BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration
Authors: Zhaoyang Li
Dongjun Qian
Kai Su
qishuai diao
Xiangyang Xia
Chang Liu
Wenfei Yang
Tianzhu Zhang
Zehuan Yuan
Venue: ICLR 2026 Poster (2026)
Keywords: Video generation
Diffusion models
Primary area: generative models

Abstract: Diffusion Transformer has shown remarkable abilities in generating high-fidelity videos, delivering visually coherent frames and rich details over extended durations.
However, existing video generation models still fall short in subject-consistent video generation due to an inherent difficulty in parsing prompts that specify complex spatial relationships, temporal logic, and interactions among multiple subjects. To address this issue, we propose BindWeave, a unified framework that handles a broad range of subject-to-video scenarios from single-subject cases to complex multi-subject scenes with heterogeneous entities. 
To bind complex prompt semantics to concrete visual subjects, we introduce an MLLM-DiT framework in which a pretrained multimodal large language model performs deep cross-modal reasoning to ground entities and disentangle roles, attributes, and interactions, yielding subject-aware hidden states that condition the diffusion transformer for high-fidelity subject-consistent video generation.
Experiments on the OpenS2V benchmark demonstrate that our method achieves superior performance across subject consistency, naturalness, and text relevance in generated videos, outperforming existing open-source and commercial models.

Published as a conference paper at ICLR 2026
BINDWEAVE: SUBIECT-CONSISTEN VIDEO GENER- ATION VIA CROSS-MODAL INTEGRATION
Zhaoyang Li!
Dongjun Qian²
Kai Su
Qishuai Diao² Xiangyang Xia?
Chang Liu Wenfei Yang' Tianzhu Zhang 3.3- Zehuan Yuan²
University Science and Technology of China 2ByteDance
National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory
En
Single-Face-ter foceetoddoo
Multi-face-to-vieee
toiti
Bingle-Body-tovviie
Bahi--Bdy-to-viice
Figure I: Examples of subject-to-videe generation results of our proposed WiadWeave, demonstrat- ing its ability to produce high-fidelity, subject-consistent videos across a broad spectrum of scenarios from single-subject inputs to complex multi-subject compositions.
ABSTRACT
Diffusion Transformer has shown remarkable abilities in generating high-fidelity videos, delivering visually coherent frames and rich details over extended du- rations. However, existing video generation models still fall short in subject- consistent video generation due to an inherent difficulty parsing prompts that specify complex spatial relationships, temporal logic, and interactions among multiple subjects. To address this issue, we propose BindWeave, a unified frame- work that handles broad range of subject-to-video scenarios from single-subject cases to complex multi-subject scenes with heterogeneous entities. To bind com- plex prompt semantics concrete visual subjects, we introduce an MLLM-DD DiT framework in which pretrained multimodal large language model performs attributes, deep cross-modal reasoning to ground entities and disentangle roles, and interactions, yielding subject-aware hidden states that condition the diffu- sion transformer for high-fidelity subject-consistent vidco generation. Experi- ments on the OpenS2V benchmark demonstrate that method achieves superior performance across subject consistency, naturalness, and text relevance gener- ated videos, outperforming existing open-source and commercial models. Project page: https:///zyy dot githabt.hb..... 10/ BindWeave/
"Corresponding author

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Published as a conference paper at ICLR 2026
1 INTRODUCTION
Recent advances in diffusion models (Ho et al.
2020: Peebles & Xic
2023:
Wang ct al. 2025
have enabled significant breakthroughs in video
generation (Wan et al.
2025 Yang et al. 2024
Hu et al., 2025 Kong et al.) Polyyk et al. 2024; Zheng et al. 2024; Wu et al.
2025). ) achieving
outstanding performance on various tasks ranging from text-to-video (T2V) (HaCohen et al. 2024
Wan et al. 2025 Chen et al., 2025a) to image-to-video (I2V) (Blattmann et al., 2023] Mao et al,,
2025j. Foundation models such as Wan (Wan et al., 2025) and Hunyuan Video (Kong et al.) now demonstrate the ability to produce high- fideliiillit, long-durations and content-rich videos, showcasing immense technological potential. However, despite these advances in visual quality, their practical utility remains constrained by limited controllability. Specifically, current models struggle to exert precise and stable control over key elements within a video, such as the identity of a specific person, the appearance of an object, a brand logo. This lack of controllability constitutes core limitation that significantly impedes deployment in customized applications, including personalized content creation, brand marketing, pre-visualization, and virtual try-on.
To address the above challenges, subject-to-video (S2V) (Liu et al. 2025) has garnered increasing attention. The core objective of S2V is to ensure that one or more subjects within video maintain high fidelity in their identity and appearance with respect to the given reference images throughout the entire dynamic sequence. This capability directly addresses the controllability shortcomings of existing general-purpose models, making it possible to generate customized videos based on user-provided subjects. To achieve subject-consistent video generation, some existing works (Yuan)
et al. 2024b Chen et al.) 2025b. Huang et al. 2025 Liu et al. 2025 Jiang et al. 2025) extend a video foundation model to accept multiple reference images as conditioning input. For instance, Phantom (Liu et al. 2025) introduces a dual- branch architecture separately process the text prompt and reference images, subsequently injecting the resulting features into the attention layers a Diffusion Transformer (DIT) (Peebles & Xie (023) as conditioning. VACE (Jiang al., (025) designs a video condition unit to unify inputs (text, image/video references, mask) into a unified format, then inject these context signals via residual blocks guide video generation.
Despite their promising results, these methods share a common limitation: they rely on a separate- then-fuse shallow information processing paradigm. Specifically, these models typically use sepa- rate encoders to extract features from images and text independently, followed by a post-hoc fusion through simple concatenation or pross-attention mechanisms. While this mechanism may suffice for simple instructions involving only appearance preservation, its ability to understand and rea- son falters when faced with text prompts involving complex interactions, spatial relationships, and temporal logic among multiple subjects. Due to lack of deep semantic association across the mul- timodal inputs, the model struggles to accurately parse the instructions, often leading to problems like identity confusion, action misplacement, attribute blending.
To overcome this bottleneck, we propose BeadWeave, a novel framework designed for subject- consistent video generation. To establish the deep mross-modal semantic associations, Wead leverages a Multimodal Large Language Model (MLLM) as an intelligent instruction parser to re- place the conventional shallow fusion mechanism. Specifically, leaved sequence from reference images and text prompt. This sequence is then processed by a pre-trained MLLM to parse complex spatio-temporal relationships and bind textual commands to their corresponding visual entities. Through this process, the MLLM generates a set of hidden states encoding both the precise identity of each subject and their prescribed interactions. These hidden states then serve as conditioning inputs to our generator, bridging high-level parsing with diffusion- semantic anchors and further reduce identity drift, we also incorporate CLIP (Radford et al.) 2021b) features from the reference images. Accordingly, our DIT (Peebles Xie (023) based generator jointly conditioned on these hidden states and CLIP features. Together, these conditioning inputs provide comprehensive relational and semantic guidance. To preserve fine-grained appearance details, we augment the vidco latents during diffu- sion with VAE (Esser al. 2021) features extracted from the reference images. This collective conditioning on high-level reasoning, semantic identity, and low-level detail ensures the generation of videos with exceptional fidelity and consistency.
first construct a unified, inter-
based generation. To provide subject-groundee
We conduct a comprehensive evaluation of BindWeave on the fine- -reeggained OpenS2V Yuaan et al. 2025) benchmark against diverse set of existing approaches, including leading open-source meth-
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ods and commercial models. The evaluation assesses key aspects such subject consistency, tem- poral naturalness, and text-video alignment. Extensive experiments demonstrate that BindWeave achieves state-of-the-arr performance, consistently outperforming all competing methods in subject- consistent video generation. Qualitative results, illustrated in Figure | further demonstrate the superior quality of the generated samples. These findings highlight BindWeave's effectiveness in subject-consisten video generation and its potential as a high-performing solution for both research and commercial applications.
RELATED WORK
2.1 VIDEO GENERATION MODEL
Diffusion models have enabled remarkable advancements in video generation. Early meth- ods (Singer et al.) 2022 Blattmann et al.] [023] Guo et al.] 2023) often extended text-to-image models (Rombach et al. 2022) for video generation by incorporating temporal modeling modules. More recently, the Diffusion Transformer (DiT) (Peebles & Xie (023) architecture, motivated by its excellent scaling properties, has inspired a new wave of models like Wan (Wan al., 2025). Hun- yuan Video (Kong et al. (2024)), and Goku (Chen et al. 2025a). However, these models focus on general-
general-purpose purpose video generation, and there still considerable room for improvement in achieving fine-grained control.
2.2 SUBJECT-CONSISEEN VIDEO GENERATION
To achieve more fine fine-grained control, subject-consistnnt video generation has garnered significant attention. Initial approaches often rely on per-subject optimization, where a pre-trained model is fine-tuned on images of specific subject, seen in methods like Custom (Wang al. 2024) and DisenStudio (Chen et al. 2024). While effective, this instance-specific tuning is computationally expensive and poses challenges for real-time applications. More recent works have shifted towards end-to-end methods that use conditioning networks or adapters to inject identity information during inference, allowing for generalization to new subjects without retraining. These models, such as IDAnimator (He et al. (024) and ConsislD (Yuan et al., 2024a), initially focused on preserving facial identity. This capabiliiy was later extended to arbitrary objects and multiple subjects by works like ConceptMaster (Huang et al., (2025), SkyReels-A2 (Fei et al. (0025), Phantom (Liu et al. (025). and VACE (Jiang et al.) 2025).. Despite this progress, significant challenges remain, particularly maintaining distinet identities and modeling complex interactions in multi-subject scenes.
METHOD
3.1 PRELIMINARIES
Diffusion Transformer Models for Text-to-Video Generation. Transformer-basee text-to-video diffusion models have shown substantial promise for video content generation. Our BindWeave builds upon a Transformer-basee latent diffusion architecture that employs a spatio-temporal Varia- tional Autoencoder (VAE) (Wan et al. 2025) to map videos from the pixel level to a compact latent space, where the generative process is performed. Each Transformer block comprises spatiotempo- ral self-attention, text cross-attention, a time-conditioning MLP, and a ferd-forwwrd network (FFN). The cross- coss-atttntionn is conditioned on text prompt embedding Gext obtained from T5 encoder Ers (Raffel et al. (020)). We employ Rectified Flow (Liu et al.) 2022 Esser et al. 2024) to define the diffusion dynamics, which enables stable training via ordinary differential equations (ODEs) while maintaining equivalence to maximum likelihood objectives. In the forward process training, ran- dom noise add to clean data 2o to generate 2; = (1-t) t) + te, where is sampled from a standard normal distribution, (O0, f), and the time coefficient t is sampled from [0, 1]. Accordingly, the
learning objective becomes the estimation of ground truth velocity field U2 dz, /tt c 2g. The
network ue is trained to this end using the Flow Matching loss (Esser et al. 2024
L Ee, ||uel (Zt, (rxu) Uz
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Video Generation with Image Conditioning. Natural language offers accessible interface for diffusion-baste basod video synthesis, yet it often under-specifies subject identity and spatial layout. This motivates the incorporation of reference image to anchor appearance and geometry in text-to-vidco pipelines Wan et al. (2025); ; Kong et al. (2024b). For instance, Wan (Wan et al., injjcts image information in two ways: first the imput level for fine-grained spatial detail, and second within the cross-attention mechanism for semantic guidance. First, to preserve fine-grained appearance, the reference image Zimg is encoded by VAE into spatial latent Zimg- This latent concatenated with the current noisy video latent along the channel dimension. The combined latent is then patchified and linearly embedded form the initial sequence tokens for the DiT block:
H in Faneed (xt Zimg))-
Then, semantic guidance is achieved by injecting multimodal conditioning via coss-attention. A pretrained vision encoder Evision (e.g.. CLIP) processes Zimg into semantic tokens Hamg- while a text encoder provides text tokens Hent. Within each cross-attention layer, queries (Q) are derived from the evolving video tokens, denoted as Haid (where Hvid is Hin for the first layer). The query, key, and value matrices computed using dedicated linear projection layers (ΦQ, & &x (v))
Quid (HuHH)
King 4x (Px(Ham))) Ving &v (Hagg)
and similarly for Kent, Vizt from the text stream. The output of the attention layer fuses these information sources using the standard scaled dot-product attention operator, Attn(-):
Hout
Held + Attn( QQuid Kests Vix)) Attn( Quid, K kmgg V Ving),
where y is scalar balancing the image and text guidance.
Image Taken
Token
VAE
Coneat
Time nep
Qwen2.5-VL.
Diffusion Transformer
VAE
trage Trender
Text Tolavorr
walking dog
park.
Noise Video Latents
Figure 2: Framework of our method. A multimodal large language model performs cross-modal reasoning to ground entities and disentangle roles, attributes, and interactions from the prompt and optional reference images. The resulting subject-aware signals condition Diffusion Transformer through cross-attention and lightweight adapters, guiding identity-faithful, relation-consistent, and temporally coherent video generation.
3.2 ARCHITECTURE
Our proposed BindWeave is designed to overcome the limitations of shallow fusion paradigms in subject-consistent video generation. The core principle of approach is to replace shallow, post- hoe fusion with a deep. reasoned understanding of multimodal inputs before the generation process begins. To this end, BindWeave first leverages Multimodal Large Language Model (MLLM) to act as an intelligent instruction parser. The MLLM thus generates a guiding schema, realized as sequence of hidden states that encodes complex cross-modal semantics and spatio-temporal logic, then meticulously guides a Diffusion Transformer (DiT) throughout the synthesis process, Figure] provides a schematic overview of the BindWeave architecture.
3.3 INTELLIGENT INSTRUCTION PLANNING VIA MLLM
To effectively foster joint cross cross-modal learning between the text prompt and reference images, introduce a unified multimodal parsing strategy. Given a text prompt and K user-specified sub- jects, each with reference image Tg. constructs multimodal sequence A' by appending one

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image placeholder for each reference image after the text prompt. The MLLM is then provided with this sequence along with the corresponding list of images Z:
x' TT, (img)1, (img)2..... -... (img) I 12,42 ....,,............ 1,]]
(5)
(6)
where (img) is special placeholder token that the MLLM internally aligns with the k-th image, Ig- This unified representation, which preserves the crucial contextual links between textual descriptions and their corresponding visual subjects, is then fed into a pre-trained MILMM. By processing the multimodal inputs (X' I), the MLLM generates sequence of hidden states, Hmllm that embodies high-level reasoning of the scene, effectively binding textual commands their specific visual identities:
Hnlim MLLM(XX I).
To align the feature space between the frozen MLLM and our diffusion model, these hidden states are projected through a trainable lightweight connector, Gpouj. to yield a feature-aligned condition Cuilm=
Cmllm = (mimt
(8)
While this MLLM-deriiee condition provides invaluable high-level, cross-modal reasoning, we rec- ognize that diffusion models are also highly optimized to interpret fine-grate fine-grained textual semantics. To provide this complementary signal, encode the original prompt independently using the T5 text encoder (ETs) (Raffel et al. (020)) to produce dedicated textual embedding Cuxti
Cixt = ETs(T)-
We then concatenate these two complementary streams to form final relational conditioning signal Gjoint:
Goint Concat(Gmllms Cwx))-
(10)
This composite signal thus encapsulates not only the explicit textual commands but also the deep reasoning about subject interactions and spatio-temporal logic, providing robust foundation for the subsequent generation phase.
0-padding
Noise video latents X
Channel dimension
mter
myef
Cow
Coow
O-padding
Temporal dimenssoo
Figure 3: Illustration of our adaptive multi-reference conditioning strategy.
3.4 COLLECTIVELY CONDITIONED VIDEO DIFFUSION
In the instruction planning process, we integrate useful semantics into Geint- Now, we need to inject Goint as condition into the DiT module guide video generation. Our generation backbone we operating in the latent space of traitrained spatio-temporal Variational Autoencoder (VAE). To ensure high-fidelity and consistent video generation, we employ collective conditioning mecha- nism that synergistically integrates multiple streams of information. As described in Section 3.1 our collective conditioning mechanism also operates at two synergistic levels: conditioning the spatio-temporal input and the cross-attention mechanism. To maintain fine-grained appearance details from the reference images, we design an adaptive multi-reference conditioning strategy as shown in Figure 3 Specifically, Cyae = Evar( TT Since S2V differs from 12V, the reference images are not treated as actual video frames. We first expand the temporal axis of the noisy video latent, padding K additional slots with
encode the references into low-level VAE features, denoted as

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zeros: x, (x,, K). We then place the VAE features of the reference images Cvae onto these K padded time positions (zeros elsewhere), and further concatenate the corresponding binary masks Mhe along the channel dimension emphasize the subject regions. The final input the DiT block is obtained via channel-wise concatenation before patch embedding:
and PatchEmbed
(concat,(*x, Patcchnbbe(connatt
Crae, m)rr))
(11)
where Eyee and Mee are zero outside the K padded temporal slots and carry the reference condition- ing only within those slots. This design preserves the temporal integrity of the original video while injecting fine- grained appearance and subject emphasis through channel-wise conditioning. Then, high-level semantic guidance is injected via the cross-attention layers. This involves two distinct signals: the relational condition Gjoint from the MLLM for scene composition, and the CLIP image features Celip = ECLI (Tar) ) for subject identity. Within each DiT block, the evolving video to- kens Hidd generate the queries Quid- The conditioning signals Gjoint and Celip are projected to form their respective key and value matrices. The final output of the attention layer is a sum of these information streams, extending Equation
Hout Huid + Attn Attn(Qued Kjoint: (ooin) + Attn QQiid. Kelpp Veup):
(12)
where (K joints V joint) and (Keiip V dtpp) are derived from Gjoint and Celip using linear projection lay- ers, respectively. By integrating high-level relational reasoning (Cjona), semantic identity guidance
(Celip), and low-level appearance details (Cens) this structured manner, Wind effectively steers the diffusion process to generate videos that are not only visually faithful to the subjects but also logically and semantically aligned with complex user instructions.
3.5 TRAINING AND INFERENCE
Training Setup. Following the rectified flow formulation described in Section 3.1 our model is trained to predict the ground truth velocity. The overall training objective for BindWeave can be formulated as mean squared error (MSE) between the model output and
mase [ t, Gjoints Colip, Cvac) Uy 112
(13)
Our training data is curated from the 5 million publicly available OpenS2V-5M dataset (Yuan et al, 2025). Through a series of filtering strategies, we distill a final, high-quality dataset of approximately million video-text pairs. The training process then follows a two-stage curriculum learning strategy on this data. All training processes are conducted on 512 xPUs, with a global batch size of 512, utilizing a constant learning rate of 5e-6 and the Adam W optimizer. The initial stabilization phase, lasting for approximately 1,000 iterations, utilizes smaller, core subset selected from the million data for its exceptional quality and representativeness. This initial phase is crucial for adapting the model to the specific demands of the Subject-to- Video (S2V) task, primarily focusing on learning to faithfully preserve a subject's visual identity while aligning it with the corresponding textual motion commands. This establishes robust foundation for the subsequent large-scale training- Subsequently, the training transitions to full-scale phase for an additional 5,000 iterations, where the model exposed to the entirety of the million curated dataset. This second stage allows the model to build upon its stable foundation and learn from a broader range of high-quality examples, significantly enhancing its generative capabilities and generalization performance.
Inference settings. During inference, our Bind accepts a flexible number of reference images (typically 1-4), while a text prompt steers the generation by describing the desired scene and behav- iors. Similar to Phantom (Liu et al. 2025), we use a prompt rephraser during inference to ensure the text accurately describes the provided reference images. Generation is performed over 50 steps using a rectified flow Liu et al. (2022) trajectory, guided by Classifier-Fres Guidance (CFG) Ho & Salimans 2022)
with a scale of w. The guided noise estimate at each step t is computed as:
Ea( c) = c0( 2) + w (co(Ze, e) - ca( 2))
(14)
where en (2, c) is the noise prediction conditioned on the prompt c, and co(2,, ()) is the uncondi- tional prediction. This estimate is then used by the scheduler to derive x4-1.

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4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Benchmark and Evaluation Metrics. To ensure fair comparison, adopt the OpenS2V-Eval benchmark (Yuan et al. 2025) and follow its official evaluation protocol, which provides fine- grained assessments of subject consistency and identity fidelity for subject-to-video generation. The benchmark comprises 180 prompts in seven distinct categories, covering scenarios from single- subject (face, body, entity) to multi-subject and human-entity interactions. To quantify performance, we report the protocol's 's automated metrics, with higher scores indicating better results across all metries. These include Aesthetics (christophschhhmami 2024) for visual appeal. MotionSmooth- (Bradski et al., (2000) for
ness (Bradski et al. (200)) for temporal smoothness, MotionAmplitur motion magnitude, and FaceSim (Yuan et al., (024a) for identity preservation. We also use three metrics introduced by OpenS2V-Eval (Yuan et al. (2225) that correlate highly with human percep- tion: NexusScore (subject consistency), NaturalScore (naturalness), and GmeScore (text-video relevance).
Implementation details. WindWeave fine-tuned from a foundation video generation model based on DiT architecture (Wan al. 2025). The T2V and 12V pre- pre-training stages excluded from this evaluation. For the core instruction planning module, we employ D0wen..5-VL-7. (Bai et al. 2023) as our Multimodal Large Language Model (MLLM). To align the multimodal control signal with the DiT conditioning space, we introduce a lightweight connector that projects the Qwen2.5- VL hidden states. Specifically, the connector features two-layer MLP with GELU activation. We train our model using the Adam optimizer with a 5e-6 learning rate and a global batch size of 512. To mitigate copy-paste artifacts, we apply data augmentations (e.g., random rotation, scaling) to reference images. During inference, we use 50 denoising steps set the CFG guidance scale w to 5.
Baselines. We compare BindWeave with the oftte-of-the-ant video customization methods, includ- ing open souured methods (Phantom (Liu et al.] 2025). VACE (Jiang et al. (025), SkyReels-A2 (Fei al. 2025), MAGREF (Deng et al.,
2025)) and commercial products (Kling-1.6 (Kwai 2024).
Vi0u-2.0 (Bao et al.
2024), Pika LLab
2024). Hailuo (Team 2024)).
4.2 QUANTITATIVE RESULTS
We conduct comprehensive comparison on the OpenS2V-Eval benchmark (Yuan al. 2025). as shown in Table providing a broad and rigorous evaluation across diverse scenarios. Following the benchmark's 's protocol, each method generates 180 videos for evaluation to ensure statistical relia- bility and coverage of all categories. We report eight automatic metrics as described in Section 4.] to ensure comprehensive assessment, thereby capturing visual quality, temporal behavior, and se- mantic alignment in unified manner. As shown in Table IT, our BindWeave achieves new state of the art on the overall Total Score, with notably stronger NexusScore that highlights its advantage on subject consistency. Notably, NexusScore (Yuan et al., 2025) is designed to address the limita- tions of prior global-frame CLIP (Radford et al. (222a)) or DINO (Oquab et al.) 2023) comparisons and provide semantically grounded, noise-resilient assessment that better reflects perceptual iden- tity fidelity. It achieves this via a detect-then then-commane strategy that first localizes the true target, crops the relevant regions suppress background interference, and then computes similarity within a retrieval- based multimodal feature space with confidence and text-image gating, finally aggregat- ing scores over the verified crops for reliable summary. Importantly, BindWeave also maintains strong competitiveness on other metrics, including FaceSim, Aesthetics, GmeScore, motion-related measures such as MotionSmoothness and Amplitu,,, and NaturalScore, which respectively reflect its strengths in identity preservation, visual appeal, text-video alignment, temporal coherence and motion magnitude, and overall naturalness across a wide range of prompts and categories.
4.3 QUALITATIVE RESULTS
To clearly demonstrate the effectiveness of our method, we present some typical subject-to-video scenarios in Figure 4 and Figure 5 including soogle-body-to-iiie object-to-video, and multi-entit--to-videe
caaaun--ntttttt---iiieee single- As shown in the left panel of Figure 4 commercial models such as Vidu, Pika, Kling, and Hailuo produce visually appealing videos but struggle with subject consistency. Among open-source methods, SkyReel-A2 is comparatively competitive on
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Table 1: Quantitative comparison among different methods for subject-to-video task. Total score is the normalized weighted sum of other scores. "1" higher is better.
Method
Total Scure"
Aesthelies?
MotionSmoothness
Modion.Amplitude! FaceSim
Gimeccure NevusScore) NaturalScure!
VACF-14R Fanne et
2025
57.55%
47.21%
94.97%
15.02%
55,09%
67: 2775
44.0%
67.04%
Phantom- hifiar gol
$6.77%
1h.39%
96.31%
33.42%
51.16%
70.65%
57.43%
69.35%
Kingl L1226.222222033 KWWi
2004
56.25%
44,59%
86.52%
41.60%
40.10%
66. 3173
45.89%
74.59%
Phantom-I 36 te
54,89%
46.67%
93, 3..30%
14 19%
48. 48.56%
69.45%
42.48%
62 50%
MAGREE
52.51%
45.02%
93.17%
21.81%
30.33%
70.7%%
43.01%
SkyRocll
39.41%
87.92%
60%%
45.05%%
43.75%
Vidid 0x20250003)
41.8%
13.52%
Plka.. (20750501))
87 069
7111
19%8
VACB-1.30) 1.30 Jang drai
09.89%
48.24%
97.20%
18.83%
20.57%
10%
57.91%
65.46%
VACE-PI Baraang d M0S
48.984
47.345
96 8055
12.03%
16.599
71.38%
40. 40.1996
31%6
0007
57.61%
45.55%
95.00%
13.91%
53.71%
67 79%
46.847
66.58 85%
subject consistency, yet its overall visual aesthetics lag behind our BindWe VACE and Phantom similarly exhibit weak subject consistency. In the right panel of Figure 4 our approach achieves markedly better subject consistency, text alignment, and visual quality. As shown in the left panel of Figure in single-object-to-vinne pronounced violations of physical and semantic plausibility- sense violations" (e.g., a human walking with severely twisted legs). Kling achieves strong visual aestheties but maintains poor subject consistency. SkyReels-A2 shows severe distortions and simi- larly weak subject consistency, and Phantom also struggles to preserve subject consistency. Among the baselines, VACE better maintains subject consistency but suffers from limited motion coherence and naturalness. In contrast, our BindWeave delivers strong subject consistency together with natu- ral and coherent motion. Notably, under multi-object and the right panel of Figure 5 methods like Vidu and Pika often miss key cues (e.g.. "hot oil"), Kling shows severe physical implausibility (c.g., fries leaking directly out of the basket), and MAGREF fails to preserve subject consistency; other baselines also omit erucial prompt details. In contrast, our results deliver fine-gra reee-grined detail while maintaining strong subject consistency. We attribute this Weav'ss 's explicit cross-modal integration of the reference image and textual prompt via an MILM, which jointly parses entities, attributes, and inter-object relations. As a result, WeadWeave preserves subtle yet crucial details (e.g., "hot oil") and constructs a unified, temporally consistent scene plan to guide coherent generation. This deep cross moddal integration reliably enforces key prompt elements and embeds basic physical commonsense for multi-entity interactions, thereby ducing implausible outcomes. More visualizations can be found in Appendix Section supplementary materials, including additional qualitative examples and comparisons.
scenarios, commercial models such as Vidu and Pika still exhibit
we summarize as "common-
settings as shown in
A.5 and our
Japslng uloeg
10
create porcoful
FAOCHALL BY
morninn cantigge
soft
146
Figure 4: Qualitative comparison subject-to-vider task, with four uniformly sampled frames shown in each case. Compared to other competing methods, our approach is superior in subject fidelity, naturalness, and semantic consistency with the caption.
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oerice exclating a
clveeie thet
suchee aettorg pepta
the
wine Lime
depect duca
ieterial
Inavattel ba ectag fille
gul, 1hat resls Aeet
player BeV.
Figure 5: Qualitative comparison on cubject-to-victe task, with four uniformly sampled frames shown in each case. Compared with other methods, our approach better avoids implausible phe- nomena and produces more natural videos while maintaining strong subject consistency.
Table 2: Quantitative ablation results comparing T5-only and T5+Qwen2.5-VL conditioning.
Method
Total Score" Aesthetiex
MothonSSmothnest MutionAmplitude FaceSimi
GmeSccre? NexusScore, NaturalScore
T5-only
8DR 45.55%
7.48%
53.02%
T5+Qwen2,5-VL
$7.61%
95.90%
13.9%%
53.71%
67.79%
46.84% 66.85%
4.4
ABLATION STUDY
We ablate our control-conditioning that concatenates MLLM- and T5-derived signals to guide a DiT during generation. We compare T5-only baseline with our T5+QWen2.5-V variant. The MLLM- only setup (Qwen2.5-VL.
DiT) is omitted from this quantitative analysis because, as discussed Section A.3 it proved to be unstable during training and failed to converge within our training budget. As shown in Table T5+Qwen2.5-VI. consistently outperforms T5-only across aesthetics, motion, naturalness, and text relevance. Qualitative comparisons in Figure further corroborate these findings: when reference images exhibit scale mismatch, the TS-only baseline tends to pro- duce unrealistic subject sizes (e.g., dog-bowl), and under complex instructions it often misparses action-object relations, whereas T5+Qwen2.5-VL remains well grounded and executes the intended interactions. We attribute these gains to complementary conditioning, the MLLM provides multi- modal, identity- and relation-awan cues that disambiguate subjects and improve temporal coher- ence, while T5 offers precise linguistic grounding that stabilizes optimization. Their concatenation yields richer and more reliable control signal for DIT. More cases provided in Section A.4
paper o1 repurrten, ai peclored
Qwaad-raas Them
Figure 6: Qualitative comparison of MILM+15 vs. T5-only. MILM+T5 shows superior scale grounding, reliable action-object execution, and stronger temporal/textual coherence.
4.5
USER STUDY
We conducted user study to evaluate our method, employing mean opinion scores (MOS) across four key criteria: subject consistency, prompt following, video quality, and motion quality. These dimensions provide comprehensive assessment the generated videos. We recruited 20 partic- ipants perform anonymized comparisons across different methods. All generated samples

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Subjeet Consiitenyy
(Max: 394)
Subject
Consistenc
Prompt
Following
Motion
Quality f
Video
Quality
Total Scoee
Averuge 1
Prompt
Method
(M76:
SkyReels
3.47
3.52
3.60
3.23
3.46
Vidu
3.40
3.55
3.63
3.50
3.52
Magref
3.25
3.65
3.65
3.58
3.53
Phantom
3.56
3.72
3.84
3.58
3.68
Kling
3.62
3.58
3,90
3.75
3.71
Quality
VACE
3.88
3.63
3.80
3.62
3.73
Weave (Ours)
3.94
3.66
3.75
3.70
3.76
ted (Deas Siyyteds Mn
Weper
Ostny
Table 3: User study results comparing different meth- ods. "Total Score" means the average score.
Figure Visualization of user study scores across the evaluation criteria.
anonymized and randomly assigned to users, with each item scored 1-5 scale. The results summarized Table 3 and for clearer presentation we also visualize the data in Figure 7 The evaluation indicates that BindWeave achieves the best performance in subject consistency while maintaining leading results across all metrics.
lifty dent
Figure 8: Evaluating copy-paste artifacts under conflict-coherence scenarios
4.6 COPY-PASTE PROBLEM
We evaluate whether conditioning reference images induces pixel-level "copy-paste" artifacts by constructing conficc-ccohrrence scenarios that deliberately mismatch the reference and the prompted outcome in Figure X Beyond contrasting facial expressions (e.g., smiling reference painful, tearful video; painful reference + joyful smiling video), we also test outfit changes and diverse pose settings to more comprehensively validate our approach. Across these cases, the model follows the instructions rather than pasting pixels; it adapts facial musculature, attire, and pose to match the prompt while preserving identity, maintains smooth temporal transitions without stuck frames, and produces coherent motion instead of static overlays.
5 CONCLUSION
In this paper, we introduce BindWeave, a novel subject-consistent video generation framework that delivers consistent, text-aligned, and visually compelling videos across single- and multi-entity set- tings through explicit cross-modal integration. By using an MLLM to deeply integrate information from reference images and textual prompts to facilitate joint learning, BindWeave effectively models entity identities, attributes, and relations, thereby achieving fine-graines grounding and strong sub- ject preservation. The empirical results demonstrate that BindWeave has fully learned cross-modal fusion knowledge, enabling the generation of high-fidelity, subject-consistent videos. Moreover, the OpenS2V benchmark, BindWeave achieves sfate-of-the- performance, outperforming existing open-source methods and commercial models, clearly showcasing its strength. Overall, BindWeave offers new perspective for the S2V task and points toward future advances in consistency, realism, and controllability.
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ACKNOWLEDGMENTS
This work was partially supported by the National Key R&D Program of China (Grant No. 2024YFB3009902) and Youth Innovation Promotion Association of CAS.
ETHICS STATEMENT
This work studies subject-to-video generation and related evaluation. All images appearing in this paper are either generated by our models or sourced from publicly available datasets under their respective licenses and are used solely to demonstrate the technical capabilities of our research. All qualitative (visualized) results are provided solely for academic comparison and research discussion and are not intended for commercial use.
REPRODUCIBILITY STATEMENT
we provide detailed description of our network architecture and the interactions among the variallss In Section 3.5 we disclose the detailed parameters for training and inference, as well as the datasets used. Section 4.1 we further present our model configurations and the parameters for training and inference, including the benchmarks and metrics used for performance evaluation. Through these efforts, we have made every possible attempt to ensure the reproducibility of our method. Furthermore, we will open-source code and models to facilitate reproducibility.
In Section 3.2
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A APPENDIX
A.1
APPENDIX OVERVIEW
This appendix comprises two sections:
Section A.2 LLM Usage Disclosure. We disclose that we used large language models (LLMs) solely for minor grammar checking after drafting the manuscript, with no contri- bution to research ideation, methods, experiments, or results.
Section A.3 Justification for Model Design. In this section, we discuss our architectural choices and provide clear, empirical justification for our final model design.
Section A.4 Visual comparisons in complex multi-subject scenarios between T5-only and MLLM+T5. We present extended qualitative comparisons under complex multi- subject settings.
Section A.5: Additional Subject-to- Video Quantitative Results, We present extended quantitative results for the subject-to-video setting.
A.2 LLM USAGE DISCLOSURE
We use large language models (LLMs) solely for minor grammar checking after drafting the manuscript. LLMs did not contribute to research ideation, problem formulation, method design, the- oretical development, experiments, implementation, result analysis, or the creation figures/tables. No code, data, experimental results were generated by LLMs. All LLM-suggested edits were manually reviewed and verified by us. We did not provide any LLM with non-pun con-public or sensitive information.
A.3 JUSTIFICATION FOR MODEL DESIGN
During the development phase, we conducted series of pilot experiments to explore various archi- tectural designs for integrating the Multi-modal Large Language Model (MLLM) and the Diffusion Transformer (DiT). These explorations served as an informal ablation study and directly informed our final design choice. Specifically, we investigated several alternatives our proposed MLLM MLP + T5 DiT architecture, including:
MILM + MLP + DiT: Using a simple Multi-Layer Perceptron (MLP) to project MLLM features into the DIT.
2. MLLM + Q-Former + DiT: Employing Q-Former structure to refine the MLLM outputs before feeding them to the DiT.
Our key finding was that architectures relying solely on the MLLM for conditioning (i.e., those without the T5 text encoder) were unstable during training and failed to converge within our available training budget. These models struggled effectively translate the nuanced visual identity information from the MLLM into the precise conditional inputs required by the DiT for high-fidelity video generation. In contrast, the inclusion of '5 provided robust and stable text- conditioning backbone, which, when combined with the identity features from the MILM,, yielded the consistent and high-quality results presented in paper. To provide concrete evidence of the convergence issues, we have included a training loss curve for the MLLM MLP + DiT architec- ture (Figure D. This curve clearly illustrates the training instability, showing that the model's loss oscillates significantly and fails to reach convergence.
A.4 COMPARATIVE RESULTS OF T5-ONLY VERSUS MLLM+T5 (BINDWEAVE) UNDER COMPLEX SCENARIOS
Figure 10 presents extensive challenging cases, including five-image reference setups and complex multi-subject interactions, with side-by-sion comparisons between T5-only and MLLM+T5. Across these reference cases, the T5-only model frequently exhibits distortions and temporal jitter, leading to unstable videos. In contrast, MLLM+T5 reliably reasons about subject placement and overall layout, yielding coherent spatial arrangements, stable motion, and higher visual quality. In
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Loss Chart
Grad norm Chart
400
1,800
2,000
3,008
4,00
1,00
5,000
Figure 9: Training loss for the MILM + MLP + DiT architecture, showing significant oscillation and failure to converge.
proninent object featuring essential. of folied clathing. Bexide the het, there lins fedore carefally placed
whate instant carera, ready to capture nenories on the go. Beneath these iteas, pear of Light blue denie jeans is visable, with pasapert resting on top of the jeans, signstying an tarinert you mey. Rolled-up sweaters and brown febric provide additional packing beneath travel adventure.
prisary objects, offering snapshot
cosy glimpse
thought
In dirly Lit rooe varned by the glow of flickering candle, a delicate hard writes with quilt on parcheent. The quill, acticulously diaped in itk, glices over paper leaving elegant strcess tied riboon aastts stopwich with silver choin atop an book, protruding
the side, hundle
semils
Hearby,
tiseless dedication, capturing the essence of a vriter seserses their craft anidst artiracts of pro found sigii icance.
well-lit plants, rutebouks is individials soeden Luble where on the left side of the table, alongside water cortle and sna soft-sided top. The partit participants are actavely ly writing or discussing, indicating focused and co collaboration enveroneent. group secting ained accoms Lishing acadenic or professional tasks, A speaker sugpesting
silling. laptup rests
scene is lively yet studious, signitying a productive
person sits on preen park bench, adriring the cutdbors, Before then, laptop ueblaazoed
icanic Apple logs rests slightly open currently using it. Surrounding ther trancuil cityscape, quickly shifts to macpaper. They [lpp through the
though they're quatnt houses
jumping fron artacle to
article, occastonally pausing to bring the paper closer for a better read. utility pote stards in the background. The setting sun creates a fale in the lets, casting serens glav over the tranquil scene.
T5-enly
Figure 10: Qualitative comparison of T5-only vs. MLLM+T5.
essence, T5 provides dense captions that help structure prompt interpretation and offer stable de- scriptive prior. However, relying on T5 alone is insufficient for compositional spatial/temporal rea- soning and multi-subject role disambiguation: T5-only often misses cross-entity relations, produces inconsistent event ordering, and may conflate subject roles leading to layout instability and drift in videos. The MLLM supplies compositional reasoning and cross-entity constraint satisfaction. With MLLM+T5, the model correctly resolves ordered cross-entity relations and better maintains relative positions and action dependencies.
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A.5 MORE SUBJECT-TO- VIDVO QUANTITATIVE RESULTS
We present a set of comparative cases, shown in Figure 11 in which the prompt specifies "a man" whereas the reference image depicts a baby. Under this conflict, many methods (Vidu 2.0 al. (2024), Pika 2.1 Lab 2024). Phantom Liu et al. (2025), VACE Jiang al. (0225), ).) generate man as the subject rather than baby. Other methods, such as Kling 1.6 Kwai (2024), Hailuo and SkyReel-A2, retain some infant characteristics but still exhibit poor overall consistency with
reference image. In contrast, our method is not perturbed by the prompt and faithfully preserves the baby's 's appearance from the reference image.
Reference
vidu2.0
Plko2.1
Prompt
Kling
The video features man with dark-haired hair, wearing blue tank top and holding pink tank top on
Hailvo
hanger. appears be in clothing store or a similar retail environment, there are racks of clothes visible in the background. The man is speaking the camera, passibly providing review or discussing the tank top he is holding, he
SkyRoel-
148
Phontom
1,38
Phanton
14B
has colorful bracelets wrist and is wearing a necklace with multiple beads. his expression suggests he is engaged in a conversation or presentation. The setting seems to be
VACE
1.38
VACE
14B
indoors, with artificial lighting illuminatimn the scene.
Ours
Figure 11: Comparisons under a prompt-reference ambiguity (prompt: "a man"; reference: baby). Most baselines follow the prompt and generate adult male, ignoring the features of the reference image or retaining only partial infant traits, whereas our method faithfully preserves the reference subject's appearance.
shown in Figure 12 under relatively simple prompt, most baseline methods exhibit pronounced copy-paste issues: the subject remains static across frames. In particular, Phantom-1.30 Liu et al. (2025) and VACE-14B Jiang et al. (2025) essentially copy-paste the reference cat directly into the video. In contrast, yielding natural and temporally coherent motion.
method avoids the copy-paste issue while preserving subject consistency,
As shown in Figure 13 given only single face reference image. BindWeave generates high-fidelity, subject-consisten videos. It preserves fine-grained identity cues, including facial structure. skin tone, hairstyle, while handling changes in pose, expression, and moderate viewpoint or illumination.
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Vidu2.0
Pike21
Reference
Magret
SkyReel-AZ
14B
Phantom
1.38
Phantom
14B
Prompt
a man sitting
VACE
P1.38
in the
office, a cat sitting beside him.
VACE 1.38
VACE 14B
Ours
Figure 12: Comparisons in the subject-to- tiiieooo setting illustrating the copy-paste issue under simple prompts. Many baselines directly copy the reference image into the video, causing the subject to remain static across frames, whereas our method preserves the subject's temporal dynamics and natural motion.
The results avoid copy-paste artifacts and identity drift, delivering smooth, temporally coherent motion and maintaining alignment with the text prompt without overriding the reference appearance.
As shown in Figure 14 when provided with multiple reference subjects, BindWeave maintains con- sistent identity for each subject across frames and preserves fine mne-grained appearance details as well as their relative spatial layout. The method produces natural, coordinated interactions between sub- jects, keeps compositions visually pleasing and realistic, and avoids identity swapping or blending.
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Reference
Generated Frames
Figure 13: More generated results of WeadWeave, demonstrating high fidelity and strong subject consistency.
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Reference
Generated Fromes
Figure 14: More generated results BindWeave, demonstrating high fidelity and strong subject consistency across multi-references.
20


Reviews:
[{"review_id": "tWveFuOzbP", "reviewer": "Reviewer_rb5c", "summary": "the paper proposes a MLLM-conditioned video diffusion framework for multi-subject referenced video generation", "questions": "please refer to weaknesses section", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- the paper proposes a simple yet effective framework for consistent multi-subject video generation\n- the overall framework design and related data flow are reasonable and clearly explained\n- the visual comparisons demonstrate the effectiveness of the proposed model", "weaknesses": "- while the paper adopts a existing evaluation benchmark, the reliability of the used evaluation metrics is still not well justified. especially the rationale of \"total score\" is questionable considering it is a normalized weighted sum of other scores: does it really reflect the actual performance? can those different so-called metrics be linearly combined in this way? a user study is highly recommended to validate the effectiveness of the proposed method considering the human evaluation is still the most reliable evaluation measure for video generation tasks\n- the paper mentioned that the proposed framework accepts a flexible number of reference images typically ranging from 1 to 4. however, it is unclear what is the max possible number of subjects the proposed method can handle: is it determined by the training? or does it can generalize to an much larger number than 4 during inference? more analysis is needed to validate the scalability of the proposed framework\n- it seems that there are only limited motion in the presented visual results. in that case, it is unclear whether the proposed framework can handle the case where the generated subjects are in largely different scales from the reference images or changing scales. perhaps some testings with zoom-in and zoom-out subjects can be conducted to validate the robustness of the proposed method\n- what is the success rate of generation for a set of given subjects? what are the typical failure cases?\n- minor: it might be better to provide actual videos as supplementary to present the results rather than only keyframes", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6GJAlDV6sm", "reviewer": "Reviewer_DPYe", "summary": "This paper introduces BindWeave, a video generation model based on multiple reference subjects. The model uses an MLLM+DiT architecture, where the MLLM is responsible for understanding the reference subjects and the text prompt, generating semantic embeddings that are input into the DiT model. The DiT model acts as the renderer to generate the video. Experiments show that the proposed method can achieve subject-consistent video generation and understand the logical relationships between the reference subjects and the prompt.", "questions": "After fine-tuning the base model with the proposed method, is there any observed degradation in video generation quality? For example, how does the visual quality and dynamic range of the videos generated by BindWeave compare to those generated by the vanilla base model? I believe it is necessary to include this comparison.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "+ The paper is well-motivated to improve complex spatial relations, temporal logic, and multi-subject interactions in subject-to-video generation.\n+ The idea of using an MLLM for multimodal reasoning to better understand the reference images and the text prompt is reasonable.\n+ The proposed method outperforms open-source and commercial baselines on the OpenS2V benchmark across subject consistency, naturalness, and text relevance.", "weaknesses": "+ The experiments are insufficient, lacking ablation studies on the model design. For example, a comparison with other multi-reference conditioning strategies, whether joint training with MLLM would lead to better results, and if there are other ways to combine MLLM and DiT. The authors should include more experiments to demonstrate the superiority of the model design and training strategies.\n\n+ More cases with multiple reference images are needed rather than single-reference generation, since the MLLM mainly enhances the ability to understand the logical and positional relationships between different subjects.\n\n+ The model design is similar to existing work, and the novelty is limited. Other potential model design solutions should be explored and accompanied by more in-depth analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NQAjkSCGKh", "reviewer": "Reviewer_TvUz", "summary": "This paper targets subject consistent text guided video generation. The key idea is to replace the common separate then fuse conditioning paradigm with cross modal integretion using a MLLM that parses the interleaved prompt with reference images sequence into subject-aware hidden states. These are projected and used alongside T5 text embeddings, CLIP identity features, and VAE low-level features to collectively condition a DiT-based video generator. They showed the competitive results on OpenS2V.", "questions": "see the major weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Deep cross modal binding, the MLLM parses text and image jointly, producing subject aware hidden states that encode roles, attributes, interactions, addressing identity confusion and instructed shallow fusion. \n2. Strong ablations and qualitative evidence. T5 only and T5 with MLLM quantitative and qualitative eval shows better scale grounding, action object execution, and temporal coherence.", "weaknesses": "major: \n1. Using image condition to Qwen2.5 / VAE / CLIP looks redundant, Qwen2.5 and VAE makes sense and also shown a part in your ablation, but why we need a additional CLIP image encoder to encode the condition images.\n2. The approach heavily depends on a relatively heavy Qwen2.5VL. The paper doesn’t quantify the extra latency and VRAM and how it scales with K references or video length, more detail would be appreciated. \n3. involving the MLLM will bring benefits and also the original issue inherit from the MLLM, the planning itself may fail due to lots of reason like hallucination, how these case will affect the subsequence video generation?\n\nminor: \n1. line 102, opens2v --> OpenS2V\n2. line 351/359 BinWeave  --> BindWeave, the method name is not consistent in this paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "y1vVSS3XV6", "reviewer": "Reviewer_NFCf", "summary": "The paper introduces BindWeave, a subject-consistent video generation framework that leverages deep cross-modal integration between text and image inputs. The authors claim that the existing models typically fail to handle complex multi-subject interactions. To overcome this, BindWeave integrates a MLLM with a DiT. Specifically, the MLLM is used to encode spatio-temporal relationships into subject-aware hidden states, which then condition the diffusion process for video synthesis. The experiments are conducted on the OpenS2V benchmark and show BindWeave outperforming state-of-the-art open-source and commercial baselines.", "questions": "- Could the author provide more samples that require complex spatial-temporal reasoning to verify the effectiveness of the MLLM model?\n- Could the author provide more samples with conflicting conditions which can verify whether BlindWeave suffers from copy-paste artifacts?\n- Could the author provide more detailed ablation study to verify the effectiveness of each module in the framework?\n- The model performance looks inconsistent in Figures 7 and 8. Could the authors explain this phenomena? Does the model include a mechanism supporting the adjustment of the trade-off between \"fidelity\" and \"pose variation\"?\n- [Minor] The citation format throughout the paper is wrong. For example, should be \"Phantom (Liu et al., 2025)\" instead of \"Phantom Liu et al. (2025)\". You can modify it by changing \"cite\"/\"citet\" to \"citep\"\n- [Minor] In line 210: should be \"we introduce\" instead of \"we introduces\"", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "- This paper targets at subject-to-video generation which is an interesting and practical problem relevant to lots of real-world application, such as video personalization or content creation.\n- The proposed adaptive multi-reference conditioning strategy sounds well-motivated to me as it allows the model to flexibly integrate multiple reference images without disrupting temporal coherence.\n- The model is trained on the open-sourced OpenS2V-5M dataset, and the paper provides thorough implementation details which could enhance the reproducibility of the proposed framework.\n- The experiments include a comprehensive comparisons with 9 open-sourced and proprietary baseline models. The quantitive results on the OpenS2V-Eval benchmark look promising and show consistent improvements from some existing models.\n- The writing quality is high and easy to understand. The figures are also well-plotted which can assist the readers to quickly understand the proposed framework.", "weaknesses": "- My major concern is the limited technical contribution. The framework jointly using \"frozen MLLM & trainable connector\" + \"VAE tokens\" + \"noisy latents\" to condition the diffusion process is a very common design in recent editing models, such as Step1X-Edit [1] and Qwen-Image [2]. The framework figures (i.e., Figure 2 of BindWeave paper and Figure 4 of Step1X-Edit paper) are nearly the same. It significantly limits the technical contribution/novelty of the paper.\n- While one of the main claims of the paper is the usage of the powerful MLLM for cross-modal reasoning, most of the provided samples (e.g., \"[man] holds a [ball]\", \"[sauces] placed nearby a [wok]\"), however, do not require complex reasoning and can be simply achieved by existing models. While Figure 6 shows the comparison of the samples using T5-only and MLLM+T5, the results do not look reasonable to me as I never saw a model combining [dog] and [bowl] in this way even without MLLM reasoning capability.\n- Following the above concern, the authors should provide samples to demonstrate the effectiveness of MLLM in handling complex multi-subject interactions that require spatial-temporal reasoning. For example, a prompt like \"[basketball court] and four [basketball players]\", where two teammates wear same uniforms and one is attacking while the others are defending. In this sample, the model should correctly place the two teammates on the same side and assign proper actions to each player to achieve spatial-temporal coherence in the output video.\n- Similar to the existing editing frameworks, one major limitation of using VAE tokens as condition is the presence of copy-paste artifacts. In the provided samples, I also noticed heavy pixel-level alignment between the conditional images and the output videos. To further verify whether BlindWeave suffers from this issue, the authors should include samples with conflicting \"image-image\" or \"image-text\" coherence. For example, \"a [garment image] and a [person image] with different poses\" or \"make a [crying boy image] [smile (text)]\"\n- Following the concern above, the model seems to achieve poor trade-off between \"pose variation\" and \"fidelity\". For example, Figure 7 shows high fidelity but low pose variation (i.e., heavy pixel-level alignment), while Figure 8 shows huge pose variation but low fidelity. Based on these results, I don't think BlindWeave can solve these copy-paste artifacts well.\n- The scope of ablation study is very limited. There is only one ablation (T5-only vs. T5+MLLM), while it is also interesting to check the effect of isolating CLIP, VAE, or individual conditional streams.\n- The authors do not provide supplementary material with video samples, which makes the reviewers unable to justify the quality of the output videos. It is impossible to check the temporal smoothness simply based on the provided video frames.\n\n[1] \"Step1X-Edit: A Practical Framework for General Image Editing\"\n\n[2] \"Qwen-Image Technical Report\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)