Phantom-Data:  Towards a General Subject-Consistent Video Generation Dataset
Authors: Zhuowei Chen
Bingchuan Li
Tianxiang Ma
Lijie Liu
Mingcong Liu
Yunsheng Jiang
Gen Li
Xinghui Li
Liyang Chen
SiYu Zhou
Qian HE
Xinglong Wu
Venue: ICLR 2026 Poster (2026)
Keywords: Video Generation
Multimodal Generation
Primary area: generative models

Abstract: Subject-to-video generation has witnessed substantial progress in recent years. However, existing models still face significant challenges in faithfully following textual instructions. This limitation, commonly known as the copy-paste problem, arises from the widely used in-pair training paradigm. This approach inherently entangles subject identity with background and contextual attributes by sampling reference images from the same scene as the target video. To address this issue, we introduce \textbf{Phantom-Data, the first general-purpose cross-pair subject-to-video consistency dataset}, containing approximately one million identity-consistent pairs across diverse categories. Our dataset is constructed via a three-stage pipeline: (1) a general and input-aligned subject detection module, (2) large-scale cross-context subject retrieval from more than 53 million videos and 3 billion images, and (3) prior-guided identity verification to ensure visual consistency under contextual variation. Comprehensive experiments show that training with Phantom-Data significantly improves prompt alignment and visual quality while preserving identity consistency on par with in-pair baselines.

Published as a conference paper at ICLR 2026
PHANTOM-DATA: TOWARDS A GENERAL SUBJECT- CONSISTENT VIDEO GENERATION DATASET
Zhuowei Chen, Bingchuan Li Tianxiang Ma", Lijie Liu", Mingeong Liu, Yunsheng Jiang, Gen Li, Xinghui Li, Liyang Chen, Siyu Zhou, Qian He, Xinglong Wu Bytedance, China
ABSTRACT
Subject-to-video generation has witmessed substantial progress in recent years. However, existing models still face significant challenges faithfully follow- ing textual instructions. This limitation, commonly known as the copy-paste problem, arises from the widely used in-pair training paradigm. This approach inherently entangles subject identity with background and contextual attributes by sampling reference images from the same scene as the target video. To ad- dress this issue, introduce Phantom-Data, the first general-purpose cross- pair subject-to-video consistency dataset, containing approximately one million identity-consistet pairs across diverse categories. Our dataset is constructed via a three-stage pipeline: (1) general and input-aligned subject detection module, (2) large-scale cross-context subject retrieval from more than 53 million videos and billion images, and (3) prior grior-guieed identity verification to ensure visual consistency under contextual variation. Comprehensive experiments show that training with Phantom- significantly improves prompt alignment and visual quality while preserving identity consistency on par with in in-pair basclines. Project Pngelhttps://phale on video. github. io/ D/Phanton Data/
Reference Sudject
Jspur Caption & Torger Vides
Reference Subject
Ispar Caption Torger Vileo
Exinting Mathhdds
JACA SCOUT WILE aucovy-
wite
perice Heaning skzavssity insolae,
1040 LAvin wilh
A list colored Leieadar p4a0s (plyy Rear
(a) Comparison on single-reference training data
(b) Multi-reference training data
Figure 1: Overview of training samples. (a) Single-reference setting: existing methods typically extract the reference image from the target video itself. In contrast, our approach uses reference im- ages captured in distinct contexts. (b) Our dataset also includes multi-reference samples, presenting each subject in varied contextual settings.
INTRODUCTION
In recent years, text-to-video generation models, exemplified by Sora Brooks al. (2024). have made significant progress Seawead et al. 20055) Kong et al. (2024): : Wang et al. (2025a) : Yang et al.
progress/Seaweddee
Equul contributions, Project lead

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2024b); Aand-Al (2025).
However, due to the limited controllability inherent in textual instructions,
achieving fine- ameegrained control over video generation remains a key challenge for practical applica-
tions. Among recent advances Hu (2024); Jiang et al. (2025); Zeng al. (2024); [Feng et al. (202);;
Kondratyuk et al. (2024); Xu et al. (2024). increasing attention has been paid to enforeing subject
identity consistency in text-to-viee generation. The subject-consistet video generation task (S2V)
Liu et al.] (025b)); Chen et al.] (2025b); Huang et al. (2025) aims to generate videos that not only follow the given text prompt but also faithfully preserve the identity of reference subjects, such as people, animals, products, or scenes, This capability has great potential in applications such as personalized advertising Chen et al. (2025a) and AI-driven filmmaking Wu et al.) (2025).
Despite encouraging progress in visual consisteney, existing S2V approaches still suffer from limited text-following ability and suboptimal video quality, a phenomenon often referred to as the copy- paste problem. As shown in Fig. 2 the generated video directly replicates the reference subject from one of its frames, leading to the omission of the "boxing ring" background described in the prompt. This issue stems from the in-pair training paradigm, where the reference subject is sampled from the same target video, as illustrated in Fig (a.. Consequently, the model tends to preserve not only subject identity but also irrelevant contextual details. However, in real-world scenarios, such entangled features may contradict the actions or semantics described in the text prompt, causing the generated videos that either deviate from the prompt or exhibit noticeable artifacts.
To address the above issue, prior works Chen et al. (225b)) Liang et al. (2025) Huang et al. (0025): :
Jiang et al.] (2025); Ju et al. (2025) have explored various data normalization and augmentation strategies, such as background removal, color jittering, and geometric transformations. However, these methods struggle to unravel complex contextual factors, such as viewpoint and motion, due to limited variation. More recent approaches introduce cross-pair data, where identity-consiseet reference and target frames are sampled from different sources. This setting encourages the model to focus on identity preservation while reducing overfitting to irrelevant visual contexts Polyak et al. 2024); Zhong et al. (2025). However, existing cross-pair datasets are primarily limited to facial domains, making them difficult to generalize to general subject scenarios. Overall, current training datasets either provide insufficient reference variation or lack domain diversity, limiting the effee- tiveness of casing copy-paste problem.
In this work, we introduce Phantom-Data, a subject-to-videe dataset specifically constructed to mitigate the prevalent copy-paste problem in the general scenarios. It is built around three core de- sign principles for the reference subject: 1) General and input aligned subjects: Reference images should span a wide range of commonly encountered subject types and reflect the distribution of real- world user inputs. 2) Different contexts: Reference subjects appear in varied conditions- different backgrounds, viewpoints, poses poses-relative encourages the model to generalize identity preservation under distribution shifts and reduces re- liance on spurious identity-irrelevant correlations. 3) Consistent identity: Despite contextual vari- ation, the reference subject must remain visually consistent with the target video subject in terms of shape, structure, and texture.
as their counterparts in the target video. This
To fulfill these principles, we design three-stage pipeline: Firstly, we perform S2V Detection by leveraging a lasion- model to conduct open-set object detection and identify candidate sub- jects of appropriate size. A second-stage filtering step further refines the results by retaining only subjects that are both semantically relevant and visually compact. Then, we conduct Contextu- ally Diverse Retrieval by constructing large-scale subject database comprising over 53 million video segments and billion image samples, increasing the likelihood of retrieving the same iden- tity under diverse backgrounds, poses, and viewpoints. Finally, we apply Prior-Guided Identity Verification to ensure identity consistency. For living beings (e.g.. humans, animals), we mine temporal structures from long videos construct cross-context pairs. For static objects (e.g., prod- ucts); we perform category-specific retrieval. A final VLM-based pairwise check verifies that each selected pair maintains both identity consistency and contextual diversity. Through this pipeline, we construct large-scale, high-quality cross-pair consistency dataset comprising approximately i million ddenttttyconsiistee pairs with over 30, 50,000 mulli--ubjeet scenes, offering a strong foundation for modeling general subject-to- to-ieooo tasks. Representative samples are shown in Fig.
To validate the effectiveness of our dataset, we conduct comprehensive experiments using open- source video generation models. The results demonstrate that, compared to prior data construction methods, our cross-pair approach substantially improves key metries such as text alignment and vi-
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Reference Subject
Input Caption & Target Video
The man is boxing the boxing ring, waving his fists and sweating his cheeks.
Figure 2: Illustration of the copy-paste problem. The shown result is generated by SOTA video generation model (Kling Kli).
sual quality, while maintaining identity consistency on par with in-pair baselines. Furthermore, we perform ablation studies to highlight the importance of a large-scale and diverse cross-pair dataset, showing that both data volume and scene diversity play critical role in enhancing generation perfor- mance. We also validate the effectiveness of our data pipeline preserving high identity consistency while ensuring sufficient contextual diversity.
Our main contributions can be summarized as follows:
We introduce Phantom-Data, the first general-purposse cross-pair video consistency dataset, comprising approximately million high-quality, identity-consistent pairs that span a wide range of subject categories and visual contexts.
We present structured data-construction pipeline purpose-built for subject-consisten video generation. unifies subject-centric detection module optimized for the S2V task, large-scale cross-context retrieval, and prior-guided identity verification, thereby securing strict identity fidelity while introducing rich contextual diversity.
We conduct extensive experiments to validate the effectiveness of our dataset, demonstrat- ing consistent improvements in text alignment, visual quality, and generalization isting in-pair baselines.
Method
General Objects
Input-aligned Objects
Diverse Context
Publicly Available
MovieGen
X
Video Alchemist
ConceptMaster
X
Ours
Table 1: Comparison between Phantom-data and datasets used in prior work.
1.1 RELATED WORK
Text-to- Videoo Generation. Early diffusion-based video generators Blattmann et al. (0033 Guo
al. (2024a); Wang al. (2024b) were limited to producing short clips with constrained spatial and temporal resolution. However, the field has rapidly progressed with the introduction of large-scale
latent diffusion models and transformer-based architectures. Notably, Sora [Bookks et al. (2024) is capable of generating minute-long. high-fidelity videos, while contemporaneous systems such as Seaweed ISeawead
(2025), Hunyuan- Video Kong et al. (2024). Cog Video-X | et al. (2024b), MAGI Sand-Ai (2025). and others Wang et al. (2025a) have further advanced frame rate, resolution, scene complexity, realism, and motion smoothness. Despite their impressive visual qual- ity, these generic text-conditioned models provide only coarse control: textual prompts alone cannot fully specify scene layout, subject appearance, or viewpoint, motivating research into finer control signals.
Bubject-Consisten Video Generation. The task of subject-consistet consistent video generation (S2V) LiU et al. (2025b); Fei et al. (2025): ; Chen et al. (2025b); Huang et al. (022); Deng al. (025 fo- cuses on generating videos that not only align with the given text prompt but also preserve the visual identity of reference subject, such as a person, animal, product, or scene. From modeling
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perspective, common strategy Huang et al.
2025): Chen et al. (2025b); Polyak al. (2024);
Hu et al.] (2025) is cross-attentionn based fusion, where visual features extracted from pretrained en- cadeor et al. (2021): :: Oquab et al. (2024) Xu et al. or VLMs, are injected into the generative backbone through dedicated attention layers. An alternative approach is noise-space conditioning, where identity features obtained from VAE encoder directly concatenated with the noise in- put of the diffusion model, without modifying the underlying architecture. This lightweight design enables nearly lossless injection of identity information, as seen in DIT-style models such Phan- tom Liu et al. (222b)) and VACE Jiang et al. (2025). Recent systems like SkyReels-A2 Fei et al. (2025) explore combining both strategies, incorporating cross-attention guidance and noise-level conditioning within unified framework.
Training Data in Subject-to- Video Generation. Training data plays a crucial role in subject- consistent video generation, as it directly influences model's ability to generate faithful and con- trollable results. Most existing approaches rely on in-pair supervision, where the reference and target frames are sampled from the same video clip. While this setup guarantees identity align- ment, often leads to the undesirable copy-paste effect-where the model reproduces not only the subject but also the background and pose of the reference frame, limiting its capacity to follow the input prompt. To mitigate this issue, several works CChnn et al. (2025b); Liang et al. (2025); Huang al. (202)); Jiang et al. (2025); Ju et al. (2025) adopt data normalization and augmentation strate- gies, such as background removal, color jittering, and geometric transformations. However, these techniques, combined with the limited diversity inherent in in- pair training, are often insufficient to address complex contextual variations such as motion, viewpoint, and scene layout. Recent efforts have turned cross-pair training, where identity from different videos. This setting encourages the model to concentrate on subject identity while reducing overfitting to specific visual contexts [Polyak et al. (2024): ; Zhong et al. (2025). Neverthe- less, current cross-pair datasets are mostly restricted to narrow domains like human faces, limiting their generalizability to broader subject categories such as animals, products, or stylized charac- ters. In summary, although cross-pai supervision offers promising direction for addressing the copy-paste issue, the absence of high-quality, diverse, and general domains remains a significant bottleneck for advancing S2V models. To bridge this gap, we introduce Phantom-data: a large-scale cross-pair dataset designed to support subject-consistent video generation across wide range of real-world categories.
reference and target frames are sampled
training data across
2 PHANTOM DATA
We provide a detailed analysis of Chantom-Data. focusing on its statistical properties and a com- parison with existing datasets for subject-consistet video generation.
2.1 STATISTICAL ANALYSIS
We analyze the dataset at both the video and subject levels.
Video-level properties. As shown in Fig. 3 a-cc,, our dataset spans a wide range of video durations, resolutions, and motion patterns. Around 50% of videos are 5-10 seconds long, and the majority are 720p resolution. Motion levels also vary considerably, covering both relatively static and highly dynamic scenes.
Subject composition. Fig. d) illustrates the distribution of subject types and their combinations. While the majority of samples (approximately 720,000) contain single subject-such as hu- man, product, or animal substantial portion (around 280,000) involve two or more co-occurring entities, supporting multi-subject consistency modeling.
Reference diversity. As shown in Fig. e), the dataset spans a broad semantic space of subject categories. Common reference entities include humans (e.g.. woman, man, girl), animals (c.g.. dog, bird), and man-made objects (c.g., smartphone, car, laptop), highlighting the dataset's suitability for general- purpose subject-to-videe tovido modeling across varied domains.
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10000'
7%
10p-15e 18
7%
5-400
8%
21%
36%
34%
200-400
25%
(4) Video Duration
(b) Video Resolution
(c) Video Motion
B de roet ca EUS hoFse SUV.
Womait
dogboy
dog
boy
person
tphon
character aacter on
creature
isman peppee
man
birds
Paptop
Jscket MANN lee
Raots nicr ophone
(d) Reference Type Distribution
(c) Reference Class
Figure 3: The statistical analysis of Phantom-Data
2.2 COMPARISON WITH PRIOR DATASETS
As summarized in Table existing datasets for subject-consistent video generation either lack gen- eral object coverage, rely heavily on input-aligned references from the same video. or are limited in contextual diversity. In contrast, Phantom- Data offers a more comprehensive setting: supports general object categories beyond faces, encourages cross-contex modeling by sampling subject- reference pairs from diverse scenes, and publicly available for research. This makes the first open-access dataset to jointly support identity consistency and context diversity in general-purpose cross-pair setup.
3
DATA PIPELINE
3.1 VIDEO DATA SOURCE
The Phantom- Data video dataset consists of clips collected from public sources such as Koala- 36M Wang et al.) (2024a), as well as proprietary internal repositories. Each video undergoes rigorous quality control pipeline, including black border detection, motion analysis, and other filter- ing steps. Subsequently, long videos segmented into short clips at the second level using scene segmentation. Each resulting clip is then annotated with a corresponding video caption. The total number of videos is approximately 53 million.
3.2
DATA PIPELINE
Given an input video and its associated caption, we focus on constructing a high-quality cross-pair dataset, where the same subject appears across different visual contexts while maintaining identity consistency. this end, we design a structured data pipeline consisting of three key stages. As shown in Fig 4 firstly, we perform S2V Detection to identify high-quality subject instances from videos. Then, we propose a Contextually Diverse Retrieval module to recall candidate images that likely to correspond to the detected subjects across varying scenes. Finally, apply Prior- based Identity Verification to filter the retrieved candidates, ensuring that only those sharing the same identity across different contexts retained.

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Input Video & Caption
Talking bear with woman nearby
Step1: S2V Detection
Intermediate Result
Detection Result
Visual Semuntic Recheck
Frame Sampling
carboon bear
Subject-text Motching
Subject Completemess
Visual Grounding
Detection
Filtering
Resllt
Keyword Extraction
Subject Specificity
Candidate References
Step2: Contextually Diverse Retrieval
Encoder
Human
cartoon Bear
Query Subject
Person Encoder
Similurity Thresholds
Candidate References
Hody
General
Encoder
Step3: Prior-Basse Verification
Final Cress-pair
Utilization Prior Knowledge
VLM Verification
Product: Hos Logo?
Samo Identity
Query Subject
Quarry Refeceso
carboon bear
Final Cross-pair
Candidate References
Living being: From viden?
Different Context/Bi
Qkay > Refaxesy
woman
Training Data
Taput cuption Reference Images
Target Video
Talking cartoon with nearby
Figure 4: The overview of the data pipeline for constructing cross-pair training samples.
3.2.1 S2V DETECTION
This stage aims to identify diverse and qualified subjects from each vidco clip as candidates for cross-scene pairing. It consists of five major steps:
Frame Sampling To reduce computation, we sample three frames at f. = 0.05, 0.5, and 0.95 of each clip. following Chen al. (2025)), ensuring temporal diversity.
Keyword Extraction. We use Qwen2.5 Yang et al. (2024a) to extract key noun phrases (e.g., people, animals, products) from captions, serving subject candidates for grounding.
Visual Grounding. Qwen2.5- 5L Bai et al. (2025) aligns each phrase to regions in the sampled frames. Ambiguous matches mapping multiple regions removed to reduce noise.
Bbox Filtering. We retain boxes covering between 4% and 90% of the image and at least 128 128 in size. Overlapping boxes (IoU 0.8) are suppressed for clarity.
Tisual-Semantis Recheck. To further ensure the quality of the grounded subjects, we employ an- other vision-language model, Intern V1.2.5 7B Chen al. (2024), validate each detection against

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the following criteria: 1) Completeness: We observe that visual grounding often produces bounding boxes around partial or cropped objects, as a result of the underlying detection model's 's exhaustive labeling strategy. However, for S2V task, users typically provide complete reference subjects, mak- ing such incomplete detections unsuitable. We therefore filter out any region that fails to cover the full extent of the object. 2) Specificity: The subject must be visually distinct and identifiable. Vague or generic objects, such as trees, rocks, or background clutter, are excluded. 3) Subject-text Match- ing: The grounded region must be semantically consistent with the associated phrase. To improve alignment precision, we employ separate instance of Intern VL2.5 to reevaluate the consistency between the textual description and the detected subject.
As a result of this pipeline, we obtain a high-quality set of subject instances, each paired with corresponding descriptive phrase. Since a subject may appear in multiple frames across the video, we select only one representative instance for visualization in the Intermediate Result of Fig. 4
3.2.2 CONTEXTUALLY DIVERSE RETRIEVAL
Given the subject instances detected in the previous stage, we aim to find candidate reference images of the same subject appearing in different visual contexts. To achieve this, we construct a large-scale retrieval bank and use the detected subjects to perform identity-aware querying.
Large-Scale Retrieval Bank Construction. The retrieval bank comprises two essential compo- nents: diverse subject image sources to increase contextual variability, and feature representations tailored for pdentity-rreserving retrieval.
Subject Source. We begin by registering every detected subject instance from the training videos into retrieval bank. To further broaden candidate diversity, augment this bank with an extra billion images from the LAION dataset Schuhmann et al. (2022) beyond the original video corpus. These external images inject greater variation in scene, pose, and appearance, delivering broader contextual coverage during retrieval, an advantage that is particularly valuable for product-centric scenarios with substantial intra-instance variation.
Subject Representation. To support reliable cross-contex. identity matching, we employ expert- designed encoders to extract preservy- and context-invariant embeddings tailored differ- ent subject categories. These embeddings are used for both indexing the retrieval bank and querying.
For facial representation, we adopt the widely used ArcFace encoder robust and discriminative identity embeddings:
Deng al. 2019) extract
Viace
Fareface (Trace)
(1)
For general objects, inspired by ObjectMate Winter et al. 2024 employ CLiP-based model
fine-tuned on a consistency-foose focused image dataset Shao and Cui (023)
to extract
embeddings:
Vaubj
EIR (1).
For human subjects, which are central to many downstream applications, we combine both facial and clothing features. Each individual is represented by concatenating the general appearance em- bedding with the corresponding facial embedding:
V person F (T), Fareface [Tace))]
Query-Based Retrieval. To ensure the retrieved candidates are visually distinct from the query image yet share the same identity, we apply both upper and lower bounds on similarity. Specifically, discard overly similar results (potential duplicates) by enforcing upper similarity threshold, and exclude unrelated identities by applying a lower threshold.
3.2.3 PRIOR-BASED IDENTITY VERIFICATION
However, due to the large scale of the retrieval corpus, false positives frequently occur within seemingly reasonable similarity ranges. To address this issue, we adopt a two-stage filtering strategy based on prior knowledge and VLM Verification.
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Subject Consistency
Prompt Following
Video Quality
Methods
DINO
GPT-40 f
Reward-TA
Temporal f
Motion f
IQ1
BG f
Subj
In-pair
0.478
2,481
2.074
0.971
0,985
0.725
0.937
0.933
In-pair+ + Data Aug
0.473
2.792
2.427
0.961
0.979
0.730
0.932
0.922
Face Cross-pair
0.354
2.378
3.022
0,983
0.989
0.723
0.937
0.935
Ours
0.416
3,041
3.827
0,975
0,986
0.739
0.948
0.944
Table 2: Main results comparing prompt following, subject consistency, and video quality across different training paradigms. Bold denotes the best performance per column. The underline indicates the second-highed scores.
Utilization of Prior Knowledge. We apply category-specific filtering strategies to improve cross- pair reliability: 1) Non-living subjects (e.g., products): These typically exhibit high intra-class vari- ability, making identity verification more challenging. To improve precision, retain only product instances that feature complete and recognizable brand logos (e.g.. Nike, Audi), which remain visi- ble across different scenes. 2) Living entities (e.g., humans, animals): For these subjects, restrict retrieved candidates to those from different clips within the same long-form video. This constraint ensures natural variation in scene and pose while maintaining consistent identity.
VLM-Based Consistency Verification. To further ensure both identity consistency and contextual diversity, apply VLMMbased verification procedure: 1) identity consistency: For non-living objects, we enforce strict similarity in visual details such as color, packaging, and textual elements, while allowing for background variation. For living subjects, especially humans, we verify facial identity consistency and, in the case of full-body samples, also ensure clothing alignment. 2) Contex- cross-pair pair samples that exhibit substantial variation in background and scene context, thereby alleviating copy paste artifacts during model training.
tual diversity. We keep only those cross-
4
EXPERIMENTS
4.1
IMPLEMENTATION
Model Architecture. We validate the effectiveness of our proposed data using the Phantom-
Liu et al. (2025b) model. Built on the Wan2.1 Wang et al. (2025b) foundation, Phantom taato wan is leading open-source framework for subject-consistent video generation.
Training and inference. We train a 1.3 billion-parameter Phantom-wan model using Rectified Flow (RF) Lipman et al.] (2022); Liu et al.] as the training objective. The training is performed on 64 A100 GPUs for 30k iterations with 480p resolution data, which yields stable performance. During inference, we apply Euler sampling with 50 steps and use classifier-free guidance Ho and Salimans 2022) to decouple image and text conditions. All experiments follow the same training and inference settings ensure fair comparisons.
Evaluation. We construct a test suite of 100 cases from diverse scenarios, covering humans, an- imals, products, environments, and clothing. These cases include both single- and multi-subject settings, paired with manually written text prompts that reflect natural user input.
We evaluate model performance across three dimensions: video quality, text-video consistency, and subject-video consistency. Subject-video consistency is evaluated using CLIP Guo et al.) (2024b), DINO Oquab al. (2023). and GPT-4o scores, following recent evaluation protocols inspired by Peng et al. (2024). Text-video consistency is measured via Reward-TA Liu et al. (2025a). both of which assess the semantic alignment between generated video content and the text prompt. Video quality is assessed using VBench Huang et al.) (2024). which provide fine- ane-ggrined evaluation along several aspects, including Temporal (temporal flickering and stability), Motion (smoothness of sub- ject motion), IQ (overall imaging quality), BG (background consistency across frames), and Subj (temporal consistency of the generated subject).
4.2 MAIN RESULTS
We evaluate our method against three representative baselines: (1) In-pair training, which samples the reference subject from the same video; (2) In-pair with copy-augmentationn which introduces
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A white jackee Nehs his band
Libble girl the with umbrelle
surt eabing hamburger
hoppy.
sirt by wasging
Inpair
Inpairt-Agg
Face Cross-pair
Cuurs
Inpair
InpairtAng
Face Crose-pair
Ours
Figure 5: Qualitative comparisons across different training strategies.
spatial and appearance augmentations reduce overfitting in Chen et al. (2025b); and (3) Face- based cross-pair, which utilizes face-level identity matching across videos. We report both quantita- tive and qualitative comparisons.
Quantitative results demonstrate that our cross-pair training paradigm achieves state-of-the-an per- formance in terms of text-video alignment and overall video quality, measured by swardd-ased evaluation metrics. Furthermore, our method delivers competitive subject consistency, rivaling in- pair baselines despite the increased scene diversity. In contrast, In-pair settings suffer from poor text-following ability due to overfitting on narrow visual contexts. The Face cross-pair method per- forms slightly better on prompt following but limited by the narrow domain of its face-centric training data, resulting in weaker identity preservation across diverse subjects.
Qualitative comparisons, as shown in Fig further support our conclusions. Across multiple prompts and subject categories, models trained with in-pair data consistently fail to follow textual instructions, often generating videos with obvious artifacts. contrast, our cross-par pair trained model successfully aligns with the prompt across all cases, producing coherent and faithful subject-driven videos.
4.3 ABLATION STUDIES
Subject Diversity. As shown in Table enriching the training set with diverse subject types-including humans, animals, products, and multi-subject scenes- connes-consistently improves sub- ject consistency and prompt following, compared to the face-only baseline.
Data Scale. Table illustrates the effect of data scale. Increasing the training set from 100 k to million samples leads to further improvements across all metrics, highlighting the importance of both diversity and scale in building robust subject-te viviiooo generation dataset.
Contextually Diverse Retrieval. To assess the impact of contextual diversity in reference selection, compare different sampling and retrieval strategies: (1) Temporal Sampling. As illustrated in

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Irput
Second-interval Frame
Minute-interval Frime
Tnput
Rigi 1-sideo are
Ré60) l-image
(A) Reference frames sampled different tempora intervals
(b)
Retrieved top ((R@@) results from different source datasets
Input
wil prior
Input
wil verification
verification
(c) False positive result if prior used
(d) False positive penson
cloth)
Figure 6: Ablation study on Contextually Diverse Retrieval and Prior- Risoed Identity Verification. (a) Reference frames from different timestamps show that longer videos offer more diverse contexts. (b) Retrieval from large-scale image datasets improves recall and candidate diversity. (c) Without prior filtering, false positives may be included. (d) Verification removes mismatched or overly similar identities, ensuring high-quality pairs.
Table 3: Ablation study subject diversity.
Table Ablation study on data scale.
Methods
Subject Consistancy
Prompt Following
Subject Consistency
Prompt Following
DINO
GPT-4a
roward-TA
Methods
baseline (face only)
0.354
2.378
3.022
DINO f GPT-4o
reward-TA
+ human
0.401
2.547
3,726,07002
IPlanimal
0.416.0.002
2.795,0-411
3.407 0033
100k
0.408
3.090
3.796
product
0. 0.3866..002
2.662,0.25
3.572.0.58
0.416
3.175
3.827
multi-subject
0.418,0,064
+0.525
3.512,00998
Fig. b)a), reference frames sampled at longer temporal intervals (e.g., minute-level vs. second-level) provides richer visual diversity. (2) Multi-source Retrieval. Fig. b(b) compares retrieval from video- only sources and from a combined image+video retrieval bank. Incorporating large-scale image datasets improves both recall and candidate diversity.
Prior-Based Identity Verification. We further evaluate the role of prior filtering and identity ver- ification in ensuring training quality: (1) Prior Filtering. Without prior-based constraints, visually similar but semantically incorrect matches (false positives) Verification Module. As shown in Fig, d), our identity verification module further refines the candidate set by removing both near-duplictes (overly similar samples) and mismatched identities (overly dissimilar ones).
often included (see Fig. ((c). (2)
5 CONCLUSION
We propose Phantom- -Data, a large-scale, general-purgee purppoe cross-pair dataset to improve subject consistency and text alignment in text-to-video generation. By introducing structured pipeline, combining open-vocabulary detection, diverse cross-context retrieval, and identity verification, we address the limitations of in-pair training and reduce the copy-paste problem. Experiments show that our dataset significantly boosts generation quality while maintaining strong identity consis- tency. Phantom-Data provides solid foundation for future research in controllable subject-to-video generation task. In future work, we will explore how to extend the reference image domain from specific subjects to general references, enabling users to create videos with flexible control over various elements through both images and text prompts.
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6 APPENDIX
6.1 THE LIMITATIONS OF SYNTHETIC DATA
We try two SOTA models, GPT4o and DreamO Mou et al. (2025) to generate consistent subjects on different context. The results in Fig shows these models could still generated inconsistent subject. However our real cross-pair data construction pipeline could provide exactly same subjects on different context.
6.2
USER STUDY
To evaluate the generated videos under different training data regimes, we conducted a user study comparing four settings: in-pair training, in-pair with data augmentation, face-level cross-pair train- ing, and our proposed full-object cross-pair approach. We ask six participants, each of whom in- dependently evaluated 50 video groups, containing four videos generated in the different training settings. For each group, participants were asked to select the best video in terms of overall visual quality, subject consistency, and alignment with textual prompts. As summarized in Table our method was overwhelmingly preferred, receiving 76% of the votes. In contrast, all other baselines received less than 12%, highlighting the effectiveness our cross-pair training design in producing videos that are faithful to the intent of users.
In In-pair In-pair Data Aug 6%
Face Cross- pair
Ours
11%
7%
76%
Table 5: User study on the best video selection based on overall visual quality, subject consistency, and text alignment across different training data settings.
6.3 PROMPTS FOR EACH TASKS
6.3.1 KEYWORD EXTRACTION
We leverage the Qwen2.5 7B language model et al. (2024a) to extract key noun phrases such persons, animals, products, and IP characters- from video captions. Given the inherent variability in L.LM outputs, we incorporate a second-stage L.M for post-processing. which enforces structured JSON formatting and filters spurious or noisy detections. To enhance phrase quality, apply the following constraints:
1. Structural Filtering: Exclude generic body parts or structural terms (c.g., leg).
2. Number Filtering: Eliminate plural forms (e.g., women) to ensure instance-level ground- ing.
3. Specificity Enforcement: Promote fine- greiggained disambiguation within categories, encour- aging phrases like "a woman with red coat" or "a woman with red hair" rather than generic references.
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Input Image
Data Aug
DreamO
GPT4-0
Ours
2
Figure 7: Comparison of reference-data construction strategies. Naïve data augmentation (Data Aug) offers only limited variation, while directly adopting state-of-the-art IP-consistent generators (e.g., DreamO, GPT-4o) can introduce appearance inconsistencies, highlighted in the figure. Best viewed in color with zoom.
Keyword Extraction Prompts First Round
User Prompt
Given an video caption, please retrieve the entity words that indicate human and subjects. [Definition of humans] human that appear in the image.
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[Definition of subjecta] belong to products bike/cloth
...) ...)/animal//3 Character
All entity words need to strictly follow two rules below:
1) the entity word is noun without any quantifier.
2) the entity word is an exact subset of the caption. Do not modify any characters, words, and symbols.
3) the entity word should not depcit human parts, such as hair/leg...
4) IF there are multiple same entity words, pelase also give the apperance of the entity word the caption.
5) The entity word should be in singular form
6) ignore the background that appear in most of the image area, such as "tree", "desert", "road", "lake", "mountain "shrub", "water/vaterfall." 7) Ignore the category that belong to plants
"stream"
Here are some examples, follow this format to output the results, note don't output the caption, give the output only:
### Caption: woman in a mask and coat, with long brown hair shows a small green-cappep bottle to the camera in the street.
4## ('human': ['woman'], 'subject': ('mask', 'coat', 'green- capped bottle'])
### Caption: woman in a pink dress and a woman in a light blue shirt are seated at the bar. ### ('human's : [' ['woman in a pink dress', 'ooan in a light blue shirt'), ] 'subject': [' l'dress', 'shirt/])
### Caption: "runners in the court". ### ('human's [""], 'subject': []]
Keyword Extraction Prompts Second Round
User Prompt
Given an entity word list, please reorgnize the entity
list you output before. Do not modify any characters, words, and symbols of the entity word. [Definition of humans] human that appear in the image. [Definition of subjects] belong products (e.g. bike/cloth...)) animal/IP Character,
Entity words that meet the following conditions should be deleted.
1) human parts, such as hair/leg...
2) entity word in the plural form
3) the background spaces that appear in most of the image area, such as "living room/court"
4) natural landscape/plants, e.g. "tree", "desert"
Here are some examples, the output results should in ison format:
### (' ('human': ; [' ['women'] ), 'subject'; ['tops', 'pants', 'door
'11
(' human' : ['], 'subject': []]
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#*# : [' (fihh) l, 'subject': ['coral reef', 'coral', 1 (' ('human's : [',, 'subject': ['fish', 'coral', "fish"]]
### (' human' ['woman with long, flowing black hair', 'woman ") 'subject': ['red and silver earring', 'white garment " ttrrditiinna building', 'wooden pillars', 'tiled roof (])
(" human'n : ['woman with long, flowing black hair', 'woman'), subject': [' r'red and silver earring', 'wiite garment'])
### (' humam'' ('man in a black suit and white gloves', 'man in black shirt and pants', 'audience'), 'subject': [' black suit', 'white gloves', 'green pool table', cue stick', 'shot', 'cue ball']]
(' ('human': ['man in a black suit and white gloves', man in a
black shirt and pants'), ) 'subject': ['black suit', 1 white gloves', 'green pool table', 'cue stick', 'cue ball(]]
6.3.2 VISUAL GROUNDING
We adopt the vision-language model Qwen2.5-VL 7B for open-set grounding.
Visual Grounding Prompts
User Prompt
Outline the position of (h. If there is only one bbox detected, output the coordinates in JSON format. For example:
json
(("bbox_2dt 2d": [654, 103, 710, 225], "label": "man"))
Otherwise, if there are multiple objects, directly return 0.
6.3.3 VISUAL SEMANTIC RECHECK
We futher use Intern VL.2.5 8B to filter unsuitable subjects.
Visual Semantic Recheck Prompts
User Prompt
Is the object in the image a valid object? The following conditions need to be met:
1. Specificity: The object is unique enough that it can be identified.
2. Completeness: a) For non-person cls, the completeness of the object should be above 808. The object is mostly unoccluded and shown in its entirety. b) For person class if the face appears, then the object can be considered as complete object.
3. classes: The object should not be a) haiz/leg/hand b) the word in the plural form. c) the background spaces that
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appear in most of the image area, such as sky, d) natural landscape/plants, e.g. "tree", "desert"
4. image-text consistency: the image depicts ().
If the above conditions
met, return 1. Otherwise, return 0. The output should only be 1/0.
6.3.4 QUERY PRODUCT
We futher use InterVL2.. 8B to find common product.
Query Product Prompt
User Prompt
[Task Instructions] Strictly analyze whether the input image belongs to easily retrievable product photography, Output yes ONLY if both core criteria are satisfied; otherwise output no: Brand Logo Visibility The image clearly contains zecognizable commercial brand logos (e.g., Nike/Adidas/ Gucci/Appler without occlusion or excessive blurring. Complete Product Presentation The product is fully displayed (no critical parts occupies the central area of the image, and maintains proper composition.
[Output Specifications]
Output lowercase yes/no with reason.
6.3.5 VLM VERIFICATION
We observed the limited performance of the open-sourced Qwen2.5 7B and InterVL2.55 8B models on this task, and therefore chose Vision--ro---333 to perform this task.
VLM Verification Prompts
User Prompt
promott .*.
Task: Verify if the main object in two images meets the following consistency criteria.
Output: Return "yes" if all conditions are satisfied; otherwise, return "no".
Verification Steps
1. Main Object Check
The main object in both images is human.
2. Clothing Consistency
The humans are wearing the same clothing (color, style, and accessories).
Identity Match
The humans are the same person (facial features and physical traits match).
4. Background Difference
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The humans are not in a similar environments or backgrounds.
Output Logic
If all conditions are met: Return "yes". If any condition fails: Return "no".
The output should be "yes" or "no" only, without any other text or explanation.
porsou =
Task: Verify if the main object in two images meets the following consistency criteria.
Output: Return "yes" if all conditions are satisfied; otherwise, return "no".
Verification Steps
Main Object Check
The main object in both images is a human.
2. Clothing Consistency
The humans are wearing the same clothing (color, style, and accessories).
3. Identity Match
The humans are the same person (facial features and physical traits match).
4. Background Class Difference
The background class should be different. Please note this is very important. The view differece should be considered the background class type
Output Logic
If all conditions are met: Return "yes". If any condition fails: Return "no".
The output should be "yes" or "no" only, without any other text explanation.
mun
"p_animal_promee и..
Task: Verify if the main object in two images meets the following consistency criteria.
Output: Return "yes" if all conditions are satisfied; otherwise, return "no".
Verificate Steps
1.
Main Object Check
The main object in both images is ip character animal. Not a food.
2. Clothing Consistency
The main objects are wearing the same clothing (color, style, and accessories).
3. Identity Match
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The main objects are with the same identity.
4. Background Difference The main objects are in different environments or backgrounds.
Output Logic
If all conditions are met: Return "yes". If any condition fails: Return "no".
The output should be "yes" or "no" only, without any other text or explanation.
eun
6.4 BROADER IMPACT
Our work aims to enhance identity consistency and contextual diversity in subject-to-video gener- ation, thereby improving the controllability and realism of Al-generated content. This technology holds promise for wide range of applications, including personalized media creation, digital asset generation, and educational or entertainment content production.
Nonetheless, recognize the potential social risks associated with realistic video synthesis. Such capabilities may be misused for malicious purposes, including the creation of deepfakes, impersonation, the dissemination of misinformation. We therefore emphasize the importance of responsible research and deployment practices. In particular, we encourage the use of watermarking, provenance tracking, and informed consent mechanisms ensure ethical and transparent use especiayy in scenarios involving human likeness or identity-sensitive content.
6.5 THE USE OF LARGE LANGUAGE MODELS
We used a large language model solely for writing polishing: improving grammar, clarity, and stylistic consistency of sentences written originally by the authors. The model was not used to generate research ideas, experimental designs, analyses, results, or related work content.
20


Reviews:
[{"review_id": "nU41AX1YfL", "reviewer": "Reviewer_Qtiu", "summary": "This paper introduces Phantom-Data, a large-scale cross-pair dataset for subject-consistent video generation, aiming to mitigate the copy-paste problem caused by in-pair training. The dataset is built via a three-stage pipeline combining open-vocabulary detection, cross-context retrieval, and prior-based identity verification. Experiments show improved prompt alignment and video quality while maintaining subject consistency.", "questions": "As seen in weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The motivation is clear and addresses a real issue (“copy-paste”) in subject-consistent video generation.\n2. The dataset construction pipeline is well-designed, integrating VLM-based detection and verification modules.\n3. The ablation and user study provide some evidence of effectiveness.", "weaknesses": "1. The prompts used in the dataset are overly simple and sparse, failing to accurately describe video semantics or capture complex spatiotemporal relations. This limits the dataset’s potential to improve text-video alignment in realistic scenarios.\n2. The paper evaluates only on the Phantom-Wan model. Without experiments on other open-source frameworks (e.g., CogVideoX, VACE, HunyuanVideo), it’s hard to judge whether the dataset quality generalizes.\n3. The paper compares mainly with older datasets, while recent large-scale subject-consistent datasets are not included. It is unclear how Phantom-Data performs relative to the latest baselines, such as OpenS2V.", "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": "1Z3DZKRv2u", "reviewer": "Reviewer_EDtH", "summary": "This paper introduces Phantom-Data, a cross-pair subject-to-video consistency dataset for video personalization, comprising one billion identity-consistent pairs across diverse categories. Experiments indicate the high quality and potential impact of the dataset.", "questions": "n/a", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper presents a well-designed pipeline for curating subject-video paired datasets, leveraging Visual Language Models (VLMs) to achieve robust subject-attribute pairing.\n- The dataset is large-scale, providing valuable resources for advancing research in video personalization.\n- The curated data effectively addresses the prevalent copy-paste issue encountered in video personalization tasks.", "weaknesses": "- It remains unclear how much the VLM-based pipeline improves over simpler approaches, such as using GPT-4o for generating image variations. As shown in Figure 7, though having drawbacks, generative models might sometimes even offer advantages in achieving controllable variations. Additionally, potential artifacts from generative approaches could be mitigated through VLM-driven verification and filtering, which the paper does not explore.\n- The evaluation is somewhat limited, as the dataset is tested exclusively with Wan2.1. Given that many established methods in video personalization predate the Wan series, broader evaluation across multiple models would strengthen the paper’s claims and demonstrate the wider utility of the dataset.", "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": "rI1CY391CJ", "reviewer": "Reviewer_zkPr", "summary": "Phantom-Data is a large-scale, general-purpose dataset designed to address the \"copy-paste\" problem in subject-to-video (S2V) generation. Existing models often fail to follow textual prompts because they are trained on in-pair data (reference and target frames from the same video), leading them to copy irrelevant background details from the reference image. Phantom-Data mitigates this by providing \"cross-pair\" data: identity-consistent reference and target frames from different videos or contexts.\nExperiments using the Phantom-wan model show that training with Phantom-Data significantly improves text-prompt alignment and video quality compared to in-pair training baselines, while maintaining high identity consistency.", "questions": "1. Table 1 states the dataset is \"Publicly Available\". Does this mean the full 1 million pairs (images and corresponding video clips) will be directly downloadable, or will it be a list of URLs/IDs that users need to scrape themselves?\n\n2. Regarding the \"same long-form video\" (L339) constraint for living beings: Did you experiment with trying to find the same human/animal across completely different videos?\n\n3. What is the estimated error rate of the final VLM verification step? Are there many valid cross-pairs that get thrown out because the VLM is too conservative?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The \"copy-paste\" problem is a major, well-known limitation in current open-source video generation models. Tackling this via better data is a highly practical and valuable approach.\n\n2 Unlike previous cross-pair datasets that mostly focused on faces, this dataset covers general objects (products, animals, etc.) and is very large (1 million pairs)\n\n3. The data construction pipeline is well-designed. Specifically, combining large-scale retrieval with strict VLM-based verification  is a smart way to automate generating high-quality pairs without human annotation.", "weaknesses": "1. While the dataset is a great contribution, the pipeline requires an enormous pool of data (53M videos, 3B images) to work effectively. This scale is likely out of reach for most academic labs to replicate or extend on their own.\n\n2. The experimental results primarily compare different custom data setups (in-pair, in-pair + aug, face cross-pair) against their proposed method. There is no comparison against models trained on currently existing public datasets to provide a true external baseline.\n\n3. While quantitative metrics are provided for the different data ablation setups (Table 3), visual examples comparing the outputs of these specific ablations (e.g., \"face only\" vs. \"+ product\" vs. \"+ multi-subject\") would strengthen the argument for the necessity of each data component.", "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": "uyNn1GTjtl", "reviewer": "Reviewer_czCa", "summary": "The paper tackles a central pain point in subject-consistent video generation—the “copy–paste” failure mode that emerges from in-pair training, where a reference frame and target video come from the same clip and the model inadvertently binds identity to background/pose. The authors introduce **Phantom-Data**, a large-scale **cross-pair** dataset (1M identity-consistent pairs across humans, animals, products, IP characters), and a three-stage pipeline to build it: (1) S2V detection via open-vocabulary grounding with quality checks (completeness/specificity/text match), (2) large-scale retrieval from **~53M videos + 3B images** with category-specialized encoders, and (3) prior-guided identity verification (e.g., logo presence for products; same long video for living beings) plus a VLM check to ensure both identity match and context diversity (Fig. 4, p.5). \n\nUsing Phantom-Data to train an open-source subject-consistent model (Phantom-wan), the paper reports clear gains in **prompt following** and **video quality** while keeping **identity consistency** comparable to in-pair baselines (Table 2, p.7). Qualitative examples (Fig. 5, p.8), ablations on subject diversity/scale (Tables 3–4, p.9), and a user study (Table 5, p.15) corroborate the improvements and the importance of cross-pair diversity.", "questions": "None", "limitations": "", "rating": 10, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "* **Addresses a key failure mode.** Clearly identifies and targets the copy–paste issue caused by in-pair supervision, and proposes a principled data solution rather than ad-hoc augmentations (Fig. 2, p.3; Sec. 3). \n* **Well-designed, scalable pipeline.** The three-stage pipeline (detection → cross-context retrieval → prior-guided verification) is systematic, with concrete checks (completeness/specificity; upper/lower similarity thresholds; VLM verification) that materially reduce false positives (Fig. 4–6, pp.5–9). \n* **General beyond faces.** Moves cross-pair training from face-only domains to **general subjects** (humans, animals, products, multi-subject scenes), aligning with real user inputs (Fig. 3(d–e), p.4; Table 1, p.3). \n* **Fair, controlled comparisons.** Same base model, objective, resolution, and inference settings across training regimes; metrics cover text alignment (Reward-TA), identity (DINO/GPT-4o scores), and video quality (VBench), with both quant and qual results (Sec. 4, Table 2, Fig. 5). \n* **Clear, strong empirical gains.** Cross-pair training with Phantom-Data improves prompt alignment and overall video quality, with identity consistency on par with in-pair training (Table 2); ablations show benefits from subject diversity and scaling from 100k → 1M pairs (Tables 3–4).", "weaknesses": "* **Ambiguity around the “Face Cross-pair” baseline (Table 2).** The paper states this baseline “utilizes face-level identity matching across videos,” but the exact construction is unclear relative to **Ours**. Did “Face Cross-pair” (i) rely solely on ArcFace-based retrieval without the **prior-guided verification** step, (ii) omit **clothing/body** features for people, and/or (iii) exclude **non-face** subjects entirely? Clarifying the settings would explain the large Reward-TA gap (3.022 vs. 3.827) and the differences in DINO/GPT-4o scores (Table 2, p.7).", "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)