoPE dimensions may not have seen a full rotation period during training (Liu et al. 2023), causing positional encodings to become (O.O.D). which may degrade attention alignment. To support long- long-range ference without retraining, we plan to try to integrate
Scaled RoPE ANTK-RoPE (Liu) al., 2023), training-free length extrapolation method that extends RoPE's context range by ad- justing the base of its sinusoidal encoding rather than learning new positional parameters. This change enables the model to handle positions beyond the training window with minimal perplex- ity degradation and no fine-tuning required. In addition to RoPE extrapolation, we also find that the generation capability of the underlying base model itself plays a crucial role in determining the upper limit of how many subjects the customization model can realistically handle. For example, Wan2.1-1 1-33B. 1.33-T2V T2V, which we rely on, also struggles significantly when generating videos containing more than ten distinct human subjects. Fig. X presents visualizations of Wan2, 1-V.BB-2V under
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Table 8: Quantitative comparison of temporal coherence metrics on subject-consistet video generation.
Methods
Subject Consistency
Backgrund Consistency
Motion Smoothness f Face Consistency
SkyReels-A (Fei al 2025
0,654
0.798
0,979
0.739
Phantom Lumos.t
0.768
0.883
0.962
0,946
0.988
0.895
10-subject setring, where the facial quality is severely degraded and the model fails strictly fol- low the prompt, generating only nine people instead of ten. This reinforces our point that the base model's capability is the primary bottleneck when extending Lumos,X scenarios involving sub- stantially more subjects. These results indicate that current video-generation models like Wan-2.1 lack the capacity learn stable representations for such large numbers of subjects, largely because high-quality 10+ subject videos are extremely rare.
C.5 QUANTITATIVE ASSESSMENT OF TEMPORAL COHERENCE AMONG DIFFERENT METHODS
To evaluate temporal coherence, we additionally report four metrics: Subject Consistency, Back- ground Consistency, Motion Smoothness, and Face Consistency. The first three are adopted from VBench (Huang et al. 2024). Subject Consistency measures how consistently the subject (e-g., per- son or object) appears across consecutive frames. Background Consistency evaluates the temporal stability of the background, while Motion Smoothness quantifies the fluidity and natural continuity of motion. In contrast, Face Consistency is a metric we define to capture ArcFace embeddings to compute the similarity of the same face across
came-to-frrame facial sim-
ilarity. Specifically, consecutive frames. The results are shown in Tab. evaluated on the test set for subject-consistent video generation, which contains 500 videos. Our Lumos.Y outperforms other methods across all metrics.
C.6 EVALUATION PUBLIC PERSONALIZATION BENCHMARK
To further evaluate method, we conducted a quantitative comparison between our Lumos, and other approaches, in- cluding SkyReels-A2 and
Table Quantitative comparison on MSRVTT--pesoonalizatton (Chen et al.
2025) benchmark. boldfacen and underline font indicate the highest and the
second highest results.
Method
#Base Model
Text-S ?
Vid-St
Subj-S?
Dyne-Dr
AkyReels-A2 (Fe al 2025
Wan2. 1-14B-T2V
0.253
0.781
0.554
0.783
Phantom, on the MSRVTT- personalization cct al., 2025) benchmark.
Phantom (Liu ara 2025
L-
0.696
0.461
Lumas
Wan2. 1-1.31-1.3B-T2
0.258
0.707
0.549
0.786
(Chen)
The ASSVVTT-peroonaliaatii benchmark provides two evaluation modes: subject-mode and face-mode. Face-mode focuses solely on face-similarity metrics, whereas we are interested in assessing the overall subject performance (face + attributes). Therefore, conduct our evaluation under the subject-mode setting. Our evaluation is conducted using one reference image for the subject and one for the background, and, the results are reported in Tab. 9 It is worth noting that MSRVTT-perssonaiization is a single-subject benchmark, and its definition of 'subject' differs from ours: subject may refer to either a human category (e.g., man, woman) or an object category (e.g., car, horse, clothes, hat). Moreover, for human subjects under the benchmark's 's subject-mode setting, the subject is provided and therefore does not involve the aite-bttri matching problem that our method is designed to address. As a result, our method does not benefit from its explicit face-attribute relational modeling in this setting. In addition, both Lumos,Y and Phantom are built on the Wan2.1-1.3B-T2> base model, whereas SkyReels-A2 adopts the much stronger therefore further benefits from a significantly more capable backbone. Despite these disadvantages, our Lumos& still achieves the second- best overall performance, demonstrating the robustness and generalization ability of our approach even outside its primary multi-subject setting.
holistic entity without decoupled face and attribute references,
base model and
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Caption
In a surny park, en old nan in white shirt enjoys leisurely walk beside a womon dressed a vibront yellow long skirt, both sharing light-hworted convensctions
Reference Images
RAPE-CSAM RIPE
yellow long
RAPE-CSAM CSAM
4 AMI05)
Figure 9: Visual effectiveness of individual components proposed in our Lumos.X'
C.7 VISUAL EFFECTIVENESS OF INDIVIDUAL COMPONENTS OF LUMOSA'
To assess the impact of each proposed component in LumosX' we conduct qualitative evaluation, as illustrated in Fig. | Without incorporating our proposed modules (first row), the generated video exhibits noticeable identity confusion. Specifically, an old white man is misrepresented as black, and young black girl is erroneously depicted as old. With the introduction of our designed R2PE (second row), coarse- grained identity attributes, including age and skin tone, are effectively recti- fied. While the introduction of the CSAM (third row) further improves fine-grained facial identity preservation, certain artifacts still persist the generated videos. Ultimately, the incorporation of the MCAM (fourth row) leads to improved identity consistency in the generated vidcos, while also enhancing video quality to certain degree. To further validate the effectiveness our CSAM and MCAM, we also visualize the Attention Similarity Scores averaged over all 12 heads in the last DiT layer for the case shown in Fig. / The results are presented in Fig. TO] It is worth noting that our generated videos contain 81 frames, and after VAE encoding, the temporal dimension is downsam- pled by factor of four, resulting in video noise token length of 21. The results in this figure can be compared with those in Fig. in the main paper. From this comparison, we observe that CSAM (Fig. [0(a) a) vs. Fig. [O(b) enables the denoising branch to independently aggregate conditional signals and effectively bind face-attribute dependencies within the conditional branch. More- over, MCAM (Fig. c) Fig. dojd)) explicitly enhances intra-group and inter- grou- correlations among subject groups and strengthens the semantic-level representation of the visual condition to- kens.
C.8 QUALITATIVE COMPARISON UNDER VARYING HYPERPARAMETERS IN MCAM
When MCAM with 0.1 introduced (third row), the artifacts in the generated videos are significantly alleviated. Nevertheless, identity consistency is still unsatisfactory; for instance, short-sleeved T-shirt may appear as long-sleeved in the video, and facial similarity is still insufficient. At 0.5 (fourth row), we observe notable improvement in identity consistency, especially in the resemblance between facial appearances the generated videos and the reference image, along with a further enhancement in video quality. Increasing to 1.0 (fifth row) results in continued improvements in video quality, though introduces a slight decline identity similarity. The above findings align with the quantitative results reported in Sec. 4.3 of the manuscript. Accordingly, choose r = 0.5 as the final setting based on a balanced trade-off between identity consistency and video quality.
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SuSeetteeettte Stetlortty
Shertartty Scone
Visual
Vidwo Tikorn
224
22
Day Protio
Toxa
Textual
Affenitnn Ciallrity
7-86 oid man
yallow
Vead
CHAM
Teaed
61 VCMM
Figure 10: Visualization of attention maps. (i): Self-Attention similarity score. (ii: Cross-Attention similarity score.
Caption
mon yellow shirt and womon in across from each of coffee shop. engaged a lively commersation
Reference Images
None
R2PE-CSAM
man
yellow
eeccesene www.AA Www. WAW.AA AOC.R COCERAA
WOnion block shrrr
R2PE-CSAM REPE-CRAMI
-MCANILE -MCAMIOSS -MEAMMO.
Figure 11: Qualitative comparison under varying control hyperparameters in MCAM.
C.9 QUANTITATIVE ANALYSIS OF TEXT-BASED ATTRIBUTE CONTROL WITHOUT IMAGES IN MULTI-SURJECT VIDEO CUSTOMIZATION
Our method supports attribute control using text alone, and this setting is already included both the main paper and the appendix under deenittyConsisteet Video Generation (Sec. 4.1 main pa- per). To more comprehensively evaluate this setting in multi-subject video customization scenar- ios, we perform subject-region quantitative evaluations under this setting, using the as in Subject-Consiste Video Generation, to compare text-based attribute control across different methods. The results are presented in the Tab. I1 Overall, the CLIP-T scores higher under
metrics
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text-only control, which is expected since the attributes directly specified via text prompts, nat- urally yielding higher text-video similarity. CLIP-I trends largely mirror those of CLIP-T because both measure semantic similarity. Notably, for Lumos,X' (rows 6-7, col. 4), the performance gap between text-only control and text&visual control is very small, whereas for the other methods (rows 2-5, col. 4), performance under text&visual control drops noticeably compared text-only control. This suggests that those methods become more prone to attribute confusion once visual conditions are introduced, likely due the absence of explicit face-attribute relational modeling, whereas Lumos,' avoids this issue. For DINO-I, text-only control is clearly weaker than text & sual control in Lumos.X' At the same time, the substantial improvement under text&visual control indicates that our face-attribuu relational constraints are accurately matched and effectively uti- lized. In comparison, Phantom and SkyReels-A2 do not show notable DINO-I improvements when visual attribute conditions are added, suggesting that their face-attribute alignment is less reliable. Meanwhile, under the text-based attribute control setting, Lumos, performs on par with Phantom and clearly outperforms SkyReels-A2, demonstrating its strong generalization ability even without attribute images.
C.10 QUANTITATIVE COMPARISON WITH INAGE-PERSONALIZATION -BAED INITIALIZATION
To evaluate whether multi-subjed image personalization models can serve an initial image gener- ator prior to video synthesis, we conducted an additional experiment using UNO (Wu et al. (2255) to produce multi-subject customized image, which was then fed into Wan2. 1-14--22- (Wang al.) 2025) for video generation. We quantitatively compared this pipeline (UNO Wan-12V) against our method on our benchmark. The results are shown in Tab, [2] Overall, our method outperforms UNO + Wan2.1-I2V. 14B, particularly on ViCLIP-V. V. DINO-I, FaceSim and CurSim, which reflects facial identity consistency. This improvement is expected, as UNO not specifically designed for face-aware attribute binding, whereas our approach explicitly models face-attribute relational straints, leading to significantly better identity preservation in video personalization. Our ViCLIP-T and CLIP-T scores slightly weaker than those of UNO Wan2.1-12V-14p which may be tributed to the latter's ' use of a substantially larger base model 1Wan-14B).
C.11 DISCUSSION OF THE IMPORTANCE OF THE INPAINTING MODEL IN DATA COLLECTION PIPELINE
To quantitatively assess the realism of FLUX compared with other inpainting models, we conducted large-scale evaluation 2,130 test cases, applying both FLUX Labs (2023) and Stable-Diffusion- 2 Rombach et al. (2022) for inpainting. We then computed FID scores against the COCO 2017 Val to measure distributional realism. addition, we randomly sampled 100 cases and asked GPT- 4o to independently judge which inpainting result appeared more realistic. The instruction provided
GPT-4o as follows:
Instruction design for inpainting model assessment GPT-4o
PROMP"
will give you three images. The first two images are background inpsinting results obtained by masking the foreground the original image and then applying two different inpeinting modele. The third inage is the cexresponding foreground mask. Please evaluate which of the first two images prevides better impainting result based pixel-level consistency, the inpainted background. You ahould output only "1* or *2*, indicating whether image betler. not cucput anything else.
continuity, and the ovarall plauaibility of
region should inpainted with sppropriate background content. particular attention the continuity and coherence of the background in the inpainted region.
that background completion,
***
The results of both evaluations summarized in the Tab. 10 The results show that FLUX achieves better performance under both evaluation metrics. To further understand how background quality affects downstream video generation, we performed qualitative comparison using the two inpainting
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Reference Images
Caption
woman with red lipstick and lespard-print top, seated in modern office setting, makes peoce sign, and holds up white box.
Icopard print Top:
Womoon
2 modern office setting
FLUX-Inpaalatnng Diffusionn
-inpststing
BAST
Figure 12: Qualitative comparison between
and FLUX for background inpainting.
outputs as imputs video generation. As shown in Fig. 12 artifacts introduced during background inpainting are clearly propagated into the generated video, degrading overall video quality. This confirms that inpainting realism plays a crucial role in high-quality video generation and motivates our choice of FLUX as the inpainting module.
C.12 HUMAN STUDY FOR MULTI-SUBIECT VIDEO CUSTOMIZATION
To complement automated metrics, conducted a user study on a randomly sampled set of 24 video cases, includ- ing 6 single-subject, 12 two-subject, and three-subject customization scenar- ios. The comparison includes LumosrY' SkyReels-A2, and Phantom, with to- tal of 30 participants. Each participant evaluated videos along four dimensions:
Method
FID
AI Judgement f
Inipannting
96.32
36%
FLUX- ILUX-Inpainting
92.83
64 64%
Table 10: Comparison between
Rombach FLUX Labs (023) for background inpainting.
at. 2022) and FLUX
Face-Attribute Alignment: whether each face correctly matched with its corresponding attributes (e.g., clothing, accessories, or hairstyle).
Face Similarity: how closely each generated face resembles the provided visual reference.
Video Naturalness: the overall visual quality and coherence of the generated video.
Prompt Adherence: whether the generated video follows the instructions specified in the
text prompt.
For each case, participants ranked the three methods, and scores were assigned as follows: 1 point for first place, 0.5 points for second place, and 0 points for third place. Final scores for each dimension were computed as the weighted average across all participants. The results are shown below Fig L3 Overall, our method achieves superior performance across all four evaluation dimensions.
C. ANALYSIS OF COMPUTATIONAL OVERIEEAD AND LATENCY IN LUMOSA'
To evaluate the computational impact of our relational attention mechanisms, we report detailed inference-stage statistics computational overhead and latency. Specifically, we present the erage latency for each Self-Attention and Cross-Attention operation, the average per-step latency, per-step FL.OPs, and GPU memory consumption. All measurements obtained on an H20 GPU
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Published as a conference paper at ICLR 2026
Human Sulti-Subjjec Video Customization Four Dimensions Comparison
HUMAN STUDY FOR SUBJECT CUSTOMIZATION
Average Scores Comparison
0.7
Fowmtor uursod
6,780
0.6
3.33
0.2
0.1
Pact Artribute
Face Siilatiy
V
Prompt Adhavika
Afgrement
Dimensions
Models
Figure 13: Human study results for multi-subjeet video customization. (a) Results across the four evaluation dimensions. (b) Average scores computed over all four dimensions.
Table 11: Quantitative comparison of text-only text&visual attribute control across methods
Methods
Attribute Control
CL.IP-T
CI.IP-I
DINO-T
text-only
0.192
0.643
0.192
SkyReels-A2 Fei
2025
text&visual
0.178
0.606
0.192
text-only
0.207
0.687
0.209
Phantom Liu al. 2025
text&visual
0.185
0.647
0,216
lext-only
0.205
0.684
0.210
Lumos
vext&visual
0.193
0.681
0,265
Table 12: Comparison of sangg--prsonnlization based method and I. Lumos.Y' for subject-consistent video gen- eration, including evaluation of the entire video and evaluation subjects.
Entire Video
Extracted Subjects
Methods
Dynamic f
VICLIP-T
VICLIP-V.
CLIP-T 1
CLIP-I
DINO-I
AreSim
CurSim
UNO Wan2. 1-22-14B
0.547
0.261
0.201
0.650
0.237
0.244
Lumos.t'
0.723
0,260
0.932
0.201
0.692
0.261
0.454
0.483
under the same video customization scenario. The results are shown in Tab. 13 and the key observa- tions are summarized below:
R2PE incurs no extra compute or memory overhead (row vs. row 2), since it reorders relative position indices rather than introducing parameters operations.
The original implementation of the Wan2.1-T22 model utilizes FlashAttention 2.0 for ac- celeration, which does not support custom masks. In our CSAM module, we replace it with MtgiAttention (Sand-AI, 2025)) (see Sec. 3.3.T in the main paper), which supports custom masking and achieves faster inference (row vs. row 3). The integration of CSAM results in only a modest overhead (row 4 vs. row 5).
For MCAM (Cross-Attention) MagiAttention cannot handle numeric masks, so we use PyTorchh' native implementation. Since the key comes from relatively short T5 token sequences (512 tokens), the computation remains lightweight. As result, MCAM does not noticeably affect Cross-Attention latency (row 5 vs. row 6, col 2), and the additional cost of computing the dynamic scaling matrix is relatively small and well within acceptable bounds (row vs. row 6, cols 3-5).
In summary, through the of efficient modules (MagiAttentton)) and optimized strategies (e.g., lightweight scaling matrix design), Lumos achieves high computational efficiency with minimal additional inference overhead (row 6 rows and 4).
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Published as a conference paper at ICLR 2026
Table 13: Inference-ster stage computational overhead and latency statistics for each module under the same video customization case on an H20 GPU,
Methods
Self Attn Latency
Cross-Attn Latency
Lateney/step
TT.OPsstep
GPU Memory Usage
0.1440s
0.0026s
8.66s
195.44
21.5G
+R2PE
0. 0.1441s
0.0025s
8.66s
195.44
21.5G
None MMgiittentionn Self-Attn)
0.0935s
0.0025s
5.79s
195,44 T
21.5G
+ #22EE Artentittntion in Seff-Artn)
0.0936s
0.0026s
5.79s
195.44
21.5G
-R2PE-CSAM
0.0966s
0.0026s
5.81s
195.44
21.5G
-CMM--CAAMMMCAM
0.0965s
0.0045s
6.11s
195.46
22.7G
D MORE VISUALIZATION RESULTS
D.1 ADDITIONAL. RESULTS OF IDENTITY- VIDEO GENERATION
The qualitative identity-consistent video generation results are shown Fig. 14 Under the single- subject setting (Case 1 and Case 2), method demonstrates significantly superior identity preser- vation performance compared to ConsislD (Yuan et al. 2024) and Concat-ID (Zhong et al. 2025). Under the multi-subject setting (Case and Case our approach demonstrates superior perfor- mance over SkyReels-A2 (Fei et al., (2025) and Phantom (Liu et al., 2025) in both identity preser- vation and the alignment between multiple subjects and captions, effectively preventing character confusion (Case 3 SkyReels-A2) and positional confusion (Case 3 Phantom).
D.2 ADDITIONAL RESULTS OF SUBJECT-CONSISTEN VIDEO GENERATION
The qualitative comparison of subject-consistent video generation is shown in Fig. 15 and Fig. 16 The experimental results demonstrate that our model supports flexible multi-subject foreground- background video customization. Compared to SkyReels-A2 (Fei et al. 2025) and Phantom (Liu al.) 2025), our approach achieves superior subject consistency, accurately matching the human faces and their corresponding attributes, and maintaining the reference identity throughout the gen- eration process. In contrast, SkyReels-A2 struggles when handling multiple customized subjects, often exhibiting character confusion (Cases 2, 4 in Fig. a5] Case in Fig. (6g. and subject disap- pearance (Case 3 in Fig. [5]. Similarly, Phantom also suffers from character confusion (Cases 1,2 in Fig. 16) in comparable scenarios. Furthermore, the videos generated by our LumosX' are natural and realistic. Unlike Phantom, which suffers from noticeable quality degradation as the number of reference condition images increases, resulting in visual artifacts (Case in Fig. 15 and Cases 2, 3, in Fig. [6] or an unintended cartoon-like style (Cases 2, 3, in Fig. 15
Fig-15)amd
E LIMITATIONS AND FUTURE WORK
Although our Lumos,' effectively addresses the key challenge of multi-subject video personaliza- tion by explicitly modeling the dependency of face-attribute within the subject group, it remains constrained by limitations in model size as well as the diversity and scale of training data. Con- sequently, the performance of Lumos has not yet reached its full potential. Looking ahead, we plan deploy Lumos, on 1-n2.1-14B-11 T2V model (Wang et al. 2025) and train it larger- scale, higher-quality, and more diverse dataset to further enhance its performance and generaliza- tion capabilities. Moreover, to push the boundaries of dynamic behavior understanding and better capture complex motion patterns, we also recognize the value of incorporating motion-aware con- straints-ffor instance, augmenting data collection with motion descriptions (e.g., walking, running) and integrating motion cues within the MCAM module to strengthen correlations between visual tokens and motion-award aware textual tokens, which would improve alignment for dynamic behaviors and multi- multi-subject interactions (e.g., hugging, handshaking, passing objects).
F LLM USAGE STATEMENT
We used large language models (LLMs) to assist with the writing and refinement of this manuscript, primarily for improving grammar, clarity, and overall readability. The LLMs were not involved in formulating research questions, designing experiments, developing algorithms, conducting analyses,
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Published as a conference paper at ICLR 2026
Caption
woman with -ooutder-knnghh wavy heir, weaning white off thoulde-shholder shoulder top and block choker nechlace with small, round, brown beads, seen speaking and gesturing with her hands She has o neutral expressiion and is weining makeup, including eyeshndow and lipetick, Behind her, there is black metel shelf with three Tiers. Or. the top shelf, there is a small weoden box with heart- tearp-d
cutoutt on the lid.
Consiell>
Reference Image
Concat-
Lamoss
Captton
cocasionally beard, and light brown shirt, stands front shed with roof, speaking
adoreed yellow
aftached to along pot, green coiled
right the shed, ground
surny featuring thick trunk and green
Concos/D
Reference Image
Lumos Concut-ID
Caption
Two mas sit side side on light sefo, engagedd in convverstionn The man on the left, wearing dark blue shirt, smiles and looks dinectly the camera, while the the night, light blue accasionally looks his companion with subtle smile. Behind them, brick woll serves the backdrap. with dark brown woeden shelf mounted on the shelf, two white condies placed side side.
Reference Images
SkyKReels-h
The left
Phantom
Lumoxx
the right
Caption
people standing
refrigerator.
top looke
alightly right,
center, young
top knat, wearing light gray sweatshirt, piace of
long sieeve
looking of har. refrigerator them has mognets papers,
with white cobinets abowe
Reference Images
Phantom SkyRoelss-h A2
woman
young
LumosX
Figure 14: Qualitative comparison for identity- consistent video generation.
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Published as a conference paper at ICLR 2026
Caption
with short brow hair, weering light gney heodie and black watch, drives sieek black comertible through scenic trees greenery. side mirror rectangular with edge.
Reference Images
Sky Rocllell
lighT gray hoodle
young
Lamnox Phantom
black watch
scenic area with frees and greenery
Caption
e
wearing green shirt, man the right, dressed green mus, anartphone
engaged
luughing clopping the mon left. bockground, placed room warmly lit, creating friendly ctmossheree
Reference Images
Phantom SkyRocls-A2
Film
the left shirt
mon on dark greent the. right..... ahirt
Lumos'
Caption
in white shirt and denim jacket with ina bed cozy, warnly lit room. weoden
bedside tablle lamp backiground
Reference Images
Phantom Roolo---AA
light brown
Lamosx
cozy, weraly
Caption
jocket and gray shirt specks womon with short Television studio, small groy table next to them holds o black cup and two red dice.
dark dress mon dark
Reference Images
MoyRoll--Az
black
blad man
Jocket
ShUFT
Phantom
dare
womoo
dregss man
BUiT
Lumonx
dice
television studio
Figure 15: Qualitative comparison for subject-consistent video generation.
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Published as a conference paper at ICLR 2026
Caption
Two men stand et bustling city crosswalk, The first man wears cripp white jacket, while the second man controsts sharply sleek block jocket, both deep conversation.
Reference Images
Phanton SkyReels-A2
mon
White Jocket
block jocket
Lamoos'
mon in white shirt and women weoring red long skirt walk side by side through bustling outdoor market, discussing the items
cole.
Reference Images
Sky
monh
white shirt
Phantom
Lumos
Caption
vibrast red adarned flowing yellow skirt leisurely strolling under by seaside. tuge playfully their dynonic
cousing charmingly. Their against the franquil blue point picturesque pasceful
Reference Images
Phantom AlyRoob-Aa
yellow skirt
Lumosx
under the seaside
Caption
AT
Norn cowboy They young girl bright tulle stands beside dressed Mhaki belt,
creating greenery background suggest peacefel, setting, emphasizing gentle connection figures.
Reference Images
Phantom SkyReehs-A2
old man
cowboy
capbara
LumouX
Figure 16: Qualitative comparison for subject-consistent video generation.
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Published as a conference paper at ICLR 2026
or drawing conclusions. All substantive content, including the conceptual framework, methodology, experiments, and findings, is entirely our own and has been independently verified by the authors.
G BOARDER IMPACT
This work proposes a personalized multi-subjeect video generation framework capable of ficxibly customizing both the foreground and background of generated vidcos. It demonstrates strong po- tential in various application domains such as virtual theatrical production and e-commerce. We recognize the ethical and social implications of generative content, particularly concerning content authenticity, potential misuse, and privacy. As our method explicitly focuses on identity generation and face synthesis, emphasize the importance of responsible use in accordance with relevant le- gal and ethical standards. Meanwhile, all training data utilized in our framework were obtained from publicly available datasets (Panda70M) under appropriate usage licenses. We also encourage future research to incorporate safeguards such as digital watermarking, provenance tracking, and content verification tools to support the trustworthy deployment of personalized video generation models.
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Reviews:
[{"review_id": "qyHcQUA8II", "reviewer": "Reviewer_mVQz", "summary": "This paper presents a novel framework for personalized video generation with controllable facial attributes. The authors introduce a dedicated dataset curation pipeline tailored to this task and propose model designs to capture subject-attribute dependencies. Experimental results demonstrate satisfactory performance.", "questions": "- In the current data curation pipeline, human subjects are extracted from frames at the start, middle, and end of the video, and all input subjects appear in the full dataset. How does the method handle scenarios where a particular subject is present only partway through a video (e.g., featured in the first half but absent in the second)? While autoregressive generation may naively address this by using a sliding window of subjects, clarification on handling such cases would be helpful.\n- Since attributes are provided as images, can existing multi-subject image personalization models be used to generate the initial image, followed by video generation? A comparison with such an approach would strengthen the experimental evaluation.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses an important and challenging problem in video generation: enabling both personalization and semantic control, areas where previous works have struggled.\n- The paper introduces the dataset for this task and the designs on model side.", "weaknesses": "- The focus on generating videos based solely on facial attributes may limit the method’s generalizability to broader contexts.\n- It is unclear how the approach performs when handling videos with multiple subjects (three or more), as the datasets support up to three subjects.\n- The implementation details lack specifics on computational requirements such as GPU count and training duration.\n- The definition of a \"subject\" during data curation is ambiguous. Figures 3 and 4 suggest that a subject may consist of a single face and its corresponding attribute, but the criteria for data curation and the types of attributes used require further clarification. While some details are mentioned in the appendix, a clear and formal definition should be included in the main text.\n- The maximum number of attributes that can be assigned to one subject is not specified.\n- It remains unclear whether the method supports using textual attributes in the absence of images. Although text prompts may control generation, the paper would benefit from further discussion and experiments on text-based attribute control in the context of multi-subject video personalization.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IwuT5G0KVr", "reviewer": "Reviewer_Mi1n", "summary": "This paper proposes LumosX, a framework for personalized multi-subject video generation that addresses the critical challenge of face–attribute misalignment in existing text-to-video (T2V) models. The core innovation lies in explicitly modeling face-attribute dependencies at both the data and model levels, enabling fine-grained, identity-consistent, and semantically aligned video synthesis.", "questions": "1. Do you plan to conduct a human study to assess perceived face-attribute alignment, video naturalness, or prompt adherence? If not, why do you believe automated metrics alone are sufficient to validate LumosX’s practical utility?\n2. Can you provide quantitative metrics to support FLUX’s \"superior realism\" over other inpainting models? How do background quality variations affect downstream video generation?", "limitations": "", "rating": 6, "confidence": 1, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Combines MLLM-driven data annotation with relational attention to solve a misalignment problem—an innovative fusion of NLP (entity extraction) and computer vision (positional embedding/attention masking) techniques.\n2. The problem statement (face-attribute misalignment) is clearly articulated with examples (e.g., \"A man on the left... and a man on the right...\" causing confusion).\n3. Enables flexible multi-subject customization (foreground/background control) that prior models lack.", "weaknesses": "1. The training dataset only includes 1–3 subjects, the paper acknowledges instability for 10+ subjects due to RoPE extrapolation. No preliminary results or mitigation detailsare provided beyond a future work note.\n2. All evaluations rely on automated metrics. Human judgment of face-attribute alignment, video naturalness, and prompt adherence would strengthen claims (e.g., do viewers perceive fewer misassignments in LumosX-generated videos?).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jA049bkssz", "reviewer": "Reviewer_GYqr", "summary": "This paper introduces LumosX, a multi-subject video personalization framework which aims at maintaining the identity and attribute consistency across subjects. To achieve this, LumosX integrates both data-level and model-level innovations: 1) on the data side, the authors uses Qwen2.5-VL to infer subject–attribute relations and generate explicit relational priors; 2) on the modeling side, they introduce relational self-attn & cross-attn, which incorporate relational RoPE and custom attention masks to enhance intra-group cohesion and suppress cross-group interference. Extensive experiments show that LumosX achieves SOTA performance compared to the baseline models like Phantom and SkyReels-A2, particularly in maintaining identity consistency and semantic alignment.", "questions": "- Could the authors provide more examples with multiple identity-attribute pairs? Since this is the main focus of the paper, it is difficult to justify the model performance without such samples.\n- Could the authors also provide the examples that the existing models fail on handling identity-attribute consistency?\n- Could the authors discuss the extension with more complex relational and dynamic modeling? The current relational modeling is too simplistic to cover the complexity of real-world videos.\n- Could the provide more theoretical/empirical analysis on the proposed CSAM and MCAM?\n- Could the authors also provide the scores on the public benchmarks?\n- [Minor] The \"3D VAE Decoder\" block in Figure 3 is upside-down.\n- [Minor] What is CSMA in Section 4.3 and Appendix C?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "- This paper focuses the problem of identity-attribute consistency, which sounds interesting but has been well explored topic in the domain of multi-subject video personalization.\n- From the Tables 1~3, LumosX shows strong quantitative results comparing the SOTA models, like Phantom and SkyReels.\n- The paper is well written and easy to follow. The figures are well-plotted and informative which can make readers quickly understand the core ideas.", "weaknesses": "- Although the paper focuses on maintaining consistent identity-attribute pairing, the author only show one generated example with multiple identity-attribute pairs (the right example in Figure 5). With such one sample, it is unconvincing to claim that LumosX is capable of solving the issue of inconsistent identity-attribute pairing.\n- While interesting, it is unclear whether the issue of inconsistent identity-attribute pairing is an actual problem. First, in the only multi-subject example provided in the paper, both the comparing models (Phantom and SkyReels) do not exhibit attribute-swapping issues. Further, most of the existing models also supports full-body personalization which can easily fix such problem without the proposed relational design. If the problem rarely appears or can be easily fixed by the current models, the value of the proposed framework is limited.\n- The relational modeling is too simplistic and only considers pairwise identity-attributes grouping. Therefore, the model cannot handle more complex spatial or interaction relations, which are quite common in real-word videos. For example, \"the person holding the cup\" vs. \"the cup near the person\" (which requires the spatial modeling) or \"person-person or attribute-attribute\" interactions.\n- Another problem of the proposed relational modeling is that it is determined during the dataset collection phase. Such design assumes the relations are fixed throughout the video and fails to handle more complex scenarios where the scene evolves, such as someone turns around / changes pose / are occluded. The authors could consider dynamic re-binding design which could address the limitation by adjusting the grouping relation during video generation process.\n- While the paper introduces multiple masks, such as CSAM and MCAM, to bias the attention, it is unclear how attention actually changes under these biases. The author could visualize the attention maps or compute the attention scores to better interpret / understand the proposed relational self-attn / cross-attn.\n- All experiments are conducged on a private benchmark collected by the authors themselves, which makes it hard to reproduce the results. Could the authors also provide the scores on the public benchmarks, such as MSRVTT-personalization [1] and ​​OpenS2V-5M-Eval [2]?\n\n[1] \"Multi-subject Open-set Personalization in Video Generation\", CVPR 2025\n\n[2] \"A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation\", NeurIPS 2025 D&B", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iEypAbfg9C", "reviewer": "Reviewer_sFZt", "summary": "This paper addresses **personalized multi-subject video generation** where each subject is defined not only by identity (face) but also by **subject-specific attributes** (e.g., clothing, accessories) and an explicit **background/object** context. On the data side, the authors build a complete pipeline that re-captions videos, extracts entities/attributes, and produces **reference image groups** for faces, per-subject attributes, independent objects, and a clean background—then binds these to **prompt tokens** for training. On the modeling side (built on Wan 2.1, 1.3B), they introduce three key components—**R2PE**, **CSAM**, and **MCAM**. Experiments focus on both identity consistency and **multi-subject group consistency**, with ablations indicating each component’s contribution.", "questions": "1. **Subject-consistency setting vs. baselines.** Both **SkyReels-A2** and **Phantom** claim multi-subject capability. In your **subject-consistency** experiments, did they receive exactly the same bundle of inputs (per-face, per-attribute, per-object, and background references), or only faces? If not identical, please clarify the input protocol and why the comparison is fair.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Clear problem focus & delta.** Goes beyond prior “multi-face-only” personalization to **multi-subject** customization with explicit attribute and background/object control, making it applicable to more realistic scenes.\n- **Well-engineered data pipeline.** The end-to-end collection & preprocessing pipeline—especially **binding reference images to prompt tokens**—is practical and valuable; if released, it would benefit future work.\n- **Methodological novelty.** The proposed **R2PE**, **CSAM**, and **MCAM** are sensible and targeted to the identified failure modes; ablations support their effectiveness.\n- **Stronger multi-subject results.** Improvements are most evident in **multi-subject group** consistency compared to recent baselines.\n- **Reproducibility mindset.** Implementation details are reasonably complete; the paper frames a clear benchmark setting derived from the same pipeline.", "weaknesses": "- **Baseline parity/inputs.** It’s not always clear that baselines ingest the same **attribute/object/background** references rather than face-only cues; this affects fairness in multi-subject comparisons.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)