PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement
Authors: Teng Hu
Zhentao Yu
Zhengguang Zhou
Jiangning Zhang
Yuan Zhou
Qinglin Lu
Ran Yi
Venue: NeurIPS 2025 poster ()
Keywords: video generation
video customization
Primary area: deep_learning

Abstract: Despite recent advances in video generation, existing models still lack fine-grained controllability, especially for multi-subject customization with consistent identity and interaction.
In this paper, we propose PolyVivid, a multi-subject video customization framework that enables flexible and identity-consistent generation. To establish accurate correspondences between subject images and textual entities, we design a VLLM-based text-image fusion module that embeds visual identities into the textual space for precise grounding. To further enhance identity preservation and subject interaction, we propose a 3D-RoPE-based enhancement module that enables structured bidirectional fusion between text and image embeddings. Moreover, we develop an attention-inherited identity injection module to effectively inject fused identity features into the video generation process, mitigating identity drift. Finally, we construct an MLLM-based data pipeline that combines MLLM-based grounding, segmentation, and a clique-based subject consolidation strategy to produce high-quality multi-subject data, effectively enhancing subject distinction and reducing ambiguity in downstream video generation.
Extensive experiments demonstrate that PolyVivid achieves superior performance in identity fidelity, video realism, and subject alignment, outperforming existing open-source and commercial baselines.  More comprehensive video results and comparisons are shown on the project page in the supplementary material.

Poly Vivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement
Teng Hul
Zhentao Yu?
Zhengguang Zhou?
Jiangning Zhang?
Yuan Zhou?
Qinglin Lu
Ran Yi
Shanghai Jiao Tong University
Tencent Hunyuan
*Zhejiang University
Project page:
sttps://sjtupaayee github. po/pro] ects/PolyViiii
young girl holding rousted dook.
playing happlly grass
goose fihning digs
mating with women hand on
man hugglng with women the
drieking sofa.
riding umbrella
Figure 1: Vivid generates high-quality customized videos from multiple subject images and text prompt, which ensures high subject similarity and good subject interaction specified by text.
Abstract
Despite recent advances in video generation, existing models still lack fine- grained controllability, especially for multi-subject customization with consistent identity and interaction. In this paper, we propose PolyVivid multi-subject video customization framework that enables flexible and identity-consistent generation. To establish accurate correspondences between subject images and textual entities, we design VLLM-based text-image fusion module that embeds visual identities into the textual space for precise grounding. To further enhance identity preserva- tion and subject interaction, we propose a 3D-RoPE- based enhancement module that enables structured bidirectional fusion between text and image embeddings. Moreover, we develop an attention innheried identity injection module to effec- tively inject fused identity features into the video generation process, mitigating identity drift. Finally, we construct an MLLM-basee data pipeline that combines MLLM-based grounding, segmentation, and clique-based subject consolidation strategy to produce high-quality multi-subject data, effectively enhancing subject distinction and reducing ambiguity in downstream video generation. Extensive experiments demonstrate that Vivid achieves superior performance in identity
Eqqual Contribution Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

fidelity, video realism, and subject alignment, outperforming existing open- and commercial baselines.
Introduction
In recent years, the field of video generation has witnessed remarkable progress [46 IA 89, M) with the emergence of numerous open-source and commercial video-generation models. These advancements have significant real-world implications, ranging from content creation in the entertainment industry applications in artist design, education, advertising, etc. However, despite these achievements, current video-generation models suffer from a notable limitation insufficient controllability. It remains challenging for these models generate videos precisely tailored to users' specific requirements, which restricts their potential applications in various scenarios.
27
60
Video customization aims to generate videos featuring user-specified subjects. Existing approaches fall into two categories: (1) Instance-specific methods that fine-tune the model for each subject identity [50, S% 23, [=]], which are time- and essource-intensive and (2) End-to-end methods
that extract identity features from subject images and inject them into the generation process. While recent works like ConsisID [56] and MovieGen [[8] show promise, they are limited to single-human scenarios and cannot handle arbitrary subject types or multiple identities.
To support multi-subject video customization, recent methods like ConceptMaster [2I]]. Video Al- chemist [[4], Phantom [22), SkyReels- A2 [[]]), and VACE [ extend single-subject frameworks by incorporating multiple image conditions. However, they still struggle with three key challenges:
(I) learning precise correspondences between subject images and textual entities to model correct actions and interactions; (2) maintaining identity consistency across multiple subjects; and (3) re- solving semantic ambiguity during data construction for accurate text-image alignment.
To address the above challenges, we propose Poly Vivid, a novel multi-subject video customization framework that enables flexible, controllable, and identity-consisten video generation. To establish accurate correspondences between subject images and their textual descriptions, we first design a VLLM-based text-image fusion module. By leveraging the semantic understanding capabilities of Vision Large Language Models (VLLMs), this module encodes subject images into the textual embedding space, enabling the model to correctly ground each image to its corresponding entity in the prompt. To further enhance identity preservation and enable richer subject interactions, we propose a 3D-RoPE- -dentity-interaatioon enhancement module. This module enables struc- tured bidirectional information flow between the text embeddings from VLLM and the subject image embeddings encoded by pretrained VAE, enhancing identity information in text embeddings and injecting interaction semantics into the image embeddings. Finally, to efficiently inject both the identity-enhanced text embeddings and the interaction-enhanced image embeddings, we propose an intentiio-inneeiteed identity injection module, which leverages the pretrained MM-AAtentiins mul- timodal processing capability to construct a new condition injection module. In this way, each frame can receive sufficient conditioning information, ensuring consistent subject appearance and prevent- ing identity drift throughout the video. Additionally, to mitigate confusion between semantically similar entities, such as multiple humans, we develop a MLLM- that integrates MLLM-base based grounding and segmentation with the proposed clique- basque-baseddae subject consolidation module. This pipeline leverages cross-modal cues to enhance subject discriminability and employs clique analysis to filter out inconsistent or transient subjects, further improving the accuracy and robustness of the generated results.
data construction pipeline
Poly Vivid has been extensively experimented on multi-subject video customization. We compared it with existing open-source methods and closed-source commercial software, conducting comprehen- sive comparisons from multiple aspects such as ID consistency, authenticity of the generated videos, and video-text consistency. Extensive experiments show that Poly outperforms all existing methods in multi-subject video customization. The contributions can be summarized in four-fold:
We propose Poly Vivid, a novel multi-subject video customization model that leverages VL.LM models to establish the correspondences between the text prompt and subject im- ages, enabling high subject-consistency video generation and complex subject interactions.
We propose a new 3D RoPF.-based identity-interaction enhancement module to enable structured bidirectional information flow between textual and visual modalities. This mech-

---

anism enhances identity information in the text embedding and enriches the interaction information in the image embeddings, enabling more effective cross-modal interaction.
We design an Attention-inheritedd identity injection module that utilizes the multimodal processing capabilities of the pretrained MM-Attention to develop novel condition injec- tion module. This approach ensures that each frame receives sufficient conditioning infor- mation, maintaining consistent subject appearance and preventing identity drift throughout the video.
We propose a MLLM-based data construction pipeline that combines MLLM-ban -aased grounding, segmentation, and clique-based subject consolidation to improve multi-subject discriminability and reduce subject ambiguity in multi-subject data construction.
2 Related Work
2.1 Video Generation Model
The development of diffusion models [HI. [6] has significantly advanced vision generation [52 19 55]. Early models [2. [2]] extended pre-trained cext-to-image models for continuous video genera- tion by adding temporal modeling. Recently, works [64] 26, 27, 577, [53i]] have employed advanced Diffusion Transformers [SG] [T], trained on large-scale text-video data, to produce longer and higher- quality videos. However, while many models focus text-to-video and image-to-video generation, there is still potential for improving fine-grained controllability in video generation.
2.2 Video Customization
Instance-specific video customization methods use multiple images of the same subject to fine-tune pre-trained video generation model, training each subject separately. Still-Moving [[]] fine-tunes video models with PEFT methods [[5 20 to create static frame videos, then repeats the image as a static video and uses DreamBooth 2
to learn the identity. CustomCrafter [50]] repeats the
image across frames, embeds it into text space, and fine-tunes the model for better identity learning. Custom Video [[S] and DisenStudio [S]] extend customization to multiple subjects by segmenting and combining images, aligning subject identity with text through cross-attention maps. These methods rely on instance-specific optimization, posing challenges for real-time large-scale video customization.
End-to-end video customization methods integrate identity information from target images through additional conditioning networks, which allows for the generalization to various identity
inputs. Earlier works focus on preserving facial identity. For example, ID-Animator [[[]]] uses face adapter and facial identity loss to ensure facial ID consistency. ConsislD [566] captures comprehensive ID information by extracting low- and high-frequency details from facial images. MovieGen [XX] embeds facial ID information into the text space and uses facial images from dif- ferent videos to guide generation, reducing facial copying issues. To customize arbitrary objects, VideoBooth [23] incorporates identity information using coarse CLIP features and fine-grained im- age features. Recent works like ConceptMaster [2]]]. Video Alchemist [7], Phantom [32]], Sky SkyReels- A2 [LЛ]], VACE [24]]. and HunyuanCustom [[X]] extend customization to multiple subjects by linking text prompts to subject images, enabling multi-subject video generation. However, challenges re- main in maintaining and interacting with multiple subject IDs due to the complexity of interactions and mutual influence among them.
3 MLLM--bssed Data Construction
In this section, we outline the creation of our multi-subject customization dataset, emphasizing the grounding and segmentation process. Details on data filtering and captioning are in the appendix.
MLLM-based Subject Segmentation. For subject extraction in videos, previous methods either use GroundingDINO~SAM [ا3], HО] to detect object boxes based on text and then segment the boxes with SAM, or employ MLLM-based segmentors like LISA [2X] to directly segment the subject parts corresponding to the captions. However, these methods have limitations: (1) The GroundingDINO-SAM approach struggles with fine-grained semantic distinctions, such differ-
3

---

VLLM-based Text-image Fusion
Image 30-RoPE
Pher Tode
louilyy Powet
Apandhploping with stop Hugeedl
Brssoder
iouge
Text
LLaVA
Looka
Tean bmage
Mentity
Fibe
Putwrerce
Teat Proned
LLaVA
Tasage Taben
Mull-moddal
Muttiroodal
VIE
cog
Text Tekooo
LoTA
elconced Vadeo
Inaniity Uhe
Tolen Ter
image Tokers
Adtrrtion
Cross
m Layers
Vikee Libort
Ganerzte
Exhance
Figure 2: Framework of our Poly the text prompt and reference image are fused by the VLLM- based text-image fusion module. Then, 3D RoPE-based identity-interaction enhancement module is employed to enhance the text-image interaction. The enhanced image tokens are injected by an MM cross-attention module, which helps preserve the identities while ensuring good subject interaction.
entiating between two people in video, leading to inaccurate caption-to sonton-tt--emmtti correspondence. (2) MLLM-based segmentation methods can better distinguish fine- ane-grained semanties but suffer from fragmented and low-quality masks due to the scarcity of multimodal segmentation data. To address these issues, we adopt a MI.LM-based detection+SAM segmentation paradigm for accurate and efficient multi-subject segmentation. Specifically, we use Florence2 [5]]], a MLsee- de- tection model, to detect subject location boxes in a caption given an image and caption. We then use SAM2 to segment within these boxes, selecting the largest object as the subject. The segmenta- tion is considered valid if the CLIP score between the segmented subject and the corresponding text exceeds a certain threshold.
Clique-based Subject Consolidation. Finally, further enhance the accuracy of multi-subject de- tection and segmentation and prevent any subject from flectingly appearing in the video, we extract CLIP features for each subject image and construct a graph = (V, E), where each node represents subject image. We calculate the feature distance between each pair of subject images, connecting them with an edge if the distance is below certain threshold. We then iteratively detect the max- imum clique (a subgraph where every pair of nodes is connected) in the graph. If the number of nodes in the maximum clique exceeds one-third of the total detected frames, we extract from the graph and continue searching for the next maximum clique, until the maximum clique has fewer nodes than one-third of the total detected frames. Ultimately, we obtain multiple cliques, each rep- resenting a reference image of subject in the video. This algorithm effectively avoids errors in detection and segmentation certain frames and removes subjects that appear in only a few frames, thereby improving the quality and robustness of the multi-subject generation model.
4 Method
Vivid is proposed for multi-subject video customization. It generates videos from multiple sub- ject images /2... (n) and text prompt describing scenes, actions, and interactions, ensuring identity preservation and accurate text-specified interactions. The framework is shown in Fig. Z First, VLLM-based text-image fusion module encodes both text and subject images into the text space as {2T,T, 2T.1.. capturing high-level semantic associations. To address LLaVA's limitation in fine-grained identity details, we use a pretrained VAE extract detailed visual identity features from the subject images, obtaining image embeddings 21. Next, an identity-interactio enhancement module with text-image interaction 3D RoPE facilitates mutual information flow between modal- ities. Image tokens inject identity information into text tokens, while text tokens provide interaction cues to image tokens, resulting in eennnnt-enhhnneed text tokens and interaction-awan image tokens. Finally, an Attention-Inheriter Identity Injection Module injects interaction-enhant kens into the video latent space via an MM cross-attention mechanism to ensure consistent identity preservation, and the identity-enhanced text tokens guide subject-specific interactions during video
image to-

---

generation using the pretrained MM-attention. By integrating these components, Poly Vivid achieves high-fidelity video generation with identity-consisent and text-aligned multi-subject interactions.
4.1 VLLM-based Text-image Fusion
In video customization tasks, the model first needs to learn the relationship between the input text and images to identify which subject image corresponds to which entity in the text, enabling the gen- eration of corresponding subject actions as described the prompt. However, how to effectively in- tegrate image-text information has been a key challenge for previous customization methods. These methods usually take subject image features and text embeddings as two separate conditions, and ei- ther lack a design for interactive understanding between them (lacks the learning of correspondence between text entities and subject images), or rely on additional newly trained network branches to achieve this interaction, causing the model prone to confusing different subjects within the gener- ated video. To facilitate efficient image-text interaction understanding. Poly Vivid leverages the text comprchension capabilities trained in the LLaVA [[]]] text space by Hunyuan Video [M7]] and uti- lizes LLaVA's multimodal interaction understanding to build the connection between the text and
images [3 24
Given a text input T and several subject images (I....., Jn), each with corresponding descrip- tion word {T Ir.n} in the text, design structured template explicitly link each image with its textual entity. This template is processed by LLaVA to learn multi-modal correla- tions between text and image identities. LLaVA is a multi-modal model trained for visual question answering and dialogue tasks. It uses a conversation-style prompt where images and texts are in- terleaved, employing pretrained vision encoder (i.e., CLIP-VIT) to extract image features, which are integrated with text tokens through causal language modeling. Our structured template appends identity prompts after the input text prompt, associating each subject word with its corresponding subject image, formatted as:
<SEP>
The Tı,1 looks like <image
1>. The T22 looks like <image 2>. ...
(1)
Text prompt
Identity prompt
Identity prompt
where <SEP> is the special token marking the end of dialogue round; <image i> is the token for subject image i. For example, with the text prompt "A man is playing guitar", the resulting template is "A man is playing guitar. <SEP> The man looks like <image 1>. The guitar looks like <image 2>".
When the template is input to LLaVA. each <image i> token is replaced with visual tokens ex- tracted from the corresponding image using a CLIP image encoder, typically resulting in 24 x 24 tokens per image. This structured template is then processed by LLaVAs autoregressive language model to produce joint multi-modal embeddings. Due to the much longer sequence of image tokens compared to text tokens, directly appending them can cause the model be overly influenced by vi- sual content, disrupting textual understanding. To address this, the insertion <SEP> acts as a soft delimiter, reducing interference between text and image parts while allowing LLaVA to correctly associate subjects with their visual references. After LLaVA processes the multimodal image-text interaction, the output is fused text embeddings zr, which can serve as text inputs for the video generation model to synthesize customized videos that reflect the specified subjects and contextual information.
4.2 Identity-Inerraction Enhancement by Text-image Interaction
The LLaVA model used in our text-image fusion (Sec. as multimodal understanding frame- work, is primarily designed to capture high-level semantic correlations between text and imagessuch as category, color, and shapewhile often overlooking finer-grained details like textural cues and de- tailed visual attributes. However, in the context of video customization, these fine details are crucial for accurate identity preservation, making the LLaVA branch alone insufficient. To complement this, we leverage the pretrained VAE from the base video generation model (Hunyuan Video) to encode subject images into image embeddings =,, which effectively retain identity-specifff information. So far, we have obtained a text embedding 27 from LLaVA (Sec. [. that captures interaction-relant semantics but lacks detailed identity information, and an image embedding ž, that captures identity features but lacks subject interaction context. To compensate for these missing information, we pro-
5

---

pose a text-image interaction module based on 3D-interacted RoPE, designed to enhance identity features in 27 and enhance interaction semantics in 2/.
Text-image Interaction Module. Our hase video generation model (Hunyuan is built upon Multimodal Attention Framework, where each model block contains MM-attention module that integrates text and video embeddings to establish cross-modal interactions and achieve video-text alignment. Since a subject image can be viewed as single-frame video, we repurpose the MM- attention mechanism to facilitate interaction between text and image embeddings. This enables the injection of identity information from image embedding 21 into text embedding 2T and, con- versely, the infusion of semantic interaction cues from text embedding 2T into image embedding 21. Specifically. the MM-attention module in the base model contains the Query, Key, and Value matrices for both video embedding V and text embedding T. i.e., WY (g.k.o) for video embedding,
and W (q.k.u) for text embedding. To effectively process the image and text embeddings, we adopt a LoRA [[L5]] approach to fine-tune the Query, Key, Value matrices, and feed- forward network (FFN) weights, enabling efficient and effective adaptation of the MM-attention layers for text-image fusion:
2/4
( =T), (WV (zr), WT 2T), (Wy (21), WT 2T)),
â,, = - (FÉN(E))FFNN
where ( (V:T. N.-(V.) ,FFN) are the weights combined by the pretrained MM- Attention and LoRA module.
Text-image Interaction 3D-RoPE. Our text-image interaction module takes both text embedding and image embedding as inputs, aiming to enhance identity information in the text embedding and enrich interaction semantics in the image embedding. However, directly integrating these modal- ities through a standard attention mechanism often fails to capture their mutual correspondence effectively, as text tokens are organized as mie-dimeniional sequences, while image tokens exhibit a wwo-dimensional spatial structure. This mismatch hinders the alignment and fusion of informa- tion between modalities, leading to ineffective interaction modeling. To overcome this challenge, we propose a text-image interaction 3D-RoPE, which facilitates structured and fine-grained po- sitional encoding for both modalities, in order to bind text tokens and image tokens of the same subject. This design enables more effective cross-modal interaction while preserving the intrinsic semantics of each modality.
Specifically, the text embedding 2т consist of two types of tokens: 2т, and æT,1, where ZT, is derived from the input text prompt and 2T, is derived from the input subject images. The text tokens are represented as:
ZT (
21 T..
2T2 TT.I
m2t
m2 T..
TL TT,
T.II
TTT
lext
<image 1>
text
<image 2>>
We denote the text tokens 2T,7 encoded from the text prompt as <text> tokens, and the text tokens ZT,J encoded from the subject image as <image> tokens. In addition, the 'image" tokens 2ι refer to the tokens extracted from the subject image by the VAE encoder. The differences between these two kinds of image tokens are that: The <image> tokens 21,1 (obtained from LLaVA) reside in the text space and capture rich interaction information derived from the text, while the 'image" tokens 21 (obtained from VAE encoder) retain detailed identity features extracted from the subject image.
Our text-image interaction 3D>RoPE is shown in Fig. 21 To preserve the source text information, it is crucial to maintain the sequential relationship in 27,7. Therefore, we assign the <texD> tokens in
2T, with position encodings of 3D RoPE, setting the spatial dimension to (0,0), and assign them sequentially along the temporal dimension. Take the first subject as an example, the RoPE position indices for its <lext> tokens are set as:
Idx IdrRoPE(<T.r) = O),, i i= m1-
This method preserves the original sequential relationship of øт, along the temporal axis. To model the interaction between <lext> tokens and <image> tokens, we assign the <image> tokens with 3D RoPE of temporal index m, 1 and expanded along the spatial axis, centering them at (0, 0) in the spatial dimensions. The RoPE position indices for the first subject's 's <image> tokens are set as:
Idx ( = (ml + 1, 1
w J. mod h - 1 17 i 1,.... wh.
(5)
2

---

Video
Image
Image
Image
QKV Concet
QKV Concat.
Image Encoder
Text Intertation
MM Attention
LoRA
Sharing MM Altemtion
K Altention
K
V
LoRA
Allention
Croes Allention
Irjection
Projection
Projection
MM Attention
Apply
Attontion Module
MM-AAttettoon Module
Vidco
(a) Concatenation
(b) Adaptor
Figure 3: Comparison of the condition injection strategies for MM-DiT.
where w and h are the width and height of the encoded image, respectively. Since the same subject's <text> tokens' and <image> tokens' RoPE positions close in temporal dimension, their position encodings are close, thereby stronger correlations are more likely to be obtained MM-Attention.
For the 'image' tokens 21, we assign them with 3D RoPE of temporal index my + 2 and spatial indices aligned with those of the <image> tokens. This alignment facilitates efficient pixel-by by by-pixel interaction between the two sets of image tokens. The RoPE position indices for the first subject's s "image" tokens 21 are set as:
h 1, 1 mod h- 12122 i= 1,... wh,
Ide RoPE (2+) (m1 + 2,
(6)
For multi-subject inputs, expand them iteratively along the time axis, with the second subject's <text> tokens starting from (m1 + 3, 0, 0). The relationships among <text>, <image>, and 'image' tokens remain consistent as described in Eq. (EI) to Eq.
This text-image 3D RoPE effectively preserves the sequential relationship between <texD and <im- age> tokens. Additionally, it enables efficient pixel-by-pixel interaction between <image> tokens and 'image" tokens, enhancing the identity the <image> tokens. Furthermore, since 'image' to- kens are embedded between text tokens through this 3D RoPE, they can efficiently interact with text tokens, thereby enriching the interaction information in 'image" tokens. With this text-image inter- action 3D-RoPE, it enables structured bidirectional information flow between text and image tokens via Eq. (,), resulting in identity-enhanced text tokens êT and interaction-enhane image tokens ε₁-
4.3 A Attention-inherited Identity Injection
With the identity ehhancey-enhance text tokens êτ and interaction-enhancee image tokens ε, obtained in Sec. A.2. the remaining challenge is how to effectively inject both types of tokens into the video generation process produce subject-comsistent videos aligned with the text-described context.
Problems in Current Controllable Generation Methods. Our model is built upon Hunyuan Video, which is based on MM-DiT, and we consider two common condition injection strategies used in MM- DIT: Token concatenation (Fig. (a)), as used in OmniControl [А involves concatenating the condition tokens with the latent video tokens and using MM AM-Attention to learn their interaction. 2) Adapter-based methods (Fig. (b)), such as IP-Adapter [5:4], extract condition features via an ex- ternal image encoder and inject them into the pretrained model through cross-attention layers. Both approaches are originally designed for image generation, and applying them directly to video gen- eration presents challenges. In token concatenation, appending image tokens before or after video tokens can lead to temporal imbalance, i.e., frames distant from the condition image receive weaker guidance, resulting in identity degradation. Adapter-basee based methods struggle to inject information effectively into MM-DiT due to the large dimensionality its feature space (considering the long token sequence of video); the adapter's 's features often misalign with the pretrained model's 's internal representations, hindering effective conditioning.
ineeeitoninheriteed Identity Injection. To overcome the limitations of existing controllable gen- eration methods, we propose a novel Attention Altention-nnheritedd Identity Injection Module. The "Attention- Inherited" principle refers to parameter-sharte
mechanism designed for efficiency and perfor- mance. At its core, we utilize single set of MM-Attention parameters inherited from the pretrained base model. This foundational weight set is shared across three key components: the text-image
7

---

Poonatt Annonn & chonel graendaly Innouts thl basdieg
Refenence
Pita
Vida
Kelig
SkyReel
Ours
Figure 4: Comparison on multi-sun multi-subject video customization.
attention, the text-video attention, and our identity injection cross-attention module (see Fig. B (c)). reparameterize the Key and Value matrices of the video tokens using a LoRA module to incorpo- rate image token information, and reparameterize the Query matrices using another LoRA module to maintain video-specific representations. This setup constructs a multi-modal cross-attention mechanism where image tokens ê injected into the video token stream 2, ensuring effective identity injection. This design treats all video frames equally, eliminating disparities between ear- lier and later frames of the token concatenation strategy, thus mitigating identity degradation time. To stabilize early-stage training, we use a zero-initialized fully-connected layer to project the cross-attention output, reducing the impact of randomly initialized attention weights. The injection process is formulated as:
2T))
(7)
2' = -Cross-AttnWW (=), WV (&1), WY (E1)),
(8)
== FF(FFC(())())
Eout, 2T, MMM-Attr.yll
(10)
where MM-Atter, and M-Atttt are the Multi-Modal Attention modules for text-image q W. WVV FFFN are composed of
and text-video interaction, respectively. The parameters { weights from the pretrained MM-Attention and a LoRA module. FC(.) is zero-initialized fully connected layer, and denotes the identity-enhanced video tokens.
After the identity injection, we utilize the original MM-Attention module from the pretrained model establish the connection between the identity-enhanee text tokens êτ and the identity-enhane video tokens 2. This allows the text information to be effectively integrated into the video tokens without compromising identity integrity, thereby supporting both strong identity preservation and vivid subject interaction generation.
Experiments
5.1 Implementation Details
Baselines. We compare Vivid with the state-of-the arts video customization methods, including commercial products (Vidu-2.0 [45], Kling-1.6 [25], Pika [37],, and Hailuo ([3]] and open-sourced methods (Skyreels-A2 [M]]] and VACE-1.33 1.3B [24]).. For each model, we generate 100 videos, which
are employed to compute the quantitative metrics. More implementation details are presented the
appendix.

---

Prompt
Prompt pecsss fulding umbrealle
Beference
drisinn the tolk
Vida
Kalng
Skyrobs
VACE
Ours
Figure 5: Comparison of three-subject customization.
5.2 Comparison Results on Multi-subject Customization
Qualitative two-subject customization comparisons. We conduct experiments on four types of multi-subject customized video generation to evaluate the effectiveness of our model: (1) Rigid human-object interaction (e.g., a person holding an object); (2) Non-rigid human-object interaction (e.g., person wearing flexible clothing); (3) Human-human interaction; and (4) Object-object in- teraction. In Fig. g (with Rigid human-object interaction and Human-humes interaction included in the appendix), we compare our model with the state-of-the-ar methods. It can be seen that Pika often produces blurry outputs when handling complex interactions. Vidu and Kling struggle to dress the human subjects in the specified clothing. Vidu and VACE confuse animal features, generating a tiger with the shape of giraffe or giraffe with the shape of tiger. SkyReels-At introduces noticeable artifacts and inconsistent transitions between frames, and struggles to accurately capture the relative size of different animals (the generated tiger is bigger than the giraffe). Furthermore, all baseline methods except Kling suffer from significant identity degradation, especially in humans. In contrast, our Poly Vivid generates videos with high identity consistency while effectively modeling complex interactions between multiple subjects as specified by the text prompts.
Qualitative three-subject customization comparisons. Our model is not limited to two-subject customization. We present additional comparison results for three-subject video customization in Fig. E As shown, Pika, Vidu, Skyreels A2, and VACE all exhibit significant identity loss. While Pika and Vidu are able to generate correct interactions that adhere to physical rules, Skyreels A2 and VACE produce unrealistic frames in which the person and tiger appear pasted into the sky. violating physical plausibility. Kling maintains a relatively good identity preservation, but there is still room for improvement. In contrast, our model achieves the best identity preservation and able to generate realistic interactions among multiple subjects while following physical rules, demonstrating our superior capability in multi-subjedd customization.
Quantitative Comparisons. To further demonstrate the effectiveness of our approach, we construct a benchmark test set containing 100 object pairs, each associated with a corresponding interaction text prompt. We apply each baseline method to generate 100 videos and evaluate the results using comprehensive set of quantitative metrics, including face and object similarity, text-video alignment, and overall video quality. The comparative results are summarized in Tab. M1 As shown in the ta- ble, Vivid Vivid achieves the highest similarity scores for both face and object identity (Face-sim and DINO-Sim). highlighting its strong capability preserving key appearance features across video frames. In terms of text- video aligmment, our method obtains the best and second- best CLIP scores among all competitors, suggesting that the generated content accurately reflects the intended seman- ties of the text prompts. Furthermore, Poly Vivid yields the lowest FVD score, indicating superior video quality and diversity. is worth noting that the FVD is computed against reference set of 1,500 high-quality 4K videos, further underscoring the realism of our results. While our method ranks third in temporal consistency, we observe that certain videos generated by Vidu and VACE remain mostly static, which slightly inflates their temporal consistency scores. Despite this, our

---

Table 1: We compare Poly Vivid with state-of- the--rrrttt--tttt video customization methods across multiple metries. Bold and underline represent the best and second best results, respectively.
Metric
Face-sim
DINO-sim 1
CLIP-B
CLIP-L 7
FVD 1
Temporal f
VACE-L.3B
0.433
0.598
0.335
0.280
1171.42
0.966
SkyRecls-A2
[
0.554
0.619
0.352
0.276
1379.65
0.941
Kling-1.6 126
0.534
0.554
0.330
0.280
1049.70
0.934
Vidu-2.0 [S]
0.532
0.588
0.336
0.282
1083,35
0.970
Pika [5x]]
0.546
0.548
0.310
0.263
980.49
0.942
Vovy (Ours)
0.642
0.623
0.336
0.281
959.74
0.964
Table 2: Quantitative Ablation Study.
LLaVA
Text-Ing Internction
Text-Img 3D-RoPF
ID- injeation
Face- simi
DINO-
CLIP-B
CLIP-L
FVD
Temporal
sim f
0.381
0.521
0.345
0.291
1150.48
0.950
0.345
0.496
0.334
0.280
1052.34
0.961
0.584
0.581
0.330
0.279
1052.14
0.965
0.605
0.340
0.286
965.62
0.963
0.642
0.623
0.336
0.281
959.74
( 0.964
LLaVA + Adaaierr
0.401
0.532
0,338
0.285
1020.35
0.952
ILaVA Dokenn-Conctteaatinn
0.628
0.615
0.328
0.271
980.56
0.960
Text-Only LLaVA + Token Concatenation
0.543
0.532
0.330
0.267
1132.12
0.959
method still demonstrates strong temporal coherence. In summary, Poly Vivid not only delivers the best performance keeping identities across humans and objects, but also achieves strong semantic alignment and generation quality, validating its effectiveness for multi-subject video customization.
5.3 Ablation Study
In this section, first compare our model with two condition injection strategies: (1) adapter-based injection and (2) token concatenation based injection. For token concatenation, we additionally com- pare with ablated model with text-only LL (i.e., without text-image fusion). We then conduct an ablation study on four key components of our framework: (1) the text-image fusion module based on LLaVA, (2) the text-image interaction module, (3) the text-image interaction 3D-RoPF, and (4) the identity injection module. Quantitative results are shown in Tab. A The adapter-base model struggles capture identity information, yielding only marginal improvements over the baseline with LLaVA alone. The token concatenation approach maintains identity better, but at the cost of reduced text-image aligmment, as reflected by lower CLIP score. Moreover, the model with text-only LLaVA results in poor identity consistency due to the limitation in distinguishing differ- ent identities. Individually, the LLaVA fusion and text-image interaction module do not provide strong identity preservation, but their combination leads to a notable improvement. Incorporating our proposed text-image interaction 3D-RoPE further enhances both identity consistency and text alignment, demonstrating its effectiveness for text-image interaction. Finally, the identity injection module significantly boosts identity preservation and achieves the best FVD score, confirming its effectiveness in subject-consistency video generation. By integrating all proposed modules, method achieves strong identity preservation, accurate text-video alignment, and high-quality video generation.
6 Conclusion
In this paper, we present Vivid, a novel multi-subject video customization framework that ad- dresses the key limitations of existing methods in controllability, identity consistency, and complex subject interaction. By incorporating a VLLM-based vision-language fusion module, 3D-RoPE- based identity-interaction enhancement module, and an our model effectively bridges the gap between text and image modalities while preserving subject identities throughout video generation. Furthermore, our proposed multi-subject data construction pipeline enhances the ability to distinguish semantically similar entities, ensuring reliable multi- subject customization. Extensive experiments demonstrate that Vivid significantly outperforms prior state-of-the-art methods in identity consistency, text alignment, and video realism, achieving superior performance in multi-subject video customization, which opens up new possibilities for controllable and high-fidelity video generation in real-world applications.
identity injection module,
10

---

Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 62302297), the Fun- damental Research Funds for the Central Universities (YG2023QNB17, YG2024QNA44) National Key R&D Program of China (20240)00115000, National Natural Science Foundation of China (No, 72192821, 62272447, 62472282, 62472285), Young Elite Scientists Sponsorship Program by CAST (2022QNRC00), Beijing Natural Science Foundation (L222117).
References
[1] Bai, K. Chen, X. Liu, J. Wang, W. Ge, S. Song, K. Dang. P. Wang. S. Wang. Tang, et al. Qwen2. 5-vl technical report. arXiv preprini arXiv:2502. 13923, 2025.
[2] A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y. Levi, Z. En- glish, V. Voleti, A. Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127 2023.
[3] J. Carreira and A. Zisserman. Quo vadis, action recognition? new model and the kinetics dataset. In proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pages 6299 6299-6308, 2017.
[4] H. Chefer, S. Zada, R. Paiss, A. Ephrat, O. Tov, M. Rubinstein, L. Wolf, T. Dekel, T. Michaeli, and I. Mosseri. Still-moving: Customized video generation without customized video data. ACM Transactions Graphics (TOG), 43(6): 2024.
[5] H. Chen, X. Wang, Y. Zhang, Y. Zhou, Z. Zhang, S. Tang, and W. Zhu. Disenstudio: Cus- tomized multi-subject text-to-video generation with disentangled spatial control. In Proceed- ings of the 32nd ACM International Conference on Multimedia, pages 3637-3646, 2024.
[6] T.-S. Chen, A. Siarohin, W. Menapace, E. Deyneka, H.-w. Chao, B. E. Jeon, Y. Fang, H.-Y. Lee, J. Ren, M.-H. Yang, et al. Panda-70m: Captioning 70m videos with multiple cross- modality teachers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13320-13333 2024.
[7] T.-S. Chen, Siarohin, W. Menapace, Y. Fang, K. Lee, I. Skorokhodov, K. Aberman, J.-Y. Zhu, M.-H. Yang. and S. Tulyakov. Multi-subject open-set personalization in video generation. arXiv preprint arXiv:2501. 006177000687..... 2025.
[8] Y. Chen, J. Zhang, T. Hu, Y. Zeng, Z. Xue, Q. He, C. Wang, Y. Liu, X. Hu, and Yan. Ivebench: Modern benchmark suite for isstrctioo-guudee video editing assessment. arXiv preprint arXiv:2510.1 1647, 2025.
[9] J. Deng, J. Guo, N. Xue, and S. Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690-4699, 2019.
[10] Esser, S. Kulal, Blattmann, Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine learning, 2024.
[11] Z. Fei, D. Li, D. Qiu, J. Wang. Y. Dou, R. Wang. J. Xu, M. Fan, G. Chen, Y. Li, et al. Skyreels- a2: Compose anything in video diffusion transformers. arXiv preprint 250::2504.02436, 2025.
[12] Y. Guo, C. Yang, A. Rao, Z. Liang, Y. Wang, Y. Qiao, M. Agrawala, D. Lin, and B. Dai. An- imatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725, 2023.
[13] Hailuo. Hailuo. https://hailuoat. video/ 2025.
[14] X. He, Q. Liu, S. Qian, X. Wang, T. Hu, K. Cao, Yan, and J. Zhang. Id-animator: Zero-shot human video generation. arXiv preprint arXiv:2404115275. 2024.

---

[15] E. J. Hu, Y. Shen, P. Wallis, Z. Allen Zhu, Y. Li, S. Wang, L. Wang, W. Chen, et al. Lora: Low-rank adaptation large language models. ICLR, 1(2)3, 2022.
[16] Hu, J. Zhang, L. Liu, R. Yi, S. Kou, H. Zhu, X. Chen, Y. Wang, C. Wang, and L. Ma. Phasic content fusing diffusion model with directional distribution consistency for few-shot model adaption. Proceedings of IEEE/CVF international conference on computer vision, pages 2205,-211,.21 2023.
[17] т. Hu, J. Zhang, R. Yi, Y. Wang, H. Huang, J. Weng, Y. Wang, and L. Ma. Motionmaster: Training-free camera motion transfer for video generation. arXiv preprint arXiv:2404.15789 2024.
[18] T. Hu, 7. Yu, 7. Zhou, S. Liang, Y. Zhou, Q. Lin, and Q. Lu. Hunyuancustom: A multimodal- driven architecture for customized video generation. arXiv preprint (rXiv:2505004522, 2025.
[19] T. Hu, J. Zhang, Z. Su, and R. Yi. Ultragen: High-resolution video generation with hierarchical attention, 2025. URL
org. http://aaxiiv.ooraaa////// 18775.
[20] T. Hu, Zhang, R. Yi, H. Huang, Wang, and L. Ma. High-efficient diffusion model fine- tuning with progressive sparse low-rank adaptation. In 13th International Conference Learn- ing Representations, ICLR 2025, pages 92066 92078.9.78. International Conference on Learning Representations, ICLR, 2025.
[21] Y. Huang, Z. Yuan, Q. Liu, Q. Wang, X. Wang, R. Zhang, P. Wan, D. Zhang, and K. Gai. Conceptmaster: Multi-concept video customization on diffusion transformer models without test-time tuning. arXiv preprint arXiv:2501.04m0 2025.
[22] Z. Huang, Y. He. J. Yu, F. Zhang, C. Si, Y. Jiang, Y. Zhang, T. Wu, Q. Jin, N. Champaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21807-21818, 2024.
[23] Y. Jiang, T. Wu, S. Yang, C. Si, D. Lin, Qiao, C. C. Loy, and Z. Liu. Videobooth: Diffusion- based video generation with image prompts. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 6789, 2024.
[24] Z. Jiang, Z. Han, C. Mao, J. Zhang, Pan, and Y. Liu. Vace: All-in-one video creation and editing. arXiv preprint arXiv: arXiv:2503.075598 2025.
[25] Keling. Keling,
2025.
[26] R. Khanam and M. Hussain. Yolov11: An overview of the key architectural enhancements. arXiv preprint a1X1v22410117255, 2024.
[27] W. Kong, Q Tian, Z. Zhang, R. Min, Z. Dai, Zhou, J. Xiong, X. Li, B. Wu, Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603 2024.
(28) X. Lai, 7. Tian, Y. Chen, Li, Y. Yuan, S. Liu, and J. Jia. Lisa: Reasoning segmentation via large language model. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pages 9579-9589, 2024.
[29] D. Li, J. Li, and S. Hoi. Blip- diff-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing. Advances in Neural Information Processing Systems, 36: 30146-30166, 2023.
[30] S. Liang, Z. Yu, Z. Zhou, T. Hu, H. Wang, Y. Chen, Q. Lin, Y. Zhou, X. Li, Q. Lu, and Z. Chen. Omniv2v: Versatile video generation and editing via dynamic content manipulation, 2025. URL https://arxiv.ot wttp://////.aa.....////////
[31] H. Liu, C. Li. Q Wu, and Y. Lee. Visual instruction tuning, Advances in neural information processing systems, 36:34892-34966. 2023.
[32] I.. Liu, Ma, B. Li, Z. Chen, Liu, Q. He, and X. Wu. Phantom: Subject-consistent video generation via cross modal alignment. arXiv preprint 11779 2025.
12

---

[33] S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, Q. Jiang, C. Li, J. Yang, H. Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European Conference on Computer Vision, pages 38-55. Springer, 2024.
[34] Y. Liu, K. Zhang, Li, Z. Yan, C. Gao, R. Chen, Z. Yuan, Y. Huang, H. Sun, J. Gao, et al. Sora: review on background, technology, limitations, and opportumities of large vision models. arXiv preprint arXiv:2402.17177, 2024.
[35] M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haz- iza, F. Massa, A. El-Nouby, al. Dinov2: Learning robust visual features without supervision. arXiv preprint ar arXiv:2304.07193, 2023.
[36] W. Peebles and S. Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4195-4205, 2023.
[37] Pika. Pika. https: art/, 2025.
[38] A. Polyak. A. Zohar, A. Brown, A. Tjandra, A. Sinha, A. Lee, A. Vyas, B. Shi, C.-Y. Ma, C.-Y. Chuang, D. Yan, D. Choudhary, D. Wang, G. Sethi, G. Pang. H. Ma. et al. Movie gen: A cast of media foundation models, 2025. URL https://arxiv wwwpxiiv.orggaaae wwtp:/gg///aaaaeeeeeeooeee abt/////.///....../ 13720.
[39] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference machine learning, pages 7748-8763. PmLR, 2021.
[40] N. Ravi, V. Gabeur, Y.TT. Hu, R. Hu, C. Ryali, T. Ma. H. Khedr, R. Rädle, C. Rolland, L. Gustafson, et al. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714. 2024.
[41] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image syn- thesis with latent diffiusin models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022.
[42] N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 22500-22510 2023.
[43] K. Song. Y. Zhu, B. Liu, Q. Yan, A. Elgammal, and X. Yang. Moma: Multimodal Ilm adapter for fast personalized image generation. European Conference on Computer Vision, pages 117-132. Springer, 2024.
[44] Z. Tan, S. Liu, X. Yang, Q. Xue, and X. Wang. Ominicontrol: Minimal and universal control for diffusion transformer. arXiv preprint arXwv:2411.15098, 3, 2024.
[45] Vidu. Vidu. https://wwn vidu. . cn/, 2025.
[46] A. Wang, B. Ai, B. Wen, C. Mao, C.-W. Xie, D. Chen, F. Yu, H. Zhao. J. Yang, J. Zeng, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint
arXiv:2503.20314 2025.
[47] Q. Wang, Y. Shi, J. Ou, R. Chen, K. Lin, J. Wang. B. Jiang, H. Yang, M. Zheng, X. Tao, et al. Koala-36m: A large-scale video dataset improving consistency between fine-grained conditions and video content. arXiv preprint wxXiv:2410.08260. 2024.
[48] Z. Wang. A. Li, L. Zhu, Y. Guo, Q. Dou, and Z. Li. Customvideo: Customizing text-to-video generation with multiple subjects. arXiv preprint arXiv:2401, 099662, 2024.
[49] H. Wen, H. Kang, J. Ma, Huang, Yang, H. Lin, Y.-K. Lai, and K. Li. Dyerowd: To- wards dynamic crowd reconstruction from large-scene video. IEEE Transactions Pattern Analysis and Machine Intelligence, 2025.
[50] Wu, Y. Zhang, X. Wang, X. Zhou, G. Zheng, Z. Qi Y. Shan, and X. Li. Customcrafter: Customized video generation with preserving motion and concept composition abilities. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 8469-8477, 2025.
13

---

[51] B. Xiao, H. Wu, W. Xu, X. Dai, H. Hu, Y. Lu, M. Zeng, C. Liu, and L. Yuan. Florence-
2: Advancing unified representation for a variety of vision tasks. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 4818 4829, 2024.
[52] Z. Xue, J. Zhang. T. Hu, H. He, Y. Chen, Y. Cai, Y. Wang, C. Wang, Y. Liu, X. Li, al. Ultravideo: High-quality uhd video dataset with comprehensive captions. arXiv preprint arXiv:2506.13691 2025.
[53] Z. Yang. J. Teng, W. Zheng, M. Ding, S. Huang, J. Xu, Y. Yang, W. Hong. X. Zhang, G. Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06077 2024.
[54] H. Ye, J. Zhang, S. Liu, X. Han, and W. Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint (rXiv:2338.06622) 2023.
[55] R. Yi, T. Hu, M. Xia, Y. Tang, and Y.-J. Liu. Feditnet++: Few-shot editing of latent semantics gan spaces with correlated attribute disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[56] S. Yuan, J. Huang, X. He, Y. Ge, Y. Shi, L. Chen, J. Luo, and L. Yuan. Identity-preservinn text-to-video generation by frequency decomposition. arXiv preprint axXiv:241.114440 2024.
[57] Y. Zhou, Q. Wang, Y. Cai, and H. Yang. Allegro: Open the black box of commercial-level video generation model. arXiv preprint 240:::2411.155555 2024.
14

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction have clearly included the motivations, important assumptions, and contributions made in the paper.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals motivation as long as it is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
The authors have discussed the limitations of the work in the supplementary material.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The au- thors should reflect on how these assumptions might be violated practice and what the implications would be.
The authors should reflect the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the ap- proach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-tex system might not be used reliably provide closed captions for online lectures because fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach ad- dress problems of privacy and faimeess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
15

---

Answer: [Yes]
Justification: The authors have clearly provided the full set of assumptions and complete proof' in the Method section.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theo- rems.
The proof' can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be comple- mented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclu- sions of the paper (regardless of whether the code and data provided not)?
Answer: [Yes]
Justification: The authors have presented all the experimental details in the Implementation Details section.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is a dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, if the contribution is specific model and empirical evaluation, may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate the research performed.
While NeurIPS does not require releasing code, the conference does require all sub- missions to provide some reasonable avenue for reproducibility, which may depend on the nature the contribution. For example
(a) the contribution primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.
(b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results way to re- produce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case au- thors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing or verifying the results.
16

---

5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, described in supplemental material?
Answer: [No]
Justification: This project is relatively large project that contains lot of real name infor- mation. We will open-source the code after the paper is published.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.cc.
httia///uuides//ddeuubmmsaeeeee wtt.bbmmmssooooooocy...o. for more details.
While we encourage the release of code and data, we understand that this might not be possible, so No an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https:)
7 //ipse.cc/pblli//uuudda Coaeeteese/ooeeeeeeiiiieeee
seioriiiiiiioooooocc.. for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental Setting/Details
Question: Does the paper specifyy all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: The authors have presented all the training and test details in the Implementa- tion Details section. Also, the codes submitted as Supplementary Material.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment Statistical Significance
Question: Does the paper report error bars suitably and correctly defined or other appropri- ate information about the statistical significance of the experiments?
Answer: [No]
Justification: Since the evaluation metrics are computed deterministically based on fixed procedures, without random components or repeated trials, we do not report eтror bars or statistical significance tests.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emoor bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
17

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
is OK to report I-sigma error bars, but one should state it. The authors should prefer- ably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
If error bars reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments Compute Resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: The authors have included sufficient information the computer resources in the Implementation Details.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental rums as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported the paper (e.g., preliminary or failed experiments that didn't t make it into the paper).
9. Code Of Ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics httpps///eerrppp..cppppll////tia
Answer: [Yes]
Justification: The authors conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make to preserve anonymity (e.g., if there is special consid- eration due to laws or regulations their jurisdiction).
10. Broader Impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: The authors have explained the broader impacts of the work at the end of the introduction and conclusion section.
Guidelines:
18

---

The answer NA means that there is no societal impact of the work performed.
If the authors answer NA No, they should explain why their work has societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact spe- cific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitiga- tion strategics (c.g., gated release of models, providing defenses addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g.. pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by re- quiring that users adhere usage guidelines restrictions access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: The authors have cited the original paper that produced the code package or dataset.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
19

---

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
assets are released, the license, copyright information, and terms use in the pack- age should be provided. For popular datasets, ssperswithcods com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the cense of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors encouraged to reach out to the asset's ' creators.
13. New Assets
Question: Are new assets introduced in the paper well documented and is the documenta- tion provided alongside the assets?
Answer: [Yes]
Justification: The new assets introduced in the paper are accompanied by detailed docu- mentation, which is provided in the supplemental material.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and Research with Human Subjects
For crowdsourcing experiments and research with human subjects, does the pa- per include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Question:
Answer: [NA]
Justification:
The paper does not involve crowdsoureing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Including this information in the supplemental material is fine, but if the main contri- bution of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, cura- tion, or other labor should paid at least the minimum wage the country of the data collector.
15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
20

---

Depending on the country in which research is conducted, IRB approval (or equiva- lent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do include any information that would break anonymity (if applicable), such as the institution conducting the review.
21

---

Appendix
A Overview
In this appendix, we offer further details on implementation, present additional experimental results, and provide more comprehensive analyses, structured follows:
Implementation details (Sec. BD:
Multi-modal data curation (Sec. Q
More quantitative evaluations (Sec. DD)
More multi nubti- comparison results (Sec. EE)
More visualization results (Sec. (
Limitations and societal impacts (Sec. G
B Implementation details
Progressive training process. To enhance the efficiency of the training process, we divide into two distinct stages. The first stage focuses on modeling the identity preservation capability, while the second stage targets the modeling of interaction generation. During the initial stage, the model is trained on single-subject data, concentrating solely on learning the target identity information without the complexity of interactions among multiple subjects. This stage involves 5,000 iterations. Once the model has effectively learned identity preservation, we proceed to the second stage, where the model is trained with multiple subjects as inputs. Here, the objective is to learn the interactions between the given subject images while maintaining their identities. This stage also comprises 5,000 iterations, Additionally, due to the extensive number of parameters in the pretrained Hunyuan Video model [2 each training iteration is time-consuming. To address this, in each stage, we initially train the model at reduced sizes for 1,000 iterations (included in the total 5,000 iterations), allowing the model to efficiently grasp the target objectives in less time. Subsequently, for the remaining iterations, we revert to the standard resolution to ensure the quality the final output. All training processes conducted on 256 GPUs, each with more than 80GB of memory, using batch size of 256.
Evaluation Metrics. To comprehensively assess the performance of video customization, we adopt several metrics focusing on identity preservation, text-video alignment, and overall video quality:
Identity Similarity. We utilize Arcface [ to extract facial embeddings from both the reference image and each frame the generated video, and then compute the average cosine similarity to evaluate how well the identity is preserved.
Subject Similarity. Each frame is segmented using YOLOv1 [26]] to isolate the subject, after which DINO-v2 [35]] features are extracted. The similarity between these features and those from the reference is calculated to assess subject consistency.
Aligmment. We employ CLIP-B and CLIP-L [39] to measure the correspon- dence between the provided text prompt and the generated video, evaluating how accurately the video reflects the textual description.
Fréchet Video Distance (FVD). To assess the quality and diversity of the generated videos, we compute the FVD between generated and real videos. Video features are extracted using 13D [R], and the Fréchet Distance is then calculated.
Temporal Consistency. Following the approach in VBench [22]], we use the CLIP model to compute the similarity between each frame and its adjacent frames, as well as between each frame and the first frame, evaluate the temporal coherence of the video.
Test Dataset. We manually collected 100 images of various objects, covering a wide range of categories such as man-made machines, food, goods, and buildings. In addition, we generated 100 human images using an image generation model. These images were then randomly paired to form 100 image pairs. For each pair, we utilized QWen2.5-VI. [D] to generate corresponding interaction text prompts.
22

---

& PPP
Figure A1: Examples of the test set, which contains images from diverse categories, such as human, animal, man mun- machine, food, goods, and building.
C Multi-subject data curation
In the main paper, have illustrated the MLLM- Subject Segmentation stage and the Clique- based Subject Consolidation. this section, we give more details for the preprocessing process, including the data source, data filtering and video captioning.
curate a large set high-quality data from open-source datasets, including Panda-70M [6] and Koala-36M [B7]] well as collected data. Initially, we split the videos by dividing long
23

---

Proceet adiie tavit hop Awilding an offines
Pronpe Nazer stowrisingg Chavel Up plasa.
Referance
Vidu
Koling
Skyreels
Ours
Figure A2: Comparison on human-object customization.
Promet and walks Assd Bendon the road
Pronpt
A gitst dyhitng with giiatts
Piko
Vidu
Kaling
Skyreels
VACE
Ours
Figure A3: Comparison human-human and animal-animal customizations.
videas into shorter clips. We then perform black border detection, subtitle detection, watermark detection, transition detection, and motion detection on these clips. Videos with black borders are cropped, and those with subtitles, watermarks, transitions, or low motion are removed. Further, we utilize Koala-36M [47]] to filter out videos with scores below a certain threshold. We then perform structured video captioning the remaining videos, generating long captions, short captions, and descriptions of background, style, and camera movement for each video. This structured combina- tion is used during training to enhance caption diversity.

---

D More quantitative evaluations
For more thorough and fine-grained evaluation of video generation quality, conduct panded analysis using the VBench benchmark [22]]. Our primary evaluation focused on Temporal Consistency, which encompasses subject and background consistency. To broaden this assessment, we now also include results for Motion Consistency and overall Image Quality, offering deeper insights into our model's capabilities.
Table A summarizes the performance of our method against several leading models. The results show that our model is highly competitive in maintaining temporal and motion consistency, achiev- ing scores comparable to the best-perforning methods. Notably, our model achieves a state-of-the- art score of 0.751 in Image Quality, demonstrating its superior ability generate visually pleasing and high-fidelity video content. This highlights a key strength of our approach in balancing motion dynamics with perceptual quality.
Table A1: Comprehensive VBench evaluation. Our method demonstrates competitive consistency metrics and achieves the highest score in Image Quality, indicating superior visual fidelity. Best results are in bold, second best are underlined.
Method
Temporal Consistency 1
Motion Consistency
Image Quality 1
Pika [+V]]
0,942
0.995
0.729
SkyReels-A2
0.943
0.994
0.731
VACE-1.3B 22
0,966
0.994
0.732
Kling-1.6 [2
0.934
0.994
0.724
Vidu-2.0 [5
0.970
0.997
0.695
Ours
0.964
0.995
0.751
E
More multi-subject comparison results
Human-object customization. The ability to generate videos depicting human-object interactions crucial, with broad applications fields such as film production and advertising. We present qual- itative results of numan-objec interaction in Fig. AV where our method is compared against several state-of-the-at approaches, including Pika [37]], Kling [25]. Vidu2.0 [AS]]. Skyreels A2 [[]]] and VACE 1.3B [244]. As shown, Pika, Vidu, and Kling often focus primarily on the object, resulting in the human face disappearing from the generated frames. Skyreels A2, on the other hand, struggles with producing smooth transitions between frames, leading to lower overall video quality. VACE sometimes fails to capture the intended interaction between the human and the object (left example), and occasionally does not preserve the appearance of the specified object (right example). In con- trast, our model consistently maintains strong subject consistency for both the human and the object, while also generating natural and coherent interaction motions between them.
Human-human & animal-animal customization. We further provide comparative results for numan-humma and animal-animal customization tasks in Fig. A3 It can be observed that Pika still suffers from incorrect attention, focusing on hands rather than preserving human identities. and introduces artifacts such as generating three giraffes in the right example. Kling copies lighting from the background of the man image, which contradicts the prompt 'road', and fails capture the full body of the giraffe, focusing only the head without depicting the fighting action specified in the prompt, indicating limited prompt adherence. Skyreels A2 fails to represent both subjects and exhibits poor identity preservation. VACE alters the generated human identities and does not follow the fighting prompt the giraffe example. While Vidu demonstrates relatively better per- formance, its identity preservation remains suboptimal. In comparison, our model achieves the best identity consistency and prompt adherence, demonstrating superior capability in customized video generation.
F More visualization results.
In this section, we present additional visualization results of our model, covering a wide range of subject customization scenarios, including human-object interaction, human-scene interaction,
25

---

looking at plate ftoh, proparing eat
Introducing a sultcase
A woman holds a bag of Lay's chips
A woman Is holding bag and anthusiastically Introducing it.
caresses dog grass.
looking at small, flutly dog.
man standing next to a traditional Japanese lantern.
man in tuxedo stands beside Tokyo Tower
young stands or the Liberty:
penguin standing on Jawn, with a giraffe behind it
A tiger is hunting an antelope
Reference Images
Generated Videos
Figure A4: More results on multi-subject customization.
human-animal interaction, and animal-animal interaction. We also provide more examples of three-
subject customization.
The two-subject customization results are shown in Fig. A4 It can be observed that our model is ca- pable generating natural and realistic interactions between various types inputs, demonstrating its potential effectiveness in applications such as advertising and movie production. Furthermore, beyond object interactions, our model can also generate specified subjects within assigned scenes, which is particularly useful for personalized content creation and other creative industries.
26

---

A person is feeding panda carrots, with elephant nearby.
A person is sitting on a sofa, petting cat.
A person is walking dog under the Elffel Tower
person dragging suitcase and chasing after an airplane.
Figure A5: More results shree-subject customization.
Next, we showcase more results of three-subject customization in Fig. AS. featuring diverse com- binations such as human-animallanine object-object. These results illustrate that our model can effectively handle different combinations of inputs and generate complex interactions among multiple subjects, all while maintaining strong identity preservation. This demonstrates the superior capability of our model in customized video generation for multi-subject scenarios.
human-object-animall numan-animal-seee
and human-
G Limitations and societal impacts
Limitations. Despite the significant advancements introduced by Voly in multi-subject video customization, several limitations remain. First, the quality and controllability of the generated videos are still constrained by the capabilities of the underlying video generation backbone and the pre-trained MILM and VAE models. Second, while our framework demonstrates strong perfor- mance a variety of subject types and interactions, it may encounter difficulties when handling highly complex scenes involving numerous subjects, intricate backgrounds, or fine-grati ine-grained inter- actions that require detailed physical reasoning. Finally, although pipeline improves subject discriminability, may still be susceptible to errors grounding or seg- mentation, especially in cases of occlusion ambiguous visual cues, potentially affecting the accu- racy of subject alignment and interaction modeling.
MLLM-based data curation
Societal Impacts. Poly enables more flexible and controllable video generation, which can benefit a wide range of applications, including creative content production, personalized education, digital marketing, and virtual reality experiences. By allowing users to customize videos with spe- cific subjects and interactions, our framework empowers artists, educators, and businesses to effi- ciently create tailored visual content.
27


Reviews:
[{"review_id": "vXl0ysvWcC", "reviewer": "Reviewer_5tse", "summary": "The paper presents a method for multi-reference condition to a text-to-video model. The method encodes both the video textual caption and the reference images into llava embedding space. The authors propose to modify the rope module in order to improve the interaction between the llava embeddings and the VAE embeddings of the image references. The authors propose an additional modification to the attention modules aiming to improve the contextualization of the image embedding with the video embedding sequence.", "questions": "* The Attention-inherited Identity Injection is not entirely clear. While Figure 3 shows the proposed modification, it can be helpful to also specify more clearly in the text what is the difference compared to the baseline architecture. Specifically, add to Equations 7-8 the entire flow of the attention as shown in the Figure.", "limitations": "The authors don't specifically addressed potential negative societal impact of their work. However, I think the limitations of their method are not fundamentally different from limitations of similar papers accepted to neurips and similar conferences.", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n* Relevant works are discusses.\n* The results seem appealing. \n\nWeaknesses:\n* Overall, the pipeline seems overcomplicated. I think that most of the new design choices that the paper introduces are unnecessary for properly integrating multi reference condition into the video model.\n* The VLLM-based Text-image Fusion (Sec 4.1) seem redundant to me, and also very specific to Hunyuan which uses llava as opposed to T5, this also reduced efficiency. To my understanding, all the experiments in the ablation study feed in the images into llava, so this component is not motivated + may contaminate the other experiments in the ablation study. I am missing the simple baseline of text-only llava embeddings (the caption may refer to the reference images!) and concatenation of the image VAE embedding to the sequence, without any other modifications to the overall architecture.\n* It's unclear to me why a special rope is needed, what is the problem with simply treating the reference images as additional frames and use the existing rope module of the video model? \n* The authors claim that simply concatenating the image vae embedding to the vae embedding sequence has a limitation as distant frames will be affected less by the condition image. However, I am not convinced that this speculation is valid. It is also worth mentioning that the authors use Hunyuan which uses DiT with full sequence attention, so the reference image directly interacts with each frame in the video. Indeed, the ablation study shows that the identity preservation increases with simple token concatenation.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewers for their valuable comments and suggestions, which have greatly helped us improve our work. Below, we provide detailed responses to each point.\n\n___\n\n### **Q1: The necessity of each proposed module**\n\nThank you very much for your valuable feedback and for sharing your perspective on the pipeline design. Our intention in introducing these design choices was to ensure a more comprehensive integration of multi-subject conditions into the video model and to address specific challenges observed in our preliminary experiments. We would also like to note that, despite these additional modules, our overall architecture remains straightforward and does not introduce significant computational overhead, as evidenced by the nearly identical inference time (4x H20 3.6 min) compared to the simple concatenation baseline (4x H20 3.5 min). We explain the motivation and necessity of each module in detail below.\n\n-  **The VLLM-based Text-image Fusion** is introduced to help the model understand the correlation between the text prompt and subject images. The model without this fails to distinguish which subject image corresponds to which text word. This is extremely serious when the subject images are similar items (e.g., two human, two cats). We have conducted additional experiments to validate this in the response for Q2.\n\n- **Text-image interaction 3D-ROPE:** The standard ROPE suffers from long-term decay, resulting in insufficient identity information for frames farther from the reference image. Our text-image interaction 3D-ROPE, in combination with the identity injection and text-video interaction modules (as shown in Fig. 3(c)), addresses this issue by enhancing the propagation of identity information and improving prompt following throughout the video sequence (More details are in the response for Q3).\n\n- **The Identity Injection and Text-Video Interaction Modules** are essential for enhancing the model’s ability to capture identity information and to follow the text prompt accurately. Compared to simple token concatenation, our approach effectively mitigates the long-term decay problem, ensuring that identity and prompt information are preserved throughout the video sequence. This leads to significantly improved multi-subject generation capability and better consistency between the generated content and the input text and image conditions.\n\n---\n\n### **Q2: About the VLLM-based Text-image Fusion**\n\nThanks for your suggestion. The text-image Fusion Module is necessary for multi-subject customization. Since the model needs to distinguish between different input subjects, i.e., which image correspond to which words. If there is not the Text-image Fusion module, the model is impossible to distinguish similar objects. (e.g., there the two subjects are 'a dog'). Therefore, we do not include the mentioned model with text-only llava and image concatenation in the ablation study due to its inherent limitation. Following your suggestion, we additionally conducted an ablation study of this model, the results are shown below. \n\n| Model           | Face-sim ↑ | DINO-sim ↑ | CLIP-B ↑ | CLIP-L ↑ | FVD ↓    | Temporal ↑ |\n|----------------|:----------:|:----------:|:--------:|:--------:|:--------:|:----------:|\n| **Ours** | **0.642**  | **0.623**  | **0.336**| **0.281**| **959.74**| **0.964**  |\n| Ablated model      | 0.543      | 0.532      | 0.330    | 0.267    | 1132.12  | 0.959      |\n\nIt can be seen that the model without text-image Fusion results in a poor identity consistency due to the limitation in distinguishing different identities, validating the necessity of it.\n\n___\n\n### **Q3: About the text-image interaction 3D-RoPE**\n\nThank you very much for your insightful question. The standard ROPE positional encoding is known to suffer from long-term decay [1], which means that frames farther from the reference image receive less conditional information. Simply treating the reference image as an additional frame and using the existing ROPE can therefore result in insufficient identity information for distant frames, leading to identity degradation.\n\nOur text-image interaction 3D-ROPE addresses this issue by enhancing both the identity information from the text prompt and the interaction between the reference image and video frames. This enables our model to achieve better identity preservation and prompt following, with the combination of the identity injection and text-video interaction module in Fig.3 (c).\n\n___\n### **Q4: About the long-term decay of the reference image in simple concatenation and the ablation study**\n\nThank you very much for the comments and for raising these important points. \n\n**Regarding distant frames,** We appreciate your insights regarding the use of simple token concatenation in DiT-based models such as Hunyuan, which employs ROPE as the positional embedding. While it is true that full sequence attention allows the reference image to interact with each frame, prior studies have shown that ROPE exhibits long-term decay [1], meaning that frames further away from the condition image tend to have weaker correlations with it. This can lead to identity degradation in the latter frames, as they do not receive sufficiently accurate identity information through simple token concatenation alone. \n\nTo further validate this problem, we conducted experiments using direct token concatenation and standard 3D-ROPE. Specifically, we measured the similarity between each segment of frames (from $t\\times10$ to $(t+1)\\times10$) and the reference image as time t increases, and compared these results with those obtained using our proposed method in the following table: \n\n| Time t         | 0      | 1      | 2      | 3      | 4      | 5      | 6      | 7      | 8      | 9      | mean   | std    |\n|----------------|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|\n| Simple Concat  | 0.649  | **0.644** | **0.645** | **0.642** | 0.636  | 0.621  | 0.626  | 0.607  | 0.624  | 0.609  | 0.630  | 0.015 |\n| Ours           | **0.651** | **0.644** | 0.638  | 0.640  | **0.641** | **0.645** | **0.643** | **0.647** | **0.639** | **0.646** | **0.643** | **0.004** |\n\n\n**Regarding the ablation study,** we agree that simple concatenation achieves relatively good identity preservation. However, our results demonstrate that it still falls short of the performance achieved by our proposed identity injection mechanism. Furthermore, in the context of video customization, prompt following ability is also crucial. Our experiments show that the simple token concatenation approach yields a lower CLIP score compared to our method, indicating inferior prompt alignment.\n\nIn summary, while simple token concatenation provides a reasonable baseline, our identity injection mechanism offers more robust identity preservation and better prompt following, addressing the limitations of existing approaches. We are grateful for your feedback, which has allowed us to clarify and further emphasize the advantages of our proposed design.\n___\n\n### **Q5: About the Attention-inherited Identity Injection**\nThank you for your helpful suggestion. We will revise the manuscript to clarify the differences between our Attention-inherited Identity Injection and the baseline architecture. Specifically, we will update Equations 7–8 to explicitly show the entire flow of the attention as follows:\n\n$$\n\\hat{z}_I, \\hat{z}_T = \\hat{FFN}(MM\\text{-}Attn_1(z_I, z_T))\n$$\n\n$$\nz' = Cross\\text{-}Attn(\\hat{W}_q^V(z), \\hat{W}_k^V(\\hat{z}_I), \\hat{W}_v^V(\\hat{z}_I))\n$$\n\n$$\n\\hat{z} = z + FC(\\hat{FFN}(z'))\n$$\n\n$$\nz_{out}, z_{T, out} =MM\\text{-}Attn_2(\\hat{z}, \\hat{z}_T)\n$$\n\nwhere $MM\\text{-}Attn_1$ is the MM Attention for text-image interaction and $MM\\text{-}Attn_2$ is the MM Attention for text-video interaction. We hope this will make our method clearer. Thank you again for your valuable feedback.\n\n___\n### **Q6: About the potential negative societal impact**\nThank you for your comment and for raising this important point. We acknowledge that we did not specifically discuss the potential negative societal impacts of our work in the current version of the paper. We agree that, similar to other works in this area, our method may share common limitations, such as potential misuse for generating misleading or inappropriate content. We will add a discussion on these societal impacts and possible mitigation strategies in the revised version, in line with the standards of NeurIPS and similar conferences. Thank you again for your valuable feedback.\n\n[1] Su J, Ahmed M, Lu Y, et al. Roformer: Enhanced transformer with rotary position embedding[J]. Neurocomputing, 2024."}, {"review_id": "QUNN3lj05H", "reviewer": "Reviewer_Mbak", "summary": "This paper presents a method for multi-subject personalized video generation, built on top of HunyuanVideo (a video diffusion model). To enhance identity preservation and interaction control, the authors introduce three key components:\n\n- A new multi-subject customization dataset, preprocessed using MLLM, SAM, CLIP, and a clique-based subject grouping strategy to support diverse and coherent interactions.\n\n- A 3D-RoPE-based identity interaction module, which encourages information exchange between embeddings from the VAE (capturing detailed identity) and a VLLM (LLaVA) responsible for interaction modeling.\n\n- An attention-inherited identity injection mechanism for more effective subject-specific conditioning throughout the video sequence.", "questions": "1. Can the model reliably disambiguate identities when subjects belong to the same category?\n\nFor example, in a prompt like ``a man on the left, and a man on the right``, does the model consistently assign the correct identity across frames? I reviewed Figure 3 of the appendix (the two-giraffe example), but it does not provide sufficient evidence to assess whether the model can reliably control identity in multi-subject scenarios where all subjects belong to the same category.\n\n2. What are the limitations of the model with respect to subject scale(size) and visibility?\n\n Can it simultaneously handle very small objects (e.g., a ring) alongside full-body subjects? Additionally, how does the model behave when a subject should be occluded or temporarily disappears from prompt? It's because I suspect the model tend to maintain the subjects visible in frames across video.\n\n3. Token Dimensions \n\nIn Equations (5) and (6), both the <image> token embedding $z_{T,I}$ and the identity token $z_I$ are said to have spatial dimensions of $w \\times h$. However, if my understanding is correct, $z_{T,I}$ comes from LLaVA and $z_I$ from the Hunyuan VAE, which may produce different token grid sizes. Are these embeddings resized or projected to enforce matching spatial shapes?", "limitations": "The model may struggle to handle more than three reference images effectively.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n- The paper is well written and easy to follow.\n\n- The generated videos show better performance in terms of visual quality, identity preservation, and prompt alignment, compared to baselines (including commercial models) —quantitatively and qualitatively.\n\n- The use of 3D RoPE embeddings for identity interaction enhancement is novel and effective, contributing significantly to performance.\n\n\nWeaknesses:\n- Since the model accepts a clique length longer than one-third of the total sequence, it may struggle to handle more than three subjects.\n\n- The model may fail to disambiguate identities when subjects are described using similar word, potentially leading to identity permutation.\n\nFor example, ``a man is standing next to a chair, and an another man is sitting on the chair.`` It will be difficult to assign each identity, as it use same natural language as description word.\n\n- Although the qualitative results are strong, the paper lacks a detailed evaluation of video quality. Metrics such as those from VBench (e.g., motion consistency) or human preference studies are not reported, so, it's difficult to determine whether fine-tuning introduces any trade-offs in visual quality.", "was_revised": true, "final_justification": "I've read authors rebuttal, and it effectively resolved my concerns on ambiguity in the method part and clarified the scope that model can handle (limitation) clearly. Given its practical applicability, I'm inclined to recommend accept.", "rebuttal": "We sincerely thank you for your valuable comments and suggestions, which have greatly helped us improve our work. Below, we provide detailed responses to each of your points.\n\n___\n### **Q1: About how to handle more than three subjects**\n\nThank you very much for your careful reading and insightful comment. We apologize for any confusion caused by our description. We would like to clarify that in our clique-based subject consolidation algorithm, **the \"total sequence\" refers to the total number of frames in the video (i.e., video length), rather than the total number of subject images**. Specifically, the threshold for the maximum clique is set to one-third of the total number of frames, not the number of detected subject images. This design ensures that each identified subject appears in a sufficiently large portion of the video, thereby filtering out subjects that only appear briefly and improving the robustness of multi-subject detection and segmentation. Therefore, our method can handle more than four subjects in a video.\n\nWe hope this clarifies the intention and effect of our algorithm. Thank you again for your valuable feedback. Additionally, we will include additional cases with four subjects in our revision to further demonstrate the scalability and effectiveness of our method in handling more than three subjects.\n\n___\n### **Q2: About subjects described using similar word**\n\nThe presence of similar subjects indeed poses a significant challenge for existing multi-subject video customization methods. To address this issue, we have designed three modules, spanning from data to model level, to enhance the discrimination of similar subjects. \n\n**(1) At the data level,** we employ a multi-modal large language model (MLLM) to distinguish similar objects mentioned in the text. Furthermore, we utilize a clustering algorithm to prevent confusion among different objects, which is not achievable in the data pipelines of previous approaches. \n\n**(2) At the model level,** we first leverage LLaVA to facilitate the model’s ability to differentiate similar subjects. Specifically, we introduce identity prompts to distinguish between similar subjects; for example, LLaVA receives prompts such as “a man looks like \\<image1\\>, the other man looks like \\<image2\\>,” enabling the model to explicitly differentiate between different subjects. \n\n**(3) In addition,** our 3D interactive ROPE mechanism places the subject images near their corresponding textual tokens, thereby strengthening the identity of each subject and further improving subject discrimination. \n\nFollowing your suggestions, we have conducted experiments to validate our model’s ability to handle similar subjects, where we achieve a Face-Sim of 0.632 and DINO-sim of 0.618. We will include relevant illustrative examples in the revision.\n\n___\n### **Q3: More metrics in Vbench**\n\nThank you very much for your thoughtful suggestion. In our paper, the **temporal consistency** metric we reported corresponds to the \"subject consistency\" and \"background consistency\" metrics in VBench. Following your advice, we have further evaluated our method using additional VBench metrics, specifically **motion consistency** and **image quality**.  In addition, we conducted a **human preference study** with 32 volunteers, who ranked the generated videos based on subject consistency, text alignment, and overall video quality. The average rankings (lower is better), along with the Vbench metrics, are reported in the following table.\n\n| Method   | Temporal consistency ↑ | Motion consistency ↑ | Image quality ↑ | Human preference ↓ |\n|----------|-----------------------|----------------------|-----------------|--------------------|\n| Pika     | 0.942                 | 0.995                | 0.729           | 3.00               |\n| SkyReels | 0.943                 | 0.994                | 0.731           | 5.41               |\n| VACE     | *0.966*               | 0.994                | *0.732*         | 4.28               |\n| Kling    | 0.934                 | 0.994                | 0.724           | 3.12               |\n| Vidu     | **0.970**             | **0.997**            | 0.695           | 3.47               |\n| Ours     | 0.964                 | *0.995*              | **0.751**       | **1.72**           |\n\nAs shown in the table, our method achieves highly competitive temporal and motion consistency, on par with the best-performing baselines. More importantly, our approach achieves the highest image quality score among all methods, suggesting that our model produces the best perceptual quality of the generated videos. Furthermore, our method receives the most favorable human preference ranking, indicating that users consistently prefer our results over other baselines in terms of subject consistency, text alignment, and overall video quality. We will include these additional quantitative results and analyses in the revised version of our paper to provide a more comprehensive evaluation. Thank you again for your valuable feedback.\n\n___\n### **Q4: Experiment on subject scale (size) and visibility**\n\n**Our method is capable of handling small objects as well as occluded or intermittently appearing objects**. This is because, in our data processing pipeline, we do not strictly constrain the size of the extracted subject images, and we retain objects that appear for more than one third of the total video duration. Therefore, our model is theoretically able to process small and occluded/disappearing objects. In practice, the capability in generating occluded/disappearing objects is demonstrated in Supplementary Figure 5 (example 8), where a person blocks the Tokyo Tower, and in Supplementary Figure 6 (example 3), where the dog only appears in the latter half of the video. Additionally, we have validated that our method can handle cases where an object first appears and then disappears, as well as extremely small objects such as rings. We will include more such examples in the revision. Thank you for your suggestion.\n\n___\n### **Q5: About Token Dimensions**\n\nThank you very much for your insightful comments. Your understanding is indeed correct. We have resized the images input to the VAE to ensure alignment with the grid size used by LLaVA. We will clarify this point in the revised manuscript. We sincerely appreciate your valuable suggestion."}, {"review_id": "qMdruJSS6m", "reviewer": "Reviewer_sm4D", "summary": "The paper proposes PolyVivid, a novel inference-time multi-subject video customization framework composed of several components. It introduces a design that leverages VLLMs for semantic understanding of both reference images and texts, along with a novel 3D-RoPE-based identity interaction enhancement module, which effectively preserves the sequential relationship between text and images through bi-directional fusion. Additionally, the paper presents an identity injection attention module featuring a new adaptation method for handling image tokens, and a data construction pipeline that consider subjects appearing only briefly in the video. This pipeline demonstrates superior performance even compared to recent closed-source models, such as Pika and Kling.", "questions": "**Q1**. While the qualitative samples suggest that identity preservation is well handled, some samples give a slight copy-and-paste impression, e.g., Figure 5 in the supplementary material. Was there any attempt to filter out overly similar segmentations in the dataset or to refine the data construction process for selecting frames?\n\n**Q2**.. How does the model perform on more outlier-like concepts (e.g., the monster toy in DreamBooth) or with highly similar subjects, such as two male individuals? Additionally, I’m curious whether the model can handle more complex compositions—beyond side-by-side placements—such as a scene featuring a can with the monster toy illustrated on it. (This isn’t meant as a negative comment, I understand that video models still have limitations in editability.)", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**S1**. The paper is easy to read and follow, and the presentation is clear. The supplementary material is also well organized.\n\n**S2**. Clique-based subject consolidation is interesting. This simple design effectively improves the robustness of multi-subject personalization datasets.\n\n**S3**. 3D-RoPE-based identity-interaction enhancement module is a novel and straightforward design for text-image fusion within the attention module. This structure presents a promising direction for future research in personalization, or in broader in-context image or video generation.\n\n**S4**. The method also demonstrated superior performance compared to closed-source models such as Kling and Pika.\n\n**W1**. (minor) I think the model name Keling should be Kling. (Kling appears to be the official name)\n\n**W2**. Image and text fusion via VLLMs is not a novel idea. I think citing [1] and [2] would be helpful.\n\n[1] Song et al., MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation, ECCV 2024\n\n[2] Li et al., BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing, NeurIPS 2023", "was_revised": true, "final_justification": "I have read the authors' responses, and my concerns about this work have been fully addressed. Considering its practical applicability, I intend to recommend acceptance.", "rebuttal": "We sincerely thank you for your valuable comments and suggestions, which have greatly helped us improve our work. Below, we provide detailed responses to each of your points.\n___\n### **Q1: Correction of Model Name (\"Keling\" → \"Kling\")**\n\nThank you very much for your valuable suggestion. We will make the corresponding revisions in the revised manuscript.\n\n___\n### **Q2: Novelty of Image and Text Fusion via VLLMs; Citation of Related Works**\n\nThank you very much for your valuable suggestion. Our main contribution lies in designing a structured template for video customization models, which helps the video model better understand the relationship between the subject image and the text. We will cite these works in the revised manuscript to make the related work section more rigorous. Thank you again for your helpful comments.\n___\n### **Q3: Copy-and-Paste Impression in Qualitative Samples; Data Processing for Identity Preservation**\n\nThank you very much for your valuable comment. In our data construction process, we used images of the same subject from other video clips as conditions, rather than always using the main subject image from the same video. This approach helps to alleviate the hard-copy issue. However, when the given subject image matches the environment specified by the prompt, the model may still copy most of the features from the image into the generated video, as this not only preserves the subject's characteristics but also closely follows the text prompt.\n\n___\n### **Q4: Handling Outlier Concepts, Highly Similar Subjects, and Complex Compositions**\n\nThank you very much for your thoughtful comments and for raising these important questions.\n\nBenefiting from the strong generative capabilities of our base model, HunyuanVideo, as well as the robust generalization ability of our proposed method, we have experimentally verified that our approach can successfully handle outlier-like concepts such as the \"monster toy\" in DreamBooth, as well as highly similar subjects, such as two male individuals. Furthermore, our method is also capable of generating more complex compositions beyond simple side-by-side arrangements. For example, we are able to generate scenes like \"a can with the monster toy illustrated on it.\" This compositional ability can be partially observed in the clothing transfer case shown in Fig. 4, where the model demonstrates the capacity to combine subject attributes in a more integrated manner.\n\nOf course, we acknowledge that for some highly counter-intuitive or extremely challenging cases, there may still be limitations due to the current capabilities of video generation models. We greatly appreciate your suggestions, and in the revised version of our paper, we will include additional representative cases to better illustrate the strengths and current limitations of our approach. Thank you again for your valuable feedback."}, {"review_id": "0ZI4kqMBaX", "reviewer": "Reviewer_mzgG", "summary": "Text-to-video generation has attracted great attention recently. Despite the significant developments, existing text-to-video generation methods still lack fine-grained controllability, especially for multi-subject customization with consistent identity and interaction. To solve the problem, the paper proposes PolyVivid, a multi-subject video customization framework enabling controllable multi-subject video generation. Specifically, PolyVivid utilizes a VLLM-based text-image fusion module to encode subject images and uses a 3D-RoPE-based identity-interaction enhancement module to further enhance identity preservation and enable richer subject interaction. Besides, to efficiently inject text embeddings and image embeddings, PolyVivid proposes an attention-inherited identity injection module. Experiments have shown the effectiveness of the proposed methods.", "questions": "Please see weaknesses. Besides, here are some other questions and suggestions below:\n\n* A comprehensive comparative analysis with the backbone model (HunyuanVideo) is required, encompassing both qualitative and quantitative evaluations to assess the relative improvements of the proposed method. On the one hand, whether the approach demonstrates improvements in established evaluation metrics, and on the other hand, whether it enables the generation of previously failed cases that the backbone model could not produce.\n* In Figure 6 of the Appendix, the case ``A person is sitting on a sofa, petting a cat.\" The generated video seems to have a blurry mosaic at the woman's elbow. What could this be due to?\n* The paper mentioned that 'existing works lack fine-grained controllability, especially for multi-subject customization with consistent identity and interaction'. I would like to know if the method is able to generate some complex counterfactual relations. For example, \"A horse rides on an astronaut.\"", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths：\n\n* Video generation involving fine-grained interactions between multiple subjects is a critically important and interesting problem in the field.\n\n* The proposed 3D-RoPE mechanism is novel.\n  \n\nWeaknesses:\n\n* The organization of the manuscript has led to an inadequately substantiated experimental component, lacking the depth and rigor required to fully evaluate the proposed approach.\n* The sharing MM Attention mechanism illustrated in Figure 3 lacks a detailed explanation in the main text, which significantly confuses the reader, making it difficult to understand the methodological intent when reading Section 4.3", "was_revised": true, "final_justification": "I have read the authors’ rebuttal, which addressed most of my concerns and provided a more detailed explanation of the shared MM attention mechanism. Although the model still lacks the ability to generate certain counterfactual results due to limitations of the base architecture, the work remains solid at the current research stage. Therefore, I recommend acceptance.", "rebuttal": "Thank you very much for your thorough review and valuable feedback. Your comments have provided us with important guidance for improving our manuscript. Below, we address each of your concerns in detail.\n\n___\n### **Q1: About the organization of the manuscript**\n\nThank you very much for your valuable feedback. We appreciate your suggestion regarding the organization of the manuscript. In the revised version, we will further streamline some of the methodological details and move certain sections to the supplementary material. This will allow us to present a more focused and rigorous experimental section, thereby providing a more comprehensive evaluation of our proposed approach. Thank you again for your constructive comments.\n\n___\n### **Q2: Lack of detailed explanation for the sharing MM Attention mechanism**\n\nThank you very much for your insightful comment. We apologize for any confusion this may have caused.\n\nTo clarify, **the sharing MM Attention mechanism in our framework refers to the use of a single set of core parameters for both MM Attention modules and the MM Cross Attention module illustrated in Figure 3**. Specifically, these shared parameters are initialized from the pre-trained weights of Hunyuan-Video. For the Text-Video Interaction, the MM Attention directly utilizes this shared parameter set. In contrast, for the Text-Image MM Attention and the MM Cross Attention, we introduce an additional LoRA (Low-Rank Adaptation) layer for the shared parameters separately. This design enables each module to learn task-specific features through the additional LoRA layers, while still leveraging the knowledge contained in the shared pre-trained weights. By sharing the main set of parameters across modules, the model can converge more quickly during training and requires fewer overall parameters, thus improving both efficiency and performance.\n\nWe will update the main text in the revision to provide a more comprehensive and explicit explanation of this mechanism in Section 4.3. Thank you again for your valuable feedback, which will help us improve the clarity and quality of our manuscript.\n\n___\n### **Q3: Need for comprehensive comparison with the backbone model**\n\nThank you very much for your insightful suggestion. We would like to clarify that the original HunyuanVideo model [1] does not support subject-driven customization. However, in our ablation studies, the (*LLaVA + Token-Concatenation*) baseline actually corresponds to our reproduction of the official HunyuanCustom model [2] (official work of HunyuanVideo designed for video customization) for multi-subject customization.\nWe have conducted quantitative comparisons between our proposed method and this baseline. As shown in the table below, our approach demonstrates consistent improvements across established evaluation metrics:\n\n| Method         | Facesim ↑ | DINOsim ↑ | CLIP-B ↑ | CLIP-L ↑ | FVD ↓   | Temporal ↑ |\n|----------------|-----------|-----------|----------|----------|---------|------------|\n| Ours           | **0.642**     | **0.623**     | **0.336**    | **0.281**    | **959.74**  | **0.964**      |\n| HunyuanCustom  | 0.628     | 0.615     | 0.328    | 0.271    | 980.56  | 0.960      |\n\nAs shown in the table, our method achieves higher scores on Face similarity and DINO similarity, indicating improved identity and appearance consistency. The improvements in CLIP-B and CLIP-L further demonstrate better semantic alignment with the input prompts. Moreover, the better FVD and temporal score value suggest that our generated videos are more temporally coherent and visually realistic. In addition, we have conducted more qualitative comparison with HunyuanCustom [2], where we observe that our method is able to generate videos with more complex subject interactions and maintain fine-grained subject details (e.g., text on the subject), which are often challenging for the baseline model. These results demonstrate the effectiveness and robustness of our approach in both quantitative and practical aspects.\n\nWe appreciate your suggestion and will further highlight these comparative results in the revised manuscript to provide a clearer assessment of the relative improvements brought by our method. Thank you again for your constructive feedback.\n\n___\n### **Q4: Blurry artifact in generated video (Figure 6, Appendix)**\n\nThank you very much for your insightful comment. This likely refers to some scarf-like items, as identified by the model, which are draped over the woman's elbow. However, since only the main part of these items is occluded by the cat, they appear to resemble a blurry mosaic.\n\n___\n### **Q5: Ability to handle complex counterfactual relations**\n\nThank you very much for your valuable comment. Our model is built upon HunyuanVideo, and therefore, its ability to model complex counterfactual relations is inherently limited by the base model. We have tested both HunyuanVideo [1] and Wan [3] on this prompt, and neither is able to generate the correct video (both produce \"An astronaut rides on a horse\"). Our method also finds it challenging to generate such counterfactual cases (although it can generate the correct \"An astronaut rides on a horse\"). We believe that as the capabilities of base models continue to improve, applying our method to a more powerful base model holds great promise for addressing these cases.\n\n[1] Kong W, Tian Q, Zhang Z, et al. Hunyuanvideo: A systematic framework for large video generative models[J]. arXiv preprint arXiv:2412.03603, 2024.\n\n[2] Hu T, Yu Z, Zhou Z, et al. Hunyuancustom: A multimodal-driven architecture for customized video generation[J]. arXiv preprint arXiv:2505.04512, 2025.\n\n[3] Wan T, Wang A, Ai B, et al. Wan: Open and advanced large-scale video generative models[J]. arXiv preprint arXiv:2503.20314, 2025."}]

Author rebuttal:

Meta-review: The paper proposes a multi-subject video customization framework for generating identity-consistent videos with multiple subjects based on HunyuanVideo. Reviewers acknowledge several strengths including the novel 3D-RoPE mechanism for identity-interaction enhancement, strong experimental results outperforming commercial baselines, clear presentation, and a novel clique-based data construction approach for filtering inconsistent detections and ensuring subject consistency across video frames.

The weaknesses noted by reviewers include insufficient clarity on technical details, insufficient evaluation, questions about the model's ability to handle complex or ambiguous scenarios, and unclear necessity of certain components (particularly VLLM fusion). Authors provided additional experiments, clarifications, and additional VBench/human evaluation metrics. Three reviewers explicitly acknowledged that their concerns were addressed and recommended clear accept, while Reviewer 5tse did not engage in discussion.

The Area Chair agrees with the engaged reviewers and recommends acceptance. Authors are advised to revise based on reviewers' comments to make the final version more solid. Additionally, kindly discuss the generalizability of the VLLM-based fusion approach beyond the current implementation, addressing concerns that it appears very specific to HunyuanVideo's architecture.
Decision: Accept (poster)