ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation
Authors: Wei Li
Yiheng Zhang
Fuchen Long
Zhaofan Qiu
Ting Yao
Xiaoyan Sun
Tao Mei
Venue: ICLR 2026 Poster (2026)
Keywords: Video Generation; Identity Preserving; Diffusion Models
Primary area: generative models

Abstract: Personalized video generation faces a fundamental trade-off between identity consistency and action realism: overly rigid identity preservation often leads to unnatural motion, while emphasis on action dynamics can compromise subject fidelity. This tension stems from three interrelated challenges: imprecise subject-video alignment, unstable training due to varying sample difficulties, and inadequate modeling of fine-grained actions. To address this, we propose ReactID, a comprehensive framework that harmonizes identity accuracy and motion naturalness through coordinated advances in data, training, and action modeling. First, we construct ReactID-Data, a large-scale dataset annotated with a high-precision pipeline combining vision-based entity label extraction, MLLM-based subject detection, and post-verification to ensure reliable subject-video correspondence. Second, we analyze learning difficulty along dimensions such as subject size, appearance similarity, and sampling strategy, and devise a progressive training curriculum that evolves from easy to hard samples, ensuring stable convergence while avoiding identity overfitting and copy-paste artifacts. Third, ReactID introduces a novel timeline-based conditioning mechanism that supplements monolithic text prompts with structured multi-action sequences. Each sub-action is annotated with precise timestamps and descriptions, and integrated into the diffusion model via two novel components: subject-aware cross-attention module to bind sub-action to the specific subject of interest and temporally-adaptive RoPE to embed the rescaled temporal coordinates invariant to action duration. Experiments show that ReactID achieves state-of-the-art performance in both identity preservation and action realism, effectively balancing the two objectives.

Published as a conference paper at ICLR 2026
REACTID: SYNCHRONIZING REALISTIC ACTIONS AND IDENTITY IN PERSONALIZED VIDEO GENERATION
Wei :i Yiheng Zhang?! Fuchen Long?, Zhaofan Qiu², Ting Yao", Xiaoyan Sun Tao Mei "University of Science and Technology of China, HHiDream.a ai Inc.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
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Figure 1: The landscape examples of ReactLD for personalized video generation. For each case, ReactID automatically plans distinct action timeline for each reference subject, generating final video where each identity performs actions according their synchronized schedule.
ABSTRACT
Personalized video generation faces fundamental trade-off between identity con- sistency and action realism: overly rigid identity preservation often leads to un- natural motion, while emphasis action dynamics compromise subject fi- delity. This tension stems from three interrelated challenges: imprecise subject- video alignment, unstable training due to varying sample difficulties, and inad- equate modeling of fine-grained actions. To address this, we propose ReactID, comprehensive framework that harmonizes identity accuracy and motion uralness through coordinated advances in data, training, and action modeling. First, we construct ReactID-Data, a large-scale dataset annotated with a high- precision pipeline combining vision-based entity label extraction, MILM- M-baeed subject detection, and post- verifieeiiii ensure reliable subject-video correspon- dence. Second, we analyze learning difficulty along dimensions such subject size, appearance similarity, and sampling strategy, and devise a progressive train- ing curriculum that evolves from easy to hard samples, ensuring stable conver- gence while avoiding identity overfitting and copy-paste artifacts. Third, Reac- IDD introduces novel timeline- based conditioning mechanism that supplements monolithic text prompts with structured multi-action sequences. Each sub-action is annotated with precise timestamps and descriptions, and integrated into the dif- fusion model via two novel components: subject-aware cross-attention module to bind sub-action the specific subject of interest and lemporally-adaptive RoPE to embed the rescaled temporal coordinates invariant to action duration. Exper- iments show that ReactID achieves state-of-the-ars performance in both identity preservation and action realism, effectively balancing the two objectives.
"This work was performed at HiDream.ai. "ooresponniig authors.

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High-Quality Subject Annotation
Difficulty-Awane Curriculum Learning
Structured Timeline-bacie based Conditioning
Converdoonal Arnoaations
Samples
Hard Samples
Goanedranned
Po.
Idit soecie
place
bost acatta
ReactiD Annotations
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Figure 2: An illustration of three key components in ReactID improve personalized video generation from the perspectives of data construction, training paradigm and timeline formulation.
INTRODUCTION
The generation of high-fidelityy video content has emerged as a pivotal task in computer vision and graphics, with profound applications in content creation, virtual reality, and personalized media. A particularly challenging and impactful subset of this field is Personalized Video Generation, which aims to create video sequences of a specific subject (e.g.- person object) performing desired action while maintaining the identity of subject across frames. The core challenge in this task lies in achieving an optimal balance between two competing objectives: preserving the identity of subject with high fidelity and ensuring the realism and naturalness of the generated actions.
Current state-of thef-the-tt approaches (Chen al. 2025b Huang et al. 2025 Fei et al. 2025 Liu
et al.] 2025b] Hu et al. (2025a) Diang et al. (025). typically built upon iittusion-baser architectures, attempt to approach personalized video generation by incorporating reference identity information through vross-attention mechanisms or adapter modules. Despite the promising results, these meth- ods consistently fall short of achieving the ideal balance between identity consistency and action realism. We speculate that this limitation stems from three fundamental bottlenecks in the genera- tion pipeline, each contributing to the imbalance in different ways:
(1) Inaccurate identity preservation resulting from noisy subject- video correspondences directly undermines the identity aspect of the balance. Conventional annotation pipelines often produce incomplete subjects, misaligned bounding boxes, and erroneous identity associations. When models learn from such imperfect supervision, they develop unreliable identity representations, leading either identity drift or visual artifacts in generated videos.
(2) Unstable convergence patterns caused by varying learning difficulties across samples disrupt the training process itself. Easy samples (with large, clear, and temporally consistent entities) encourage the model to adopt "copy-paste" strategy, preserving identity the expense of natural motion. Conversely, hard samples (with small scale, or appearance variations). though slowing convergence and complicating identity alignment, serve critical function: they compel the model to maintain and leverage its pre-trained generative priors, thereby enhancing realistic action synthesis and mitigating catastrophic forgetting of motion dynamics.
(3) Compromised action naturalness often results from coarse-grained action modeling that fail to capture temporal dynamics. Without precise temporal guidance, models tend to prioritize identity features-whitn are more explicitly supervised- motion patterns, resulting in stiff, unnatural movements that diminish the overall realism of generated videos.
In this paper, we propose ReactID, comprehensive framework designed to synchronize realis- tic action generation with faithful identity preservation through synergistic improvements across data curation, training strategy, and action modeling, as illustrated in Figure / Specifically, we first introduce ReactID-Date a large-scale dataset with unprecedented annotation quality, enabled by novel automated pipeline that leverages vision-based entity word extraction, Multi-modality Large Language Models (MLLM) based detection and additional post- vestfeiifiiatoon to ensure reliable subject-video correspondences. Then, we develop a difficulty-wware progressive learning strategy that explicitly manages sample difficulty based on subject size, appearance similarity, and sam- pling strategy. Our curriculum learning approach ensures stable convergence while preventing both overfitting to easy samples and underfitting plex, multi-action sequences, ReactID exploits a structured mmeline-based conditioning mechanism complement the conventional use of single, monolithic text prompt for video generation. This timeline is composed of multiple fine- -reegga sub-actions, each annotated with precise start and end times alongside detailed textual description of the specific motion. To effectively inject this rich
challenging ones. To enable the modeling of com-
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-eatioltempora information into diffusion- generator, we introduce two key novel compo- nents: a subject-aware cross-attention module and a temporally-adaptive RoPE mechanism. The former embeds the rescaled temporal coordinates of the sub-actions, enhancing the compatibility and robustness to actions of varying lengths. The latter explicitly binds the descriptive sub-action cues to the specific subject of interest within the scene, ensuring that the generated motions are curately associated with the correct subject. This synergistic integration enables ReactlD to achieve unprecedented control over personalized video generation, seamlessly synchronizing realistic ac- tions with subject identity.
The main contributions of this work are summarized as follows. We propose ReactID, an innovative framework that effectively harmonizes identity preservation and action realism in personalized video generation. To support this framework, we introduce large-scale, nigh-precision dataset (ReactID- Data), progressive training strategy that transitions from easy to difficult samples, and novel modeling architecture capable of representing complex multi-action sequences. Through extensive experiments, ReactID demonstrates state-of-the-arr performance in both identity consistency and motion naturalness. Moreover, we believe the release of ReactlD-Data with its temporally-prece multi-action annotations will significantly facilitate future research in personalized video generation.
2 RELATED WORK
Video Diffusion Models. Recently, text-to-video (T2V) generation has witnessed remarkable progress, largely driven by advances in diffusion models. Early attempts (Blattmann et al. [023]
Guo et al. 2024 Zhang et al., 2024, 2025c) built on UNet (Ronneberger et al.] (015) architec- ture are restricted to synthesize short clips with limited spatial and temporal resolution. Facing the unprecedented capability demonstrated by Sora (Brooks al. (024) in generating high-quality, minute-long videos, transformer-based diffusion models (Peebles & Xie 2023) are increasingly introduced the mainstream, replacing UNet backbones and enabling scalability. Among these, MMDiT (Esser et al., (2024), a dual-stream DiT architecture introduced by Stable Diffusion 3, is sub- sequently adopted in open-source video diffusion projects such as Cog VideoX (Yang et al., (025) and Hunyuan Video (Kong et al., 2024). More recently, Wan et al. (2025) and Cai et al. (2025) pro- pose novel video DiT structures, and achieve remarkable performance in realistic video generation.
The field of personalized video generation has witnessed rapid evolution from early UNet-based approaches (Chefer et al. 2024: He al.. 2024b Ma et al. [024] Wu et al., 2024 Wang et al., 2024b) Xu et al. 2025b: Chen et al.) 2024 [i et al. [025bb Wu et al. 2025) to advanced Diffusion Transformer (DiT) architectures (Yuan et al., 2025b] Zhang et al, 2025b Wei et al. 2025a). Initial efforts on UNet were often confined to simple single-subject sce- narios (He et al., 2024b; Ma et al. 2024] He et al., 2024b Jiang et al. 2024; Weii et al. 2024b), struggling with limited motion and the need for extensive test-time tuning (Wei et al.] [024a]] Wu jet al. 2024). Video (Wei et al.] (024a) first decouples the learning for subject and motion. CustomCrafter (Wu al. 2025) finetunes self-attention layers to enable subject customization with- out disrupting the inherent motion modeling. With the paradigm shift to DiT frameworks, recent works have substantially advanced visual fidelity and tuning- free capabilities. Owing to the scalable nature of DiT, the scope of personalization has been broadened to encompass complex multi-subject and open-domain settings (Huang et al., 2025) Chen et al. 2025b Liu et al. 2025b Jiang et al. 2025 Fei et al. 2025 Hu et al. 2025a| Deng et al.) 2025 Hu et al. 2025b Xu et al. (025a)) More- over, customization learning are not limited to visual appearance and identity, but also extended to relations and interactions. Pioneering this direction, Re (Huang et al.) (024) proposes diffusion-based relation inversion framework to capture specific relation from images, which is fur- ther employed for image generation. DreamRelation (Wei et al. 2025b) further extends such type of relation modeling into video synthesis. Despite these advancements, a key challenge persists: the inherent trade-off between subject identity preservation and accurate motion generation often results in ''coyy-patte" artifacts and unnatural motion. Compounding this issue is the scarcity of high-quality training data, which remains a primary bottleneck. This underscores the urgent need for datasets with high action precision, video temporal consistency, and reference image diversity.
Personalized Video Generation.
In summary, our work mainly focuses on preserving subject identity with high fidelity and ensuring natural actions in personalized video generation. The proposal of ReactID contributes by studying not only how to conduct progressive subject-to-video learning for accurate identity preservation while alleviating "copy-paste" artifacts, but also how to integrate the timeline information into video diffusion to achieve precise action realism.
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3 METHOD
3.1 REACTIDD-DATA: A DATASET FOR PERSONALIZZRD VIDEO GENERATION
Data Preparation. We initialize a pool of 20 million videos from publicly available video collec- tions such as HD-VG-130M (Wang et al. 2025) and OpenHuman Vid (Li et al., 2025a). Then employ a pipeline to process raw videos, which involves key stages such as scene detection, video transcoding, text removal, quality assessment and filtering, and subject-centric captioning. The im- plementation details for this workflow are elaborated in the supplementary materials.
Entity Extraction. A typical method to identify entities from video captions is extract nouns via a Named Entity Recognition (NER) model (e.g,, SpaCy (Honnibal & Montani 2017 with a pre-defined taxonomy. However, the limited taxonomy results in coarse-grained entities that cannot distinguish between different instances, especially for living subjects. For instance, two distinct individuals might both be labeled simply as "a person." " To mitigate this issue, we process living and non-living subjects differently. We first construct a 1 1200-term taxonomy by combining nouns from captions with labels from image datasets (e.g.. Deng et al. (2009), Caesar et al. (2018), and Shao et al. (2019)), and then divide the terms into living and non-living categories. For each video- caption pair, an NER model guided by our taxonomy is employed to extract all entities from the caption. Entities identified as non-living subjects are directly retained the final result, whereas those corresponding to living subjects undergo further processing. For these cases, a lision-language model analyzes the video content to generate fine-gratne mne-grrined, descriptive entity label for each living subject. As a result, we obtain specific entity labels for living subjects like "the person in red" and "the person sitting the bench", rather than generic ones such as "a person" and "another person", enabling more precise entity referencing, which is curial for the following processing.
Subject Detection and Segmentation. Next, extracted entities are grounded to specific spatio- temporal regions within the video via subject detection and segmentation, thus forming compre- hensive entity annotations. The primary challenge in this process lies in distinguishing between visually/semanticaly similar subjects and ensuring their correct association with the corresponding entities. To achieve this, an MLL.- detector, Florence-2 (Xiao et al.) 2024), is employed to locate the bounding boxes of the given entity labels, which are further verified by examining the cross-modal distance in the SigL.PP (Zhai et al.] (023) feature space. Conditioning on the bound- ing boxes, we obtain the segmentation masks via the SAM (Ravi et al. 2024) model for each entity. Moreover, to better support face-centric face bounding boxes and masks for "human" entities utilizing the InsightFace Toolbox (Deng al., 2019) and the SAM model, respectively.
generation tasks, we additionally extract
3.2 DIFFICULTY-AWARE CURRICULUM LEARNING
A training sample consists of one more reference images Iref cropped from subject's bounding box, a video clip in which the subject appears Vgt, and a video caption text. Easy samples, where the subject in the video is large and highly similar to the reference, can encourage a "copy-paste" short- cut that harms generalization. In contrast, harder samples with small or varied subjects in videos force the model to learn the desired skill of fusing identity in Iref with action and context provided in text, but they also present convergence challenge. To balance the easy and hard samples and ef- fectively leverage our ReactID- -Dtaa we introduce an easy-to- tard curriculum learning strategy that ensures stable convergence while avoiding identity overfitting and "copy-paste" artifacts. Techni- cally, we formalize this difficulty-aware learning by quantifying sample difficulty using three cues: subject size, appearance similarity, and sampling strategy.
Subject size refers to the spatial proportion of the subject within the video frames. We define the difficulty score Daub for each sample. Let the video Vgz consist of N frames, each with a resolution of H x W. For the n-th frame, a binary segmentation masks M,, is used, where (i,j) = indicates that the pixel belongs to the subject in the reference image. The difficulty is calculated as:
Danh = 1 (NHW)-
'
N R=
H j=1 [(M,(1))113
Σ
W
(I)
where 1(.) is the indicator function. This formulation ensures that a smaller subject yields a higher difficulty score D which approaches 1.
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Appearance similarity quantifies the identity alignment between the reference image Iref and the subject's 's appearance in the video. We measure this by first detecting all N subject regions [P.} P f in the video and then calculating the average cosine similarity between the feature embeddings of Ires and each P. The difficulty score Dapp is defined as:
Dapp = -1-- Σ N
[F(Let) F(P)) (I)+(/))) -[FF()))))
(2)
where F()) is DINOv2 Oquab et al.
2023) for general subjects and ArcFace (Deng et al. 2019) for faces. This formulation ensures that lower similarity results in a higher score.
Sampling strategy evaluates diffieulty based whether the reference image and video originate from the same clip. We define two strategies: intra-clip sampling (easy, Dsam = 0), and inter-clip sampling (hard, Daam = 1). In intra-clip sampling, the reference I is taken directly from the video Vgt, ensuring high visual consistency (lighting, background). Conversely, inter-clip sampling pairs the video with a visually similar reference from different clip, forcing the model handle greater variation and thus posing a more challenging learning task.
Finally, obtain a single, comprehensive difficulty metrie for each training sample, we compute weighted sum of the scores from the three cues: Doveral: = >sabDrab + >opppaap + Asam Deams where the Asub. Aapp, and Asam are trade-off parameters that are determined empirically. During model training, we implement our curriculum by imposing a difficulty threshold and only samples where Doverolt I T are used for training. As the training progresses, the threshold is increased in a multi-step schedule, progressively introducing more challenging examples for training. We pre-sort the training data according to the difficulty score Doveroll and derive the difficulty threshold from the statistical quartiles of the score distribution. Following the ratio of 4:2:1:1 1:1, we partition the total training steps into four phases. At the transition of each phase, T is stepped up to the next quartile. After each update point, the threshold remains fixed until the next scheduled change.
3.3
TEMPORAL-AWARE ACTION MODELING
3.3.1 TIMELINE ANNOTATION CONSTRUCTION
Modern text-to-video and subject-to-vidde datasets normally construct the training data as the pair of text-video or the triplet of text-video-referenee and the granular timeline information of actions is scarcely provided. The lack of temporal annotation could limit the model capacity to characterize sophisticated time-related action generation (Zhang et al., 2025a] Qiu et al.] (025). In the scenario of personalized video generation, exploiting additional timeline information in training can further precisely regulate the fine-grained sub-action generation, facilitating the synthesis of more natural motion dynamic. To obtain such time-aware information for training, we sample a subset with 1.2M subject-to-vide videoo pairs from the ReactID-Data -Dta, and further construct the detailed timeline annotations for each video.
Specifically, we devise an ensemble approach that combines the vision language model (VLM), i.e., 2.en2.55VVL722 (Bai et al.] (025). with two visual temporal grounding models UniMD ZZnng et al.) 2024) and TFVTG (Zheng et al., 2024) for timeline annotation construction. The VLM is responsible for initial temporal localization of events, and the two grounding models provide addi- tional two sets of timestamp based on the initial captions generated by Qwen. Finally, we exploit another VLM, i.c., InterVideo2 (Wang et al. 2024a) as a scoring model to select the best candidates from the three annotations. Please refer Appendix B.2 3,22fo for more details about the timeline annotation.
3.3.2 TEMPORAL-AWARE SUBJECT INTEGRATION
The overview of our ReactID is illustrated in Figure 3 To handle the subject timeline integration in diffusion, we introduce novel subject-synchroneett i., subject-aware cross-attention and temporally- adaptiie RoPE, in DiT blocks.
module which consists of two specific designs,
Subject-Aware Cross-Attentionn For the
module as depicted in Figure 3 we exploit an attention-based mask predictor to estimate the mask for each subject, and further employ the masks to associate subject-relates video latents and timeline prompts in cross-attention. In detail, first compute the cross-ates aess-attention map between reference image tokens and video tokens. Then, we feed the attention map into MLP for mask prediction for each subject, which is supervised by the ground-truth subject mask using a focal loss. To enhance the accuracy of subject mask prediction,
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Figure 3: An overview of ReactID framework for personalized vidco generation.
we average all results from last five DiT blocks to obtain final subject masks. Next, we implement a label binding mechanism to establish explicit correspondence between subject-related video tokens and their sub-action timeline prompt in cross-attention. Specifically, we assigned a unique numerical label for subject-related video tokens located in the mask, and the corresponding timeline prompt receiving the same label. For instance, video tokens of "woman in red" receives label a, while video tokens of "woman in purple" is assigned label B3. Video tokens falling outside all subject masks are classified as background and assigned a distinct label that differs from any subject label. Given the assigned labels, a label-dependent phase modulation is proposed to facilitate video tokens to primarily focus on their corresponding subject's timeline while maintaining global interaction in cross-attente antssantt The phase adjustments on both the query 9: (i.e, video tokens) and the key k, (i.c., timeline prompt embeddings of cach sub- sub-action) are formulated as follows:
q. = q:ett k2 = k,e's"0
where 4 and In denote the assigned label value, and 6g is the base angle. Such phase adjustment ensures that the attention bias q. k3 is maximized when label 4 and l match.
The subject-aware cross-attention guarantees that the video tokens with matched subject labels pre- cisely attend to the timeline prompts at spatial level. The tokens with matching labels can be well- aligned, and those with mismatched labels are softly separated, ensuring the textual context embed- ded in the timeline annotations are correctly routed to the corresponding subject.
Cemporally-Adaptivy RoPE. In addition to spatial-level attention design to enhance subject and sub-action association, the precise timeline-aligned personalized video generation also necessitates the subject-action correlation modeling in temporal dimension. Vanilla temporal RoPE (Rotary Position Embedding) in attention exploits the absolute frame indices to construct the embedding, implicitly assuming that all sub-actions have the same duration. Nevertheless, the durations of sub- actions typically vary, and directly applying vanilla RoPE will lead to attention bias misalignment near the transition boundaries between two adjacent sub-actions. As demonstrated in Figure (3] the distance between the frame I and the midpoint of long sub-action is more than that between / and the midpoint of the subsequent short sub-action. Thus, f will perceive more attention bias to the midpoint of the short sub-action than the long sub-action when using vanilla RoPE. The allocation of attention bias is not reasonable since belongs to the long sub-action, and this will lead to inaccurate temporal modeling and abrupt transitions. To alleviate this, we propose cmporally-adapttin that rescales the temporal axis when constructing the temporal RoPE, which is invariant to the du- ration of various sub-acti actiin. Taking the frame index of that locates in n-th time interval pstart pand) as an example, the rescaled index f' is formulated as:
RoPE
(f - ( 1/ )_an)) T ++ (n - 1)- s.t. start S S pnnd
(4)
where T a pre-defined constant that rescales each sub-action into uniform temporal length.
Inference with Single Prompt. In addition to the personalized video generation using the structured timeline inputs, our ReactID can be readily extended for video generation given a single natural prompt. To enable this feature, we leverage LLMs to function as automated temporal planner, which converts the single prompt into the timeline formats. The input prompt is first expanded into a detailed global description, and then decomposed into sequence of distinct event captions, assigned with a plausible time interval for each sub-action. The obtained results are finally formed as structured timeline for ReactID to generate videos with realistic actions and identity preservation.

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Table 1: Quantitative comparison of different approaches the personalized video generation task metrics: Aesthetics, Motion Smoothness, Motion Amplitude, Face Similarity, GmeScore, NexusScore, and NaturalScore. The berf-perfonning result is highlighted in bold, and the second best is underlined.
Method
Aes.t
M. Smo.t
M. Amp.1
FaceSim?
Gine.
Nexus. 1
Natural.
Total.
OpenS2V-Eval: Single Domain
VACE-PL3B
48.93%
95.68%
36.24%
66.85%
49.20%
VACE-L 1.3B
49.41%
95.42%
22.51%
22.37%
70.87%
38.34%
68.33%
51.13%
Phantom-1
49.00%
93.70%
37.72%
66.76%
54.50%
ReactLD
49.79%
96.25 %
38.21%
40.80%%
71.16%
39.85%
70.29%
56.04%
OpenSZV-Evat
Domain
ID An-Anmattr
42,03%
94.69%
33.54%
31.56%
52.91%
56.11%
49.75%
ConsisID
41.77%
79.83%
37.99%
55.83%
EchoVideo
39.93%
77.96%
35.58%
48. 65%
68.40%
62.22%
56.36%
FantasyID
45.60%
85.44%
23.41%
32.48%
72.68%
62.36%
54.33%
VACE-P1.3B
51.91%
95.801%
8.78%
19,98%
73.27%
65.8318
$3,97
VACE-1.3B
53.18%
95.84%
16.87%
22.29%
73.61%
65.28%
54.90%
Phantom-1.33
50.80%
92.022%
72.17%
65.83%
60.00S
Concat ID N
43.13%
85 86%%
17.19%
50.05%
71.90 9..90%
68.47%
59 85%
52.02%
96.46
40.79%
73.96
ReacdDD ReacdD-EEval.Ce Eval-SEQ SEQ
VACE-PI.3R
46.59%
90.07%
15.81%
13.52%
69.83%
34.22%
60.68%
45. 66%
VACE-1.3B
47. 92%
92.73%
23.65%
19.48%
70.02%
35. 18.1
64. 78%
48. 58%
Phantom-L_3B
45.68%
92.16%
3
62.30%
51.40%
ReactID
49.11%
94,58% %
39.46%
38.20%
71.23%
37.13%
68.69 %
54.42 %
EXPERIMENTS
4.1
EXPERIMENTAL SETTINGS
Datasets and Metrics. OpenS2V-Eval (Yuan al. 2025a) fine-grained benchmark that con- tains 180 subject-text pairs focusing on the model's ability to generate subject-consistent videos with natural subject appearance and identity fidelity. Moreover, better assess the generation of com- plex, multi-action sequences, we introduce an evaluation set, React[D-Eval-SEL It comprises 120 subject-text pairs, encompassing 40 human identities and 20 animals. The text prompt in each pair specifies sequential actions for the subject. Our evaluation methodology adheres to the OpenS2V- Nexus Yuuan et al. (025a), employing seven scores to assess the generated videos: Aesthetics, Mo- tion Smoothness, Motion Amplitude, Face Similarity, GmeScore, NexusScore, and NaturalScore. line with the OpenS2V-Nexus protocol, all scores are normalized and then aggregated into a final TotalScore, with both processes following the procedures specified therein.
(Wan et al. 2025) as our foundational model. Our ReactID model is trained for 10k steps with global batch size of 32, utilizing the AdamW optimizer with a learning rate of 1 x 10 - and 500 warmup steps. All training is conducted on 8 NVIDIA A100 GPUs, and consumes total 1, 464 GPU hours. The averaged time of each optimization step is 65.8 seconds. Regarding the curriculum learning, we initialize the difficulty threshold 5 at 0.53 and update it 0.67, 1.44, and 1.84 at training steps 5k, 7.5k, and 8.75k, respec- tively. The ratio of inter-clip references within the total training samples is dynamically modulated by т, adjusted to 0%, 0%, 11% and 33% the corresponding update points. Since our proposal is insensitive to Asub. Xapp. and Agam. we empirically set them to 0.5, 1. respectively, according to the relative importance of each dimension. For inference, we use denoising steps and classifier- free guidance (CFG) scale of 5.0. Generating a 5-second video requires about 316 seconds. An LLM (Achiam et al. 2023) employed to convert prompts into a timeline format when necessary.
Implementation Details. We employ
4.2 EVALUATION OF REACTID
Quantitative Results. In Table [ we first present an analysis of our proposed ReactlD compar- ison with several open-source approaches on the OpenS2V-Eval across two scenarios: the Single Domain, which consists of test samples each containing a single subject drawn from various cate- gories, and the Human Domain, which is composed solely of subjects from the 'person' category. All the mentioned baselines utilize foundational models with complexities comparable to ours, pro- viding fair basis for evaluation. In general, ReactID outperforms baselines across most metrics and achieves the highest overall scores in both domains. In the Single Domain, ReactID achieves superior performance across most metrics, including NexusScore, GmeScore, NaturalScore, and To-

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Published as a conference paper at ICLR 2026
Satperid
Pooppt
Subjectsi
Promptt
shamssyu yestare rowars the canevo
seales
holking lastop while clsrry restog her are be dvaalerr...
Mane.
Sa Spp Sppicae
Figure 4: Qualitative comparisons of vidcos produced by different approaches on the OpenS2V-Eve Eval dataset.
Satjectid
dog aperssin the bonchar and parss of the plash bear with curisuit...mmp Jerpo up onto bench piblo up jullen lea/ romoiin
step too thes polder sbor of The gray- bnirre man wallo froed, londing cay sermnn follon... salted bratl
Figure 5: Comparisons of vidcos produced by different approaches on the React EealI--Eva--EE datasct.
Moren
def
Figure 6: Examples of video generation conditioned on multiple subjects and their respective timelines.
talScore. This highlights its capability to effectively harmonize subject consistency with action real- ism. However, it scores marginally lower than Phantom- on FaceSim. We attribute this deficit to ReactID generating more dynamic and natural movements (higher motion amplitudessmoothnss)) which may introduce motion blur, thereby reducing facial similarity to the reference image. In the Human Domain, ReactID achieves TotalScore of 17%, surpassing the strongest of the seven baselines, Phantom-1.3B. by 2.17%. also secures the top position in four out of the six avail- able metrics (NexusScore is not applicable in this domain). Similar to the results in the Single Domain, ReactID achieve a competitive FaceSim score of 44.08% with a high Motion Amplitude of 40.79%. To further demonstrate ReactlD's advantages in generating videos with sequential actions, we conducted experiments on the ReactlID-Eval-S approaches supporting both human and animal subjects. The results show that ReactD outperforms all baselines across most metrics. Notably, its TotalScore is 3.02% higher than the best competitor, which highlights ReactID's capability for sequential action generation.
We compared our model with three baseline
Qualitative Results. We proceed with a visual examination of the generation quality, comparing ReactID against four basclines (VACE-PI.3) VACE-1.3B. Concat-ID- 1.3B) on two input subject-text pairs. As illustrated in Figure g while all approaches successfully generate videos featuring the subjects from the reference images, our model produces the most plausible visual quality and naturalistic motions. Taking the left case an example, ReactID adeptly generates a man a snowy park giving a thumbs-up gesture. In contrast, the baseline VACE- P1.3B and VACE-I.3B struggles preserve the subject's key visual attributes from the reference image, leading to a degradation in subject consistency. While Phantom- the subject's 's appearance, it fails to follow the text prompt and its video quality is compromised by "copy-pastee artifacts. These artifacts manifest as subjects appearing static or being unnaturally duplicated across frames, which results in visually implausible motion sequences.
and Phantom-
-.ittomm-1.3B. 1.3B effectively replicate
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Published as a conference paper at ICLR 2026
Table 2: Comparisons between Re- Table 3: Results label binding in
Table 4: Ablation results on Tem- porally Adaptive RoPE in terms of strategy and rescale length.
actlD and four basclines with gard human preference.
Subject-Aware Stoss-Attention.
Method FaceSimt Gme.t Natural
Method
SCt
AN
TVA
OVQT
Label Strategy
Method
CLIP-T
TVAT
TCT
ConsisID
1.97
1.66
2.13
1.84
Adaptive 48.42%
70.16%
66.24%
Attention Mask
0.241
2.62
2.25
Concall-
3.06
2.10
2,91
2.74
Uniform 49.11%
71.23%
68.69%
Vanilla RoPE
0.242
2.67
2.35
Phantom-1.38
3.46
2.97
2.88
3,25
Label Value Selection: (a. 8)
TARoPE T=
0.246
2.70
2.36
V.CE-L.3B
3.23
3.20
3.08
3.37
(0, 1)
49.34%
70.08%
68.51%
TARoPE T=4
0.247
2.69
2.35
ReaciLD
3,90
4.00
3.47
3.42
(2, 20)
49.11%
71.23%
68.69%
TARaPR Tw2
0.247
2.71
2.38
Table 5: Ablation study of the difficulty-awarr curriculum learning in ReactD.
Method
Aes.t
M. Smo.t
M. Amp-t
FaceSimt
Gme-t
Nexus:t
Natural.1
Total.
Full Data Training
48. 19%-
92.64%
37.22%
34.89%
69.169%
33.10%
65.13%
54%
Random Expansion
48.46%
92.56%
37.46%
34.74%
69.11%
33.24%
64.36%
51.39%
Curriculum Dar
48.43%
93. 03.68%
35,27%
38.03%.
70.68%
34.33%
68.01%
51.35%
Curriculum w/o Dapp
48. 98.96
94.07%
38.84%
37.18%
71.16%
35 89%
68. 14%
53.76%
Curriculum w/o Dem
48.57%
93.26%
37.87%
35.76%
70.41%
35.06%
66.43%
52.68%
Full Curriculun
49.11%
94.58%
39.46%
38.20%
71.23%
37.13%
68.69%
54.42%
Evaluation of ReactID-Data.
comparative analysis of our ReactID-DData against OpenS2V. 5M (Yuan et al. (025a)), which includes evaluations of data quality, human preference, and model training impact, is detailed in the supplementary materials.
User Study. We further conducted user study to evaluate whether videos produced by Reac- tID better align with human preferences compared to those from four baseline methods: ConsisID, Concat-ID-Wan-Adae
(ConcatII--WA) Phantom-1.3B, and VACE-1.3B For this study, we ran- domly sampled 50 subject-text pairs from OpenS2v-Eval to serve as test cases. We recruited 25 evaluators (17 males, 8 females) with diverse educational backgrounds and ages. Each participant was asked to rate the generated videos on a 5-point Likert scale across four critical dimensions: (1) subject consistency (SC), (2) action naturalness (AN), (3) text-video alignment (TVA), and (4) overall visual quality (OVQ). To ensure a reliable assessment, the final score for each dimension was computed by averaging ratings across all participants and test cases. As presented in Table 2 ReactID demonstrates consistent superiority over all baselines across all four evaluated dimensions. This study thus validates the effectiveness of ReactID from a human perceptual standpoint.
4.3 ABLATION STUDY ON REACTID
In this section, we perform a series of ablation studies to delve into the design of ReactlD for personalized video generation. Note that all results here are evaluated on the ReactID--Fval-SQQ.
Difficulty-aware Curriculum Learning. We first delve into the effectiveness of our difficulty- aware curriculum learning for personalized video generation, and report the performances in Table When progressively expanding the training set without using any difficulty guidance (i.e., Random Expansion), the model achieves 51.39 31.39% TotalScore, which is lower than that of training with all data (i.e, 51.54% achieved by Full Data Training). To ablate each component of difficulty-awan curriculum learning, we conduct experiments by discarding any one of the three difficulty cues, and the performances are superior the baseline of curriculum. By further exploiting all the three cues in curriculum learning to avoid both overfitting to easy samples and underfitting on challenging ones, ReactDD with Full Curriculum learning shows the best 54. 64.42% TotalScore.
Subject-Aware Cross-Attention. Then, we investigate different technical choices of label binding strategies in subject-aware cross-attention of subject synchronized module. Table B details the per- formances of two runs, i.e., adaptive labeling and our uniform labeling. Adaptive labeling assigns varying label values based on predicted mask probabilities. The pixels with higher probability re- ceive larger values, while pixels with lower probability are assigned smaller ones. Instead, Uniform labeling assigns fixed label value to all pixels within each predicted subject mask. Counterintu- itively, the uniform labeling strategy outperforms the adaptive counterpart across all metrics. We speculate that the results may be caused by the misalignment between the attended region derived from mask probabilities and the region essential for personalized video generation. Compared to potentially erroneous adaptive labeling. uniformly applying the same label to all tokens in the mask can be more robust attain better identity preservation. For the uniform labeling, we further explore the influences of the labeling value selection. As indicated by the results, exploiting more distinct label values (i.e., = 2 and 3 = 20) for different subjects empirically achieves better performances.

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Published as a conference paper at ICLR 2026
Table 6: Ablation study of de- signs w.r.t. timeline.
Table 7: Annotation quality timeline and mask.
Table 8: User pref-
Table 9: Human Distin- guishability of timelines from Exper//LI.M.
erence of timelines
Model
Data
TotalScore
Timeline Method Fl
Mask
from Expert/LIMM.
Base
OPenS2V.5M
50.37%
Method P. Rate
Dims
LLM
Expert
PD
LLM
Expert
Basc
ReactID
52.14%
TFVIG 0.57
IJSA
15%
AO
46%
54%
GT
IntemVI. 0.63
Base
ReactTD
52.21%
Qwen
0.71
SAM
31%
I.C.
48%
52%
1.M
52.8%
47.2%
ReseiI
ReacilD
54.42%
ReaetID
0.78
ReactIL
54%
53%
47%
Expert
47.8%
52.2%
Temaprally-Adaptive RoPE. We also explore different approaches of the RoPE design in temporal dimension for temporal alignment in ReactID. Two additional runs of attention masking and vanilla RoPE are conducted for comparison. Attention masking directly applies the temporal mask on video tokens to enforce the timeline prompt only attend to the corresponding frames, and there is no temporal RoPE implemented. Vanilla RoPE assigns original temporal RoPE to video tokens without temporal axis rescaling. In addition to utilize the metric of CLIP-T, we further leverage two measures from VideoScore (He et al. 2024a), i.e., Text-to- -Vdeo Alignment (TVA) and Temporal Consistency (TC) for temporal alignment evaluation. The performances of the three runs are sum- marized in Table 4 Compared to attention masking vanilla RoPE that totally abandons RoPE or takes no account of event duration variances in temporal RoPE modeling, our temporally-adaptive RoPE dynamically adjusts the attention bias according to the sub-action duration in RoPE formula- tion, thereby achieving the best performances in temporal alignment. We also detail the performance comparisons by using different T', In practice, our ReactID yields the best results when T is 2.
Data Annotations. In Table 6 We quantify the impact of annotations in ReactID-Data by eval- uating four model-data combinations on ReactID-Eval-SEQ, Base refers to the ReactID model without timeline-specific modules, while ReactID denotes the ReactID-Date without timeline annotations. Base+ReactID outperforms Base+OpenS2V-5M. confirming that our data pipeline drives generation quality. Notably, naively incorporating timeline information as enhanced prompt (Base+ReactlD-Datte yields negligible gains, suggesting the Base struggles to interpret complex temporal contexts from text alone. In contrast, our full model ReaculD+ReactlD-Daie achieves the optimal performance, indicating that our timeline-related design is crucial to fully leverage the timeline information for accurate sub-action generation.
Subject Mask. To verify our carcfully-min minted subject mask generation, we conduct a user study on three methods: LISA, vanilla SAM and our proposal. For each mask, participants select the best one. As shown in Table 7
ours achieves the highest preference rate. Details are in Appendix E.
Timeline Annotation. We compare the quality of timeline annotation generated by ReactID and other methods via user study. As indicated by Table the F1 score attained by ReactID is high than those of other methods, verifying our better quality. Please refer to Appendix E.2 for more details.
LLM-PPanned Timelines. To evaluate the quality of our LLM-planned timelines, we conduct two user study, i.e., user preference (Table across three dimensions (Action Order, Logical Coherence and Motion Naturiness) and distinguishability (Table between human expert and L.LM. The qual- ity of videos generated on LLM-planned timelines are comparable to that of using expert-writing timelines, verifying the reliability of ours. More details are in Appendix E.3
5 CONCLUSIONS
We have presented ReactID, a comprehensive framework devised to address the fundamental tension between identity preservation and action realism in personalized video generation. We identified and tackled the root causes of this problem, i.e., imprecise data alignment, unstable training, and inade- quate action modeling, through a holistic approach. Our solution integrates three key components: a high-quality dataset (ReactDD-Data) with reliable identity-vidoo correspondence, a progressive training curriculum for stable learning, and a novel timeline-basedd conditioning mechanism for fine- grained action control. Extensive experimental results validate that ReactID achieves new state-of- the-art performances, excelling in both maintaining subject identity and generating natural, complex motions. Beyond its immediate performance gains, we believe this work makes two pivotal contri- butions to the field. First, the release of the ReactID- dataset provides a critical foundation for future research, offering benchmark for precise identity-action modeling, Second, our timeline- based paradigm demonstrates a scalable and intuitive path toward controllable, multi-action video synthesis, significantly broadening the potential applications of personalized generation.
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ACKNOWLEDGMENTS
This work was supported by the Key Science Technology Project of Anhui Province No. 202523009050002 and the National Natural Science Foundation of China under grants 62472399.
ETHICS STATEMENT
The primary of this paper is to introduce personalized video diffusion model for reference subjects to perform desired action while maintaining the identity across frames. this work, we construct ReactIID-Data from publicly available academic datasets. To ensure ethical compliance, strictly adhere to licensing policies and respect privacy rights. We only utilize publicly accessible videos that are either explicitly licensed for research use or fall under fair-use considerations. No copyrighted content is redistributed or modified in violation of licensing terms. We uphold the highest ethical standards in the construction, use and dissemination of data.
REPRODUCIBILITY STATEMENT
The paper provides necessary implementation details required to reproduce the main experimen- tal results, including dataset processing procedure in Section 3.1 and Section model architectures, training protocols in Section 3.2 hyperparameters and experimental settings in Section 4.1 Addi- tionaly, will release constructed dataset for academic research, ensuring faithful reproduction of our core results.
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A APPENDIX: CLARIFICATION OF USING LLMS
In the period of paper writing, only exploit the Large Language Model (LLM), i.e., GPT-4, for word polishing to improve the presentation quality. No LLMs contributes to the core scientific methods, technical contents, or any substantive research contributions.
B APPENDIX: DETAILS OF REACTID-DATA CONSTRUCTION
B. VIDEO DATA PREPARATION
We first collect pool of 20 million videos from publicly available video datasets such as HD-VG- 130M (Wang et al., 2025) and OpenHuman (Li et al. 2025a). To detect the scene transition and partition long videos into single shot video clips, we utilize the PySceneDetect [Team] 2024b to coarsely find the cut points according to color and brightness variations. Then, we detect the po- tential semantical transition points in videos using the cosine similarity of DINOv2 (Oquab et al. 2023) features between adjacent frames. Video clips longer than 5 seconds are saved to files using H.264 codec. To remove the text overlays, subtitles, and watermarks from videos, we use Pad- dleOCR (Team] 2024a) to find all text regions throughout the video, then crop it to the largest possible text-free area. Next, we apply a suite of automatic video quality assessment methods to evaluate each video clip across multiple facets, including aesthetic quality, technical quality, and motion dynamies, and filter out the clips with low scores in any of these dimensions. To provide rich, subject-centric textual captions which are crucial for personalized video generation, we em- ploy an off-the- the-- vision-language model (Qwen2.5-VI. (Bai et al. 2025)), which is prompted to analyze each subject appearing in the video and describe their physical appearance, corresponding actions, and background environment (Liu et al. 2025a). According to the descriptions, vidco clips that possibly contain excessive number of subjects (e.g., crowd of people line of cars) are filtered out, ensuring that the remaining videos clear focus on a limited number subjects.
Video Scene Cut. To prepare high-quality video clip pool, we employ systematic two-stage strategy to partition long videos into semantically coherent clips. The process begins with a coarse, content-aware segmentation using PySceneDetect. Subsequently, we refine these initial segments with DINOv2, inserting additional cuts at temporal locations where the cosine similarity between adjacent frame features drops below predefined threshold. Finally, we retain all clips with durations above 5 seconds, which are well-suited for personalized video generation model training.
Video Quality Filtering. We implement multi-dimensional video quality scoring mechanism:
(1) Aesthetic Score: We utilize an improved aesthetic predictor to evaluate the visual appearance of each clip, only retaining those with high aesthetic scores.
(2) Motion Score:
To distinguish deliberate subject motion from incidental camera movement, we compute optical flow using RAFT model. The foreground and background flows caleu- lated separately, and the final score is based on the foreground flow, effectively isolating and quantifying subject-specifie motion.
(3) Content Purity:
We employ PaddleOCR detect and remove frames containing overlaid text watermarks. Frames with OCR confidence scores above 0.7 are flagged, and the clip is cropped to retain only clean visual content.
(4) Technical Quality: We assess the technical quality using DOVER model for data filtering.
Video Captioning. Effective captions for personalized video generation must be subject-centric, de- tailing the reference subject's appearance and motion while incorporating relevant contextual cues from the background environment. To meet this requirement, we employ Owen2.5-VL-7B to gen- erate rich descriptions. Moreover, filter out any captions containing plural entity nouns. This operation not only maintains a clear focus on limited number of subjects, but also facilitates con- sistency between the used textual prompts during training and inference.
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B.2 DATASET ANNOTATION
We further construct the timeline annotation based on the collected video data. There are four main steps in this data processing stage, i.e., entity label extraction, subject detection and segmentation, face detection and segmentation, and timeline prompt captioning.
Entity Label Extraction. To accurately identify distinct entities, we adopt a hybrid strategy that leverages both textual and visual information for entity labeling, mitigating the risk of error propaga- (I) For object label extraction, texl-based approach is exploited, which first pre-defines a taxonomy of common objects, and then use a NER model, i.e., SpaCy (Hon- nibal & Montani 2017), to parse the captions and extract relevant entity labels in the taxonomy. (2) To deal with human and living subjects, we shift to iisionbbased method to accurately discriminate entities with similar semanties. We leverage Qwen2.5-V1 VI. model to analyze the video content and extract unique, descriptive entity labels. The potential inaccuracies caused by the generated captions can be alleviated, providing a more robust foundation for subsequent subject and face detection.
tion from the video recaptioning.
Subject Detection and Segmentation. Conventional CLIP-based detectors like Ground- ingDINO (Liu et al., 2024) can fail to distinguish semantically similar concepts due to the limi- tation in understanding. To overcome this, we leverage an MLLM-basee detector, i.e., Florence- 2 (Xiao et al. (024), for highly accurate subject detection. To further verify label-subject corre- spondence, we perform a subsequent classification step using SigLIP (Zhai et al., 2023) post- verification (Zhang et al.) 2023). Finally, we apply SAM22.1 (Ravi et al.) (0024) to generate seg- mentation masks for the detected subjects. These masks are further refined using morphological operations to improve boundary precision.
Face Detection and Segmentation. To detect high-quality human faces, we first utilize Insightface toolbox on video frames with FPS sampling rate. Then, we apply Non-Maximum Suppression with a 25% overlap threshold to filter duplicates and remove outlier bounding boxes. For each unique person, the detected faces are ranked by confidence, and five representative frames are uniformly sampled. Finally, SAM2.1 model employed to generate precise masks for the frame with highest detection confidence, which are subsequently refined using morphological operations as well.
Timeline Prompt Captioning. We devise an ensemble approach that combines the vision language model (VLM), i.e., Swen2.55VLL-22 (Bai et al., 2025), with two visual temporal grounding models UniMD ZZng et al. 2024) and TFVTG (Zheng et al., 2024) to obtain both timestamps and the descriptions of sub-actions Tan et al., 2026). First, instruct Qwen2.5-VV VL to identify and caption each sub-action in the video based on meaningful action changes and detect the timestamps of each sub-action. Next, utilizing these captions of each sub-action as queries (Zeng al.] (025), employ UniMD and TFVTG to independently re-localize the temporal boundaries. This process yields three distinct sets of candidate annotations per sub-action: one originating directly from the Qwen2.5-VL LL and two derived from specialized visual temporal grounding models. Finally, we employ InterVideo2 (Wang et al. 2024a) as a scoring model. For each candidate, we utilize the video and text encoders of Inter Video2 to extract visual and textual representations from the video segment and the sub-action caption, respectively. We then calculate the cosine similarity between these embeddings as alignment score. The candidate timeline annotation (comprising both the timestamp and caption) with the highest alignment score is selected as the final detection for each sub-action.
C APPENDIX: STATISTICAL ANALYSIS ON REACTID-DATA
Figure 7 further demonstrates statistical analysis on our proposed ReactID-Data. We measure the video dataset from the perspectives of duration, resolution, word sequence length of prompt, aesthetic score, and motion score. The word cloud of entity label is also depicted show the major components of the detected subjects. According to the statistics, the duration of more than 97% videos is longer than seconds, and all videos have a resolution of at least 720P, guaranteeing the high video quality for model training. Meanwhile, around 94% videos having 2 50 words in each text prompt indicate that ReactID-Data can provide precise and comprehensive textual descriptions for videos to enhance video generation learning. The distribution of motion score is very uniform, reflecting that the training data can cover both relative static and dynamic scenes. In the word cloud, the entities such human (e.g., woman and man), animals (e.g., horse) and common objects (c.g., sofa and mug) have been included. The reasonable data composition of ReactID- DData highlights the merit for precise personalized video generation modeling.
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215
1541
UMMP
5568
925,
SN
36-55
Video Duration
(b) Video Resolution
Aesthetic
31.41%
13.8%
O
O
138%
13.4%
65%
O
6.1%
2300
O
158-281
1.54%
13
15.0%%
0.-41 61.1%
(d) Motion Score
Prompt Range
(1)
Figure 7: Statistical analysis on our ReactID Dattt.--Dat.
Figure 8: Examples of data items in the React|D-Darl
D APPENDIX: OVERALL ARCHITECTURE OF REACTID
Given the subject-to-video pairs with timeline annotations, we proceed along the direction of typical personalized video generation DiT framework for subject condition injection, and further consider the timeline information learning. We choose the 1-1.3B (Wan et al.] (025)) as the base ar- chitecture of ReactID. To preserve the intrinsic capabilities of the original DiT blocks while enabling personalized video generation, we introduce a dedicated conditioning branch for reference image feature encoding. This conditioning branch consists of several reference blocks that are struc- turally identical to the base DiT blocks. We first apply the VAE on the reference images to obtain the image latents, which are treated as the condition and further fed into the condition branch. The extracted middle-level features from the image condition are then injected into the video diffusion model via Res-Ture des-Tuning (Jiang et al. 2023) manner. To handle the subject timeline integration dif- fusion, we introduce a novel subject-synchronizee module which consists of two specific designs, ie, subject-awand awwae cross-attention and semporally-adaptio sures robust subject-action association in multi-subject scenarios by accurately binding each action to its corresponding subject. The latter precisely anchors each sub-action within the timeline to its designated timestamps, enhancing temporally alignment and semantically coherence.
RoPE, in DiT blocks. The former en-
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E APPENDIX: MORE EVALUATION
E HUMAN EVALUATION ON SUBJECT MASK GENERATION
To obtain more accurate subject mask for our ReactID training, we improve the performance of vanilla SAM (Ravi et al. 2024) segmentation pipeline by leveraging additional visual foundation models and VLMs. First, we utilize SigLIP Zhai et al. 2023) to filter out bounding boxes with low image-text similarity to ensure that SAM receives correctly matched subject region for segmentation. Then, we apply iterative morphological operations, including dilation and hole filling improve mask continuity. Finally, we employ Qwen2.5-VL-33. to score and verify the final mask quality.
To validate the effectiveness of our deliberate design, we conduct human evaluation on 200 ran- domly sampled cases. We recruit 20 participants from diverse backgrounds to perform a blind, side-by-side comparison across three segmentation approaches: LISA (an MLLM-based segmenta- tion method), vanilla SAM and our method. For each sample, participants select the best mask. As shown in the right part of Table 7 our pipeline achieves the highest preference rate, demonstrating its effectiveness in reducing segmentation failures in challenging real-world scenarios.
E.2 HUMAN EVALUATION ON TIMELINE ANNOTATION
We conduct a user study to compare the quality of timeline annotation between our ReactID and other VLMs specialized models. Particularly, 20 participants are invited from diverse academic and professional backgrounds. We randomly sample 200 timeline annotations from our dataset for the human evaluation. Each participant instructed evaluate every annotation generated by React]) and other VLMs along two dimensions, event and timestamp accuracy. According to the evaluation results, we classify the timeline annotations into three categories to compute the F1 score.
False Negatives: When the annotation pipeline misses a salient event present in the video.
False Positives: When the detected event does not exist in the video or the timestamp does not match the event boundaries.
True Positives: When both the caption and timestamp are correct.
As shown in the left part of Table 7 our ReactlD achieves a superior F1 score of 0.78, supported by a inter-annotator agreement of 0.86. The Qwen2.5-V VI. follows as the second-best annotator with an Fl score of 0.71. The results reflect the robustness of our timeline generation pipeline, and the precision the timeline annotations facilitate timeline-based generation model training.
E.3 HUMAN EVALUATION ON LLM-PLANNED TIMELINES
To evaluate the quality of our LLM-planned timelines for personalized video generation, conduct two types of human evaluations, i.e., user preference and distinguishability, between the timelines generated by human expert and LLM. Specifically, instruct 20 participants evaluate 200 videos generated on the human expert-annotatee and our LLM-ppanned timelines across three dimensions: Action Order (AO), Logical Coherence (LC), and Motion Naturalness (MN).
User Preference. The results in Table g demonstrate comparable human preference between the generated videos using such two kinds of timelines. Notably, videos conditioned on LLM-planned timelines even slightly outperform expert annotations in terms of Motion Naturalness, suggesting superior fluidity in the generated content.
User Distinguishability. Table 9 further presents the confusion matrix of the human distinguisha- bility test. As observed, evaluators could not reliably differentiate between the numan-annotater or our LLM-based sources. The evaluation indicate that our timeline annotation planned by LLM is reliable and aligns closely with the human expert logic.
E.4 COMPARISON BETWEEN OPENS2V-SM AND REACTID-DATA
We conduct comprehensive comparison between recent public OpenS2V-5M Yuan al. 2025a) and our proposed ReactID-Data, First, we randomly select 2, 000 samples from both OpenS2V- SM and ReactIDD-Data, then employ CLIP to classify subject images against their corresponding
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Prefereace Rate
1005
100%
$2.4%
50%
50%
Table 10: Comparisons of ConsislD trained on different datasets. Models are evaluated on the ReactID-Eval Eval-SEQ dataset.
63.0%
62%
37.0%
Training Data
FaceSim
Gme.
Natural,
20%
OpenS2V-5M ReactID-Data
45. 12%
72.80% 73.20%
0%
45.41%
64.77 %
RexiiD- Dat Figure 9: Classification and preference comparison be- tween OpenS2V-5M and our ReactDD-Ded Data.
OpenS2V
ReactID OpenSZV
Quantitative comparison of different approaches our ReactID-Eval-SFQ dataset. performing result is highlighted in bold.
Table 11:
The hest-
Method
Acs.t
M. Smo.t
M. Amp-t
FaceSimf
Gme.t
Nexus,t
Natural.1
Total.
Without Pronpt Pre processing
VACE-
47 92%
92.73% 92.16%
23.65% 15.38%
19.48% 37.41%
70.02% 67.35%
35. 18%
64, 78%
48, 58.58%
Phantom-1.38
68%
38.69
62.30%
51.40%
Wich LIM Enkanced Prompty
VACE-1.38
48 20%
92.60% 92.05%
23.45%
19.70%
70.00%
35.30%
64.90%
48.70%
Phantom-1 1,33
45.80%
15.25%
37.20%
67.50%
38. 60.
62.40%
51,30%
With LLM Planned Stractrre Timeline Prompts
VACE- 1.3B
98%
92.78%
23.38%
19.25%
69 99.996%
05.05%
64. 52%
18.25%
Phantom-1.38
45.75%
10%
15.18%
37.05%
67.45%
38.50%
05%5%
50.95%
ReactLD 49.11%
94.58%
39.46%
38.20%
71.23%
37.13%
68.69 %
54.42%
entity labels. We compute Fi-score to evaluate classification performance. As shown in left part of Figure ours achieves significantly higher F1-scores, demonstrating the superiority of our data construction pipeline. When handling complex scenarios containing entities with high visual or se- mantic similarity, our pipeline can maintain precise entity labeling and accurate subject extraction. However, the commonly adopted text-based entity extraction approach (i.e., Grounded-SAM) in OpenS2V may suffer from omissions and misclassification. To further validate the labeling qual- ity of the two measurements, we conduct human evaluation. Specifically, we randomly sample 2, 000 valid instances from the HID-VG-130 dataset and process them using both the conventional data pipeline and our data pipeline. 25 participants are recruited to evaluate the quality of both datasets, specifically assessing whether subject extraction is accurate, complete, and consistent with the assigned labels. Each participant selects the better-processed result from the two alternatives. We report the average preference rate across all trials in the right part Figure D There are 63% of participants that favor data processing pipeline, compared to only 37% for the conventional approach. To further testify the quality ReactID-Data, we train ConsisID from scratch under identical setting 500K samples randomly selected from the OpenS2V-5M and ReactID-Data, spectively. As shown Table TO the model trained on ReactID--Data outperforms the model trained on equivalent data in OpenS2 V-5M, confirming that our ReactID-Date facilitates superior model optimization in personalized video generation.
E.5 MODEL PERFORMANCES ON DIFFERENT PROMPT FORMATS
To analyze whether the existing models can be improved through enriched text prompts, we select two top-performing models, VACE-1.33 1.3B and Phantom-1.3B and perform these two methods on the input prompts processed by two augmentation strategies: prompt enhancement and structured timeline prompts. The performances on OpenS2V are illustrated in Table TT Prompt enhancement yields minimal performance gains across all baselines that generate videos without any prompt pre- processing. The results are expected since the majority of prompts in the OpenS2V already consist of detailed, comprehensive captions. Notably, for the models trained without timeline annotations, the structured timeline prompt also fails to deliver significant improvements and, in some cases, leads to performance degradation. This somewhat reveals fundamental limitation: models not explicitly trained on smeline-structurr data possess limited temporal reasoning capabilities, and cannot ef- fectively leverage sequential action descriptions enhance motion naturalness. Without designing
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Figure 10: Visualization of two 30-second long videos generated by ReactlD.
proper temporal modeling architectures, simply reformatting single text prompts into timeline struc- tures may introduce confusion for action modeling in video generation. These findings underscore the importance of architectural design and training paradigms tailored for temporal action modeling, rather than solely relying on input formatting modifications.
E.6 LONG VIDEO GENERATION
Long video generation in personalized video generation generally suffers from identity drift (Chen) et al. [2025a] Luo et al. 2024), minimum or collapsed motion. Although ReactID was not explicilly optimized for long video generation, it can be effectively coupled with an auto-regressive generation strategy generate longer videos while maintaining high identity consistency. Specifically, ReactDD generates the video begment-by-segmen the visual condition for the subsequent one. This iterative process allows to customize videos extending well beyond the standard 5-second duration. As shown in Figure TO we successfully gen- erate long videos in multi-subject scenarios with four and six reference subjects while maintaining the identity of all subjects throughout the video, without significant temporal drift.
(e.g., in 5-second clips), utilizing the preceding segments
E.7 ADDITIONAL VISCALIZATION
We provide additional visualization to further demonstrate the capabilities of ReactID. As shown in Figure IT given predefined timelines in which each subject performs distinct action sequences, ReactID faithfully follows the temporal schedule to generate a series of overlapping or asynchronous actions performed by different subjects. The results highlight the model's 's ability to handle complex temporal dynamics. As shown in Figure 12, when provided with only a single face reference image, ReactID preserves fine-grained identity cues while remaining well aligned with the text prompt. As illustrated in Figure [3] ReactID demonstrates strong robustness and generalization ability multi- subject scenarios. It supports both human and non- non-human entities, including animals and common objects, and enables flexible combinations across different categories. Compared with other models, ReactID effectively mitigates the copy-paste issue and produces consistent subjects with coherent
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and natural actions, striking balance between identity preservation and action realism. Additional results in Figure 14 further validate the versatility and stability of React|D across diverse scenarios involving single subject and multiple subjects. Our model is capable of generating Overall, our model is capable of generating high-fidelity subjects and realistic, natural interactions.
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Figure 11: Additional results of ReactDD conditioned on given timelines.
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Figure 12: Additional comparative results of single-face customization across different methods.
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Figure Additional comparative results of multi-subject customization across different methods
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Subject(s)
Generated Frames
Figure 14: Additional results generated by ReactDD.
23


Reviews:
[{"review_id": "2cr2xXj87S", "reviewer": "Reviewer_PVcC", "summary": "This paper tackles the central trade-off in personalized video generation between identity consistency and action realism. The authors propose ReactID, a framework that coordinates advances in data curation, training strategy, and action conditioning to balance identity fidelity and motion naturalness. They introduce ReactID-Data, a large-scale dataset built via a high-precision pipeline. To stabilize learning, they analyze difficulty along axes such as subject size, appearance similarity, and sampling, and adopt a progressive curriculum from easy to hard to mitigate identity overfitting and copy-paste artifacts. For action modeling, they propose a timeline-based conditioning scheme that augments text prompts with structured multi-action sequences annotated with timestamps, integrated through two components: a subject-aware cross-attention module and a temporally-adaptive RoPE mechanism. Experiments report state-of-the-art performance on both identity preservation and action realism, suggesting the method effectively balances these competing objectives.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The manuscript is clearly written and easy to follow.\n2. The motivation is well defined, pinpointing three key challenges in video customization: inaccurate identity preservation, unstable convergence, and compromised action naturalness.\n3. The work is solid, introducing a new dataset and method components, including Subject-Aware Cross-Attention and Temporally-Adaptive RoPE.\n4. Extensive experiments convincingly demonstrate the effectiveness of the approach.", "weaknesses": "1. Will the dataset, code, and model weights be released? Open-sourcing would significantly benefit community research and reproducibility.\n2. How well does the method generalize to multi-subject customization beyond two subjects (e.g., three or more)?\n3. For multi-subject scenarios, can the approach handle more complex, independent action timelines per subject (e.g., multiple subjects performing distinct actions concurrently)?\n4. The results shown in the paper are about human subjects. Can the method support customizing animals and general objects?\n5. Please consider citing closely related work:\n    - CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition Abilities\n    - ReVersion: Diffusion-Based Relation Inversion from Images\n    - DreamRelation: Relation-Centric Video Customization", "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": "sOBpW30Z5Q", "reviewer": "Reviewer_vCvz", "summary": "This paper targets personalized video generation and argues that current methods face a persistent tension between identity fidelity and realistic action. The authors propose ReactID, a framework that couples a new data pipeline, a difficulty-aware curriculum, and a timeline-based conditioning mechanism. ReactID-Data is built with entity extraction for living and non-living subjects, MLLM-assisted subject detection and verification, and subject masks for faces, aiming to deliver precise subject–video correspondences. The training strategy scores sample difficulty using subject size, appearance similarity, and sampling strategy, and then increases a difficulty threshold over time. The model introduces a subject-aware cross-attention with label binding and a temporally-adaptive RoPE that rescales time within sub-actions so temporal bias aligns with action boundaries. Experiments on OpenS2V-Eval and a new ReactID-Eval-SEQ show consistent gains on aggregated metrics and ablations indicate each component contributes. The paper also describes an LLM planner that converts a single prompt into a timeline at inference.", "questions": "1. How accurate are the VLM-derived timelines, both in absolute timestamps and boundary placement between sub-actions. Please report a small human study with boundary F1 and inter-annotator agreement.\n\n2. Please clarify the compute budget, number of GPUs, and wall-clock time for the reported 10k-step training, and whether the method remains effective when scaled up or down.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well scoped around balancing identity fidelity and action realism, and it articulates the root causes spanning data noise, training instability, and coarse action modeling.\n\n2. The data pipeline is thoughtfully designed, separating living and non-living entities, grounding with MLLMs, and adding face-centric masks, which is important for identity preservation.\n\n3. The timeline-based conditioning is technically interesting: subject-aware cross-attention with label binding plus temporally-adaptive RoPE to normalize sub-action durations targets a known failure mode at action boundaries.", "weaknesses": "1. The timeline annotations are produced automatically by a VLM at scale, yet the paper does not quantify timestamp accuracy or action-boundary noise, which could systematically bias training and evaluation for sequence control. A small human audit or agreement study would help.\n\n2. Subject masks are supervised by SAM-style masks. Failure modes such as strong occlusion, rapid camera motion, or multi-subject overlap are not analyzed, although these are common in personalized scenarios.\n\n3. Curriculum details are under-specified. The schedule for the difficulty threshold, the mixing ratio of intra-clip and inter-clip references over time, and the sensitivity to the λ weights are not provided, limiting reproducibility beyond the high-level recipe.", "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": "48OhMAkzkf", "reviewer": "Reviewer_3tVk", "summary": "This paper addresses a challenge in personalized video generation: balancing identity consistency (preserving a subject’s unique features across frames) and action realism (generating natural, dynamic motions). The authors identify three root causes of this trade-off—imprecise subject-video alignment, unstable training due to varying sample difficulty, and coarse-grained action modeling—and propose ReactID, a holistic framework to mitigate them.", "questions": "1. How does ReactID perform on scenarios with 3+ subjects (e.g., a family of 4 performing distinct actions)? Does the label binding mechanism in subject-aware cross-attention scale efficiently, or does it introduce misalignment/overhead with more subjects?\n2. Can you provide training time per step (on a standard GPU, e.g., A100) and inference time per 5-second video, compared to baselines ? This would help readers assess ReactID’s practicality.\n3. ReactID focuses on 5-second videos—can it generate longer videos (e.g., 30 seconds) while maintaining identity-action consistency? If not, what are the main barriers (e.g., temporal drift)?", "limitations": "", "rating": 4, "confidence": 1, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The problem of identity-action balance is central to personalized video generation, and ReactID’s solutions (dataset, curriculum, temporal modeling) advance the field beyond incremental improvements. The dataset release also fosters collaboration.\n2. ReactID’s holistic approach (data + training + modeling) is innovative. For example, the subject-aware cross-attention’s label binding mechanism (assigning unique labels to subjects) solves a longstanding issue of action-subject misalignment in multi-subject scenarios.\n3. Ablation studies validate each component.", "weaknesses": "1. The paper does not report training/inference time compared to baselines. For real-world use (e.g., edge devices), efficiency is critical—readers cannot assess if ReactID’s performance gains come at the cost of slower runtime.\n2. ReactID uses LLMs to convert single prompts to timelines, but does not evaluate how LLM errors (e.g., incorrect action order, wrong timestamps) affect generation quality. This is a practical limitation, as LLMs are not perfect.", "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": "kUx8rTTR8E", "reviewer": "Reviewer_GCZR", "summary": "This paper presents ReactID, a comprehensive framework designed to address the fundamental trade-off between identity consistency and action realism in personalized video generation. The authors identify three core challenges—imprecise subject-video alignment, unstable training dynamics, and inadequate modeling of fine-grained actions—and propose a holistic solution. The main contributions are threefold: (1) the creation of ReactID-Data, a large-scale, high-precision dataset with detailed temporal annotations; (2) a novel Difficulty-Aware Curriculum Learning strategy to mitigate the \"copy-paste\" artifact and ensure stable convergence; and (3) a structured, timeline-based conditioning mechanism with two novel components (Subject-Aware Cross-Attention and Temporally-Adaptive RoPE) for fine-grained action control. The paper demonstrates through extensive experiments that ReactID achieves state-of-the-art performance, effectively balancing identity preservation with natural motion generation. It is a solid piece of work with significant practical contributions to the field.", "questions": "None", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1.  **Clear Motivation and Problem Formulation:** The paper begins with a clear and insightful analysis of the core challenges in personalized video generation. It methodically breaks down the problem into three specific bottlenecks. The proposed methods directly and logically address each of these points, creating a coherent and well-motivated narrative.\n\n2.  **High-Quality Data Pipeline and Dataset:** The construction of the ReactID-Data dataset is a major strength. The data processing pipeline is thorough, robust, and well-reasoned, combining vision-based entity extraction, MLLM-based detection, and post-verification. The decision to annotate videos with structured action timelines and fine-grained textual descriptions is particularly valuable, addressing a critical need for temporally precise data in the field.\n\n3.  **Effective Curriculum Learning Strategy:** The proposed Difficulty-Aware Curriculum Learning is an elegant and well-thought-out solution to the common \"copy-paste\" problem, where models overfit to easy samples at the expense of motion realism. By formalizing sample difficulty along three intuitive axes (subject size, appearance similarity, and sampling strategy) and progressively introducing harder samples, the method effectively guides the model toward a more generalized solution. This claim is convincingly supported by a thorough ablation study (Table 5), which demonstrates the value of each component and the full curriculum.\n\n4.  **Novel and Controllable Timeline Conditioning:** The timeline-based conditioning mechanism is a significant step forward for controllable video generation. Decomposing monolithic prompts into timestamped sub-actions gives users granular control over complex action sequences. The architectural innovations—Subject-Aware Cross-Attention to bind actions to specific subjects and Temporally-Adaptive RoPE to handle variable action durations—are technically sound and well-justified.\n\n5.  **State-of-the-Art Performance:** The experimental results are comprehensive, demonstrating superior performance against numerous baselines across multiple benchmarks (OpenS2V-Eval and the newly proposed ReactID-Eval-SEQ). The qualitative results (Figure 1, Figure 4) further underscore the model's ability to generate fluid, natural movements while faithfully preserving the subject's identity, a feat many prior methods struggle with.", "weaknesses": "1.  **Lack of Ablation on Data Annotation Quality:** While the paper shows that a model trained on ReactID-Data outperforms one trained on OpenS2V-5M (Table 6), it lacks a direct ablation study on the impact of the fine-grained timeline and textual annotations. It would be beneficial to see how the ReactID model itself performs when trained on the same video data but with coarser captions or no timeline promtps, which would more directly quantify the benefit of the expensive and detailed annotation pipeline.", "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)